Groundwater level responses to precipitation variability in Mediterranean insular aquifers

Accepted Manuscript Research papers Groundwater level responses to precipitation variability in mediterranean insular aquifers Jorge Lorenzo-Lacruz, Celso Garcia, Enrique Morán-Tejeda PII: DOI: Reference:

S0022-1694(17)30462-6 http://dx.doi.org/10.1016/j.jhydrol.2017.07.011 HYDROL 22113

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

24 August 2016 29 June 2017 7 July 2017

Please cite this article as: Lorenzo-Lacruz, J., Garcia, C., Morán-Tejeda, E., Groundwater level responses to precipitation variability in mediterranean insular aquifers, Journal of Hydrology (2017), doi: http://dx.doi.org/ 10.1016/j.jhydrol.2017.07.011

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

GROUNDWATER LEVEL RESPONSES TO PRECIPITATION VARIABILITY IN MEDITERRANEAN INSULAR AQUIFERS Jorge Lorenzo-Lacruz*, Celso Garcia, Enrique Morán-Tejeda Department of Geography, University of the Balearic Islands (Spain), Cra. de Valldemossa km 7.5, E07122, Palma (Spain). *corresponding author ([email protected])

ABSTRACT Groundwater is one of the largest and most important sources of fresh water on many regions under Mediterranean climate conditions, which are exposed to large precipitation variability that includes frequent meteorological drought episodes, and present high evapotranspiration rates and water demand during the dry season. The dependence on groundwater increases in those areas with predominant permeable lithologies, contributing to aquifer recharge and the abundance of intermittent streams. The increasing pressure of tourism on water resources in many Mediterranean coastal areas, and uncertainty related to future precipitation and water availability, make it urgent to understand the spatio-temporal response of groundwater bodies to precipitation variability, if sustainable use of the resource is to be achieved. We present an assessment of the response of aquifers to precipitation variability based on correlations between the Standardized Precipitation Index (SPI) at various time scales and the Standardized Groundwater Index (SGI) across a Mediterranean island. We detected three main responses of aquifers to accumulated precipitation anomalies: (i) at short time scales of the SPI (< 6 months); (ii) at medium time scales (6–24 months); and at long time scales (> 24 months). The differing responses were mainly explained by differences in lithology and the percentage of highly permeable rock strata in the aquifer recharge areas. We also identified differences in the months and seasons when aquifer storages are more dependent on precipitation; these were related to climate seasonality and the degree of aquifer exploitation or underground water extraction. The recharge of

some aquifers, especially in mountainous areas, is related to precipitation variability within a limited spatial extent, whereas for aquifers located in the plains, precipitation variability influence much larger areas; the topography and geological structure of the island explain these differences. Results indicate large spatial variability in the response of aquifers to precipitation in a very small area, highlighting the importance of having high spatial resolution hydro-climatic databases available to enable full understanding of the effects of climate variability on scarce water resources.

Keywords: Drought preparedness; Standardized Precipitation Index; Standardized Groundwater Index; water resources; Mallorca; Balearic Islands.

1. INTRODUCTION In recent years drought has become one of the most studied natural hazards because many people (Obasi, 1994) and a large range of natural and socioeconomic systems (Wilhite, 2000; EEA, 2001; Van Loon, 2015) are affected. Droughts are complex and unique phenomena, but are usually only detected when their impacts becomes apparent. This is because, in contrast to other hydro-climatic hazards, droughts cannot be directly measured, and as multidimensional phenomena their complexity cannot be summarized using a single variable (Vicente-Serrano, 2016). Droughts are also a natural hazard that may be exacerbated by climate change (Van-Loon et al., 2016), although there has been little evidence of this over the past 60 years (Sheffield et al., 2012). Various studies based on proxy data have shown the extreme severity of many droughts that occurred during the second half of the 20th century, which was a period that included the most extreme drought episodes in recent centuries, especially in the Mediterranean region (Nicault et al., 2008; Briffa et al., 2009).

Climate models point to a general increase in warming conditions and a pronounced decrease in precipitation by the end of the 21st century in the Mediterranean region, especially during spring and summer (Sumner et al., 2003; Giorgi et al., 2008; Osca et al., 2013). Other studies have predicted greater interannual variability in precipitation (Summner et al., 2003; Alpert et al., 2008) and an increase in the length of dry seasons (Palutikof et al., 2004). In this context, Mediterranean islands are among the areas expected to be particularly affected by these changing conditions (Diffenbaugh et al., 2007). To assess the possible consequences of climate change for the future availability of water resources, it is necessary to understand the relationships between climate variability and the occurrence of hydrological droughts in vulnerable regions, such as the Mediterranean basin (Green et al., 2011). Nevertheless, many uncertainties in estimating the effects of climate change on water resources remain unresolved (Raje et al., 2010; Alkama et al., 2011; Finger et al., 2012). Some of these are related to difficulties in establishing direct relationships between climate variability and hydrological variables (e.g. groundwater levels), which in turn are affected by land use changes (Beguería et al., 2003) and varying water management strategies (LópezMoreno et al., 2007; Lorenzo-Lacruz et al., 2010, 2013). Despite groundwater being of critical importance for freshwater supply in many regions worldwide, in some areas little management attention has been paid to groundwater compared with more obvious surface water supplies, including streamflows and reservoir storages. “Free-for-all” groundwater policies have resulted in water being pumped at far greater rates than can be replenished naturally (Famiglietti, 2014), and in some cases pumping better explains groundwater level variability than does precipitation. In this context, investigating how human responses to precipitation

variability and drought causes large changes in groundwater resources is of crucial importance in managing the effects of future climate variability (Meixner et al., 2016; Russo et al., 2017). Many studies involving various methods have analyzed groundwater droughts, and explored the relationships between rainfall and groundwater levels (e.g. Peters, 2003; Tallaksen et al., 2009; Hughes et al., 2012); some of these have involved the use of satellite data (e.g. Rodell et al., 2009; Castle et al., 2014). However, drought is a multiscalar phenomenon (McKee et al., 1995), with the effects of precipitation deficits (meteorological drought) becoming evident in various systems (e.g. surface and groundwater hydrology, vegetation activity, and crop production) at various temporal scales. These are very important factors in quantifying and monitoring droughts, as the temporal scales over which precipitation deficits accumulate distinguish different drought types; knowledge of these differences facilitates quantification of the delays between meteorological and groundwater droughts. In this context, the Standardized Precipitation Index (SPI; McKee et al., 1993) has been extensively used to monitor the propagation of meteorological droughts in various hydrological systems (Spinoni et al., 2015), including river flows and reservoir storages, and at differing time scales (e.g. Vicente-Serrano et al., 2005; Lorenzo-Lacruz et al., 2010; Hannaford et al., 2011; Lorenzo-Lacruz et al., 2013; Haslinger et al., 2014; Barker et al., 2016). Nevertheless, assessments of groundwater responses to precipitation using the SPI time scales approach have recently been undertaken (see Table 1). In this study we investigated use of the SPI for monitoring groundwater level variability, based on its relationship with data series on water table heights/depths. This is one of few studies (Mendicino et al., 2008; Fiorillo et al., 2010, 2012; Bloomfield et al., 2013, 2015; Kumar et al., 2015) to explore the usefulness of the SPI for assessing the response of groundwater variables to

precipitation variability. We also explored the effects of various aquifer characteristics (including lithology, degree of exploitation, and water management) on the response of aquifers to precipitation variability on a Mediterranean island that has small and limited water resources, and is very vulnerable to climate variability. < Table 1 here please > We undertook the study using 12 wells located in 8 insular Mediterranean groundwater bodies having differing characteristics in terms of precipitation variability, available water for recharge, lithology, land uses, and exploitation regimes. The four objectives of this study were: i) to quantify the response of groundwater levels to temporal precipitation variability; ii) to explore monthly differences in aquifer responses to precipitation; iii) to assess the responses of aquifers to the spatial variability of precipitation; and iv) to explore the influence of various aquifer characteristics on the response of groundwater levels to precipitation variability. These objectives provide the structural sub-headings used in the following Results and Discussion sections.

2. STUDY AREA Mallorca (3640 km2) is the largest of the Balearic Islands, which are located in the western sector of the Mediterranean Sea, off the eastern coast of the Iberian Peninsula (Fig. 1). The island comprises three geomorphological units: the Tramuntana Range, the central depression, and the Llevant Ranges (Giménez et al., 2007). The Tramuntana Range is the largest geomorphic unit, and is located in the northwestern part of the island. It runs parallel to the coast in a SW–NE direction, and is 90 km in length and averages 16 km in width; the highest elevations (1445 m.a.s.l.) on the island occur in this unit, and it plays an important role in determining the distribution of precipitation.

The Llevant Ranges, in the eastern part of the island, have a similar geological structure to the Tramuntana Range, but have a smoother topography and reach elevations of 500 m.a.s.l. The central depression is composed of structural basins having graben or semigraben forms, and elevations rarely exceed 300 m.a.s.l. The location of the island in a convergence zone between air masses from the Rhône and Ebro valleys, and the general nature of the relief, produce its characteristic climatic conditions, which include a warm temperate climate typical of the western Mediterranean, and hot summers and mild winters. In contrast, the spatial and temporal patterns of precipitation are more complex. The topography produces a large pluviometric gradient, with the mean annual precipitation ranging from more than 1200 mm in the northern sector of the Tramuntana Range to less than 300 mm in the southeastern part of the island (see Fig. 1). Rainfall episodes are uncommon but intense; approximately 70% of the annual precipitation occurs in less than 20 days (Jansà, 2014). The contrasting spatial distribution of rainfall generates differing occurrences of meteorological droughts within the island, with those occurring in the Tramuntana Range being independent of those in the eastern and southern areas of Mallorca (Lorenzo-Lacruz and Morán-Tejeda, 2016). The island is drained by ephemeral streams that flow briefly following periods of intense rainfall (Garcia et al., 2017). The fluvial network in the headwaters of the Tramuntana Range provides base flow during 6–8 months annually because it is fed by springs; it can be classified as a groundwater-dominated stream system. Far from the headwaters, transmission losses and cessation of discharge from springs predominantly explain water losses and drying of the network. < Figure 1 here please >

The lithology of the island (primarily permeable carbonate rocks) and the Mediterranean climate determine the seasonal stream flow regime from the mountains. Two small reservoirs located in the Tramuntana Range (Gorg Blau and Cúber; Fig. 1) regulate a water volume of 7.4 million m3 (1976–2014). Water resources in Mallorca depend on 65 groundwater bodies (GBs), which represent a potential resource of 396 million m3 and water availability (through pumping) of 142 million m3 (GIB, 2015). The main aquifers of the island are in terrain of Mesozoic and Cenozoic origin. The most important include Jurassic limestones and dolomites in the Tramuntana and Llevant ranges, and Miocene carbonate rocks and Quaternary detritic deposits in the central plains and southeastern coastal areas. These formations have distinct permeabilities that range from high (5–10 m/day) to medium (1 m/day) and low (0.1 to 0.5 m/day), and this heterogeneity creates large differences in the water volume available for aquifer recharge (Barón et al., 1995). Aquifer recharge from precipitation occurs mainly in small rock outcrop areas, and from discharge of the temporal streams flowing from the mountain ranges. The aquifers are generally unconfined, but could become confined through changes to facies or geological structures. Table 2 shows the main characteristics of the aquifers analyzed in this study. The aquifers in the Tramuntana Range are calcareous formations having karstic circulation that generates several springs (Barón et al., 1995). The coastal aquifers, except those in the Tramuntana Range, have high transmissivity and are connected to the sea. As a result of overexploitation and inappropriate management (including illegal wells), these aquifers are affected by seawater intrusion that has deteriorated the water quality (Garcia and Servera 2003). In the central plain, especially in the Inca-Sa Pobla area, the detritic aquifers have a high content of nitrates derived from intensive agriculture (Robledo-Ardila et al., 2004). The Exploitation Index (EI) is

defined as the ratio of the volume of water pumped out of an aquifer to the volume of water recharged by rainfall; EI data for this study were provided by the Balearic Environmental Agency (Table 2). Based on these data, the most altered aquifers in terms of water extraction are Estremera (N2), Llubí (N8), Son Cosmet (N11), and Son Mesquida (N12). < Table 2 here please > The main source of aquifer contamination is diffuse inputs from agriculture (nitrate and phosphorous), with only 11 of the 65 GBs having no association with agricultural activities. Point source pollution from urban, industrial, and agricultural activities (Fe, Al, toluene, nonylphenols) affects 58 GBs (GIB, 2015). The water plan has identified 22 GBs that are overexploited, are subject to seawater intrusion, or have a deficit in the water mass balance (GIB, 2015). The high population density, which is exacerbated by tourism pressure during summer (Hof et al., 2011), contributes to overexploitation of the aquifers and threatens water supply during dry years, leaving many uncertainties about future water availability in Mallorca (Diffenbaugh et al., 2007). The Hydrological Plan of the Balearic Islands (GIB, 2015) quantified water consumption in Mallorca as 196.5 hm3 for 2011. Domestic activities, including tourism, accounted for 52% of the island's total water use. Agriculture (42.4%) was the second largest water use, with irrigation of golf courses (3.9%), industry (0.9%), and livestock (0.8%) being other important water uses. Groundwater was the main source of water, accounting for 79% of the water used. Irrigation using wastewater represented 13%, whereas the surface water stored in the two reservoirs accounted for 3%. In 2012, desalination plants supplied 5% of the total water consumed, although during drought periods it rises to 12%, reducing the contribution from groundwater. Therefore,

desalinated water has been used as a management tool to reduce water abstraction from aquifers for domestic supply.

3. DATABASE AND METHODOLOGY 3.1. Precipitation records: quality control, homogenization, and gap filling processes Analysis of the effects of precipitation variability on groundwater resources in Mallorca was based on use of the SPI computed at various time scales. It was important to use a robust and quality controlled monthly precipitation database that encompassed the high spatio-temporal variability of precipitation in the Balearic Islands (see Fig. 1). We computed monthly totals for 94 daily precipitation series provided by the Spanish Meteorological Agency (AEMET). The lengths and occurrence of data gaps varied among the series, so based on our aim of balancing spatial and temporal coverage we selected a set of 44 series having < 10% of the records missing for the period 1974– 2014 (see Fig. 1). This provided a representative sample comprising 44 precipitation series for Mallorca and 41 years of monthly records with which to perform the analysis. The protocol followed to assess the data quality was the same as that successfully used in several studies focused on this region; further details of the protocol are described by González-Hidalgo et al. (2011). The process was based on the iterative use of reference series, and included detection of anomalous data using ratios, relative homogenization tests (Alexandersson, 1986), and gap filling. Gap filling was achieved by means of linear regression models using the most correlated precipitation series from neighboring stations. 3.2. Groundwater level database

Series of water table data (height/depth) were provided by the Spanish Geological Survey (IGME) and the Balearic Environmental Office (Conselleria de Medi Ambient). Both agencies maintain a very dense monitoring network comprising > 1700 observation wells in Mallorca. Nevertheless, measurements made at any well by the two agencies are independent, with the result that two different databases having different temporal coverage and recording frequency were available for the study. The vast majority of series contained few non-systematic measurements corresponding to sporadic exploratory investigations. From each database we selected the 12 most complete series (see locations in Fig. 1) in terms of temporal coverage. Two series were merged to encompass the entire study period (1974–2014), and contained data overlap for at least 50% of the records over the total period of the study. Prior to merging the series we checked the correlations between the series during the overlap periods, and in all cases it was greater than R = 0.9. In cases of the presence of two or more records corresponding to the same month (derived from one or both agencies), we calculated the mean value for those records. Following merging of the series we tested for homogeneity using the Student’s t-test and the Worsley likelihood ratio test for the detection of abrupt changes in hydrological series, following the recommendations of Kundzewicz (2004), and Kundzewicz and Robson (2004). Both tests highlighted one inhomogeneity in the series for the Estremera aquifer (see Fig. 2). However, we did not correct this inhomogeneity as it was related to water management activities: the Estremera aquifer supplies water to the city of Palma (430,000 inhabitants in 2016), and the extraction of water is systematic. In other cases, such as the period following the 1999–2001 dry episode (Lorenzo-Lacruz and Morán-Tejeda, 2016), water from the spring of Sa Costera was pumped into the aquifer to avoid it becoming depletion (6 hm3 was transferred in 2002).

3.3. Standardized Precipitation Index The SPI is one of the most widely used indices worldwide for meteorological drought monitoring and impact assessment, and the World Meteorological Organization considers it to be a universal reference drought index (Hayes et al., 2011). The SPI has been proven to accurately quantify drought severity at various time scales. It provides a powerful tool for the analysis and monitoring of meteorological droughts (Lana et al., 2001; Bonaccorso et al., 2003; Patel et al., 2007; Di Lena et al., 2014), and for the assessment of drought effects on various systems, including river flows and reservoir storages (Vicente-Serrano et al., 2005; Lorenzo-Lacruz et al., 2010; Hannaford et al., 2011; Lorenzo-Lacruz et al., 2013; Barker et al., 2016), vegetation activity (Pasho et al., 2011; Vicente-Serrano et al., 2012a; Morán-Tejeda et al., 2013), soil moisture (Sims et al., 2002; Hirschi et al., 2011), and spring discharges and groundwater levels (Fiorillo et al., 2010, 2012; Bloomfield et al., 2013, 2015; Kumar et al., 2015). The SPI transforms precipitation series into series of anomalies expressed as z-scores (with a mean equal to 0 and a standard deviation equal to 1), and accumulates the precipitation deficits for different temporal durations. In this study we used the algorithm developed by Vicente-Serrano (2006) to calculate the SPI at various time scales, basing the calculation on the fit of the precipitation series to the Pearson III distribution (see also Guttman, 1990; Quiring, 2009). To assess drought effects on the evolution of groundwater storages, we calculated the SPI from the precipitation series for the 44 Majorcan weather stations from January 1974 to December 2014 at time scales from 1 to 48 months, and correlated this with the standardized series of water table data (height/depth).

3.4. Standardized Groundwater Index

To obtain a groundwater index that enabled correlation with the SPI, and comparison of series in time and space, we transformed the monthly series of water table data (height/depth) into non-dimensional series of standardized anomalies, regardless of the water heights in the series and the recharge–discharge regimes involved. To do this we followed the procedure for calculation of the Standardized Streamflow Index (SSI; Vicente-Serrano et al., 2012b). The series of water table data (height/depth) were fitted to the most suitable probability distribution, according to the minimum orthogonal distance between the sample L-moments at site i and the L-moment relationship for a specific distribution. The most suitable probability distribution was selected from among the general extreme value, the Pearson Type III, the log-logistic, the log-normal, the generalized Pareto, and the Weibull distributions (see Fig. 2). This versatile approach for the calculation of a groundwater index ensured the adaptability of the new standardized variable to different climates and water regimes. It also provided for a more accurate calculation of the index when based on highly biased distributions, which is very common for hydrological variables including groundwater storages. This calculation procedure has been successfully used in various spatial contexts involving a range of hydrological variables (Telesca et al., 2012; Lorenzo-Lacruz et al., 2013; Barker et al., 2016; Huang et al., 2016). Thus, we calculated 12 Standardized Groundwater Index (SGI) series from 1974 to 2014; these were temporally and spatially comparable, and not affected by the magnitude of the variable (piezometric level) or the aquifer recharge–discharge regime. < Figure 2 here please >

4. RESULTS 4.1. Response of groundwater levels to temporal precipitation variability

Figure 3 shows the Pearson R correlation coefficients between the SGI series for each aquifer and the SPI series derived from the nearest weather station, calculated at time scales from 1 to 48 months. In general, climate variability explained a high proportion of the variability in groundwater levels. Correlations were positive and significant in 9 of the 12 series. The maximum correlation recorded at any time scale for these 9 series was greater than R = 0.6; the exceptions were Can Bajoca (N5), Can Guillemet (N10), and Son Cosmet (N11), which had maximum correlations between R = 0.2 and R = 0.4. However, different response patterns were observed in terms of the effects of accumulated precipitation deficits on aquifer storage. Some aquifers responded at short time scales, with maximum correlations occurring for periods < 6 months. These included Can Bajoca (N5; R = 0.42 at 6 months), Can Guillemet (N10; R = 0.25 at 2 months), and Son Cosmet (N11; R = 0.35 at 2 months); these also showed the lowest correlations among all the SGI series considered. This weaker relationship between climate variability and evolution of the aquifer levels is discussed in section 4.4, where the effect of aquifer characteristics on the groundwater response and its sensitivity to climate variability is considered. In other cases the highest correlations were found at medium time scales (6–24 months). Amongst this group were Almadrava (N1; R = 0.77 at 21 months), Massanella blue (N3; R = 0.66 at 16 months), Massanella red (N4; R = 0.72 at 9 months), Sa Pobla S-3 (N6; R = 0.7 at 13 months), Sa Pobla S-12 (N7; R = 0.7 at 7 months), and Sa Pobla S-14 (N8; R = 0.73 at 10 months). These series also showed a similar response pattern, which was characterized by very low correlations at short time scales and a steep positive slope for the correlation curve, which reached the maximum correlation at medium time scales. After this point, all the correlation curves maintained a pattern of decrease until the 48-month time scale. In terms of correlation between the SPI and the SGI, the last group of aquifers comprised those showing the

greatest correlations at long time scales (> 24 months), and included Estremera (N2; R = 0.75 at 46 months), Llubí (N9; R = 0.77 at 35 months), and Son Mesquida (N12; R = 0.57 at 41 months). The response pattern in these cases involved a correlation curve having an increasing slope from the shortest to the longest time scales, where the greatest correlations were observed. Analysis of this group revealed three temporal response (reduced stored water) times among the aquifers to accumulated precipitation deficits, with aquifers responding at short, medium, and long SPI time scales. < Figure 3 here please > 4.2. Monthly differences in aquifer responses to precipitation variability Following assessment of the general response of aquifer storages to climate variability we explored seasonal differences in the relationship between meteorological and groundwater variables. As expected, the relationship between the SPI and the SGI changed markedly on a monthly basis. Figure 4 shows the Pearson R correlation coefficients between the monthly SGI series and the monthly SPI values at time scales from 1 to 48 months. Four response patterns were observed. The first, which included the Can Bajoca (N5), Can Guillemet (N10), Son Cosmet (N11), and Son Mesquida (N12) wells, showed higher correlations between December and February (mainly at short SPI time scales), immediately following the first characteristic humid period of the year in Mediterranean climates. The second pattern encompassed the three series for the Inca-Sa Pobla aquifer (N6, N7, and N8), and showed a very strong relationship (R > 0.8) between climate and groundwater variables at medium time scales during the second humid period of the year (February to May), which is characterized by moderate precipitation rates. The third group included Almadrava (N1), Massanella blue and red (N3 and N4, respectively), and Llubí (N9). These showed a more complex monthly response pattern including a band of high correlations (R > 0.75) in October, and a

secondary peak of correlations during June and July. The correlations in October may have been related to the climate signal, whereas the correlations during the dry season (June and July) may have been a consequence of water management strategies and aquifer exploitation. The very low rainfall during the dry season, the high level of evapotranspiration demand by vegetation and crops, and the continuous pumping of aquifers for irrigation during summer make aquifer recharge unlikely during these months. The Estremera aquifer (N2) showed a distinctive pattern, with fewer monthly differences than were found for the other aquifers. Correlations greater than R = 0.7 were observed in most months, which was not surprising as water is constantly transferred from the Estremera aquifer to the water supply network of the city of Palma (6.1 hm3/year between 2000 and 2014). < Figure 4 here please >

4.3. Response of aquifers to the spatial variability of precipitation Figures 5 and 6 show the spatial distribution of correlations between the SGI series for each well (N1–N6 and N7–N12, respectively) and the SPI series computed at representative time scales (3, 6, 12, 24, and 48 months) for all precipitation observatories. We computed correlations between the SGI and SPI series for the 44 Majorcan weather stations and interpolated these correlations using ordinary kriging. This analysis revealed two spatial patterns of the influence of meteorological droughts on groundwater levels. We found that the recharge of some aquifers depended on meteorological drought conditions within very limited spatial extents (e.g. Estremera N2; Fig. 5), whereas the recharge processes for other aquifers were associated with precipitation over broader areas. In general, those wells in the Tramuntana Range (N2, N3, N4, and N5) and Son Cosmet (N11) showed high and moderate correlations with

the SPI within specific limited areas. For these aquifers the maximum correlation at any SPI scale where observed in areas of limited extent. The Estremera aquifer (N2) is emblematic of this spatially limited recharge pattern. High correlations (R > 0.7) between the Estremera SGI series and the SPI series were only observed in a small area to the northwest of the well, on the northern side of the summit line of the Tramuntana Range. The Estremera aquifer also showed moderate correlations with the SPI at medium and long time scales in the northern sector of the Tramuntana Range, whereas negative correlations with the SPI series occurred in the eastern part of the island. For the other wells in the Tramuntana Range the highest correlations with the SPI at limited spatial extents were only observed at specific time scales (N3 at 48 months; N4 at 6 months; N5 at 6 months). Another example of correlations with the SPI in a restricted area was that for Son Cosmet (N11; Fig. 6), where moderate and low positive correlations were observed only at the shortest time scales in the southernmost area of Mallorca. < Figure 5 here please > < Figure 6 here please > The second pattern of spatial distribution of correlations between the SPI and groundwater levels was characterized by high correlations with accumulated precipitation deficits affecting wide areas; these included Almadrava (N1), the three wells of the Inca-Sa Pobla aquifer (N6, N7, and N8), Llubí (N9), Can Guillemet (N10), and Son Mesquida (N12). For Almadrava and the three Inca-Sa Pobla SGI series there was a gradient in the distribution of correlations involving a close relationship with the SPI at medium time scales in the north of the island, and low correlations for the southern part of Mallorca. The Llubí aquifer (N9) also showed this spatial gradient (high correlations with the SPI in the north of the island, and low correlations in the

south), although the highest correlations were recorded at long time scales. The SGI series for Can Guillemet (N10) and Son Mesquida (N12) showed SE–NW and E–W spatial gradients, respectively, in the distribution of correlations with the SPI series. However, the correlations were higher in the case of Son Mesquida, especially for long SPI time scales. These results highlight the great complexity and spatial variability of the effects of meteorological droughts on groundwater storage, even in areas of very limited spatial extent, such as the island of Mallorca.

4.4. Influence of aquifer characteristics on the response of groundwater levels to precipitation variability

It was noted above that differences in the response of aquifer levels to precipitation variability were found, based on the time scale at which the best correlations between the SGI and SPI occurred. To explore the causes of these differences we grouped the aquifers showing the highest correlations at the various time scales according to the strata permeability and the level of human exploitation, and represented the grouped correlations using boxplots (Fig. 7). We hypothesize that aquifer size or volume may also play a role in the time-scale response to droughts, but there was no measure for this parameter in our study area. Figure 7a shows the aquifers grouped according to the percentage of highly permeable lithological strata in the recharge area. For those aquifers having recharge areas dominated by highly permeable strata (mainly dolomites and limestones), the highest correlations were found at medium and long SPI time scales (median: 29 months), highlighting a pattern of sustained response of groundwater drought to accumulated precipitation deficits. In contrast, for aquifers where the recharge area had low permeability strata the strongest response of water levels to accumulated precipitation deficits occurred at short SPI time scales (median: 8 months). The boxplots in Figure 7b show the highest correlation observed for each aquifer for

any SPI time scale, classified according to the percentage of clay in the corresponding aquifer recharge area. The aquifer recharge areas in which clays dominated showed a weaker relationship between meteorological and groundwater droughts. The highest Pearson correlation coefficients between the SGI and SPI series in this type of aquifer ranged from R > 0.3 to R < 0.5. This contrasted with those aquifers where no clay was present in the recharge areas, where higher maximum correlations (R > 0.7) occurred in most cases. These results suggest that the presence of clay in the aquifer recharge area affected the relationship between precipitation and aquifer storage, and makes these aquifers less sensitive to climatic variability. < Figure 7 here please > Figure 7 (bottom row) summarizes the monthly correlations between the SGI and SPI series during the dry season (June: Fig. 7c; July: Fig. 7d; August: Fig. 7e), computed at time scales from 1 to 48 months and classified according to the aquifer exploitation level (EI) (Table 2). For aquifers having a high level of exploitation (EI > 0.5) the relationship between groundwater and precipitation during summer was much stronger than for those aquifers having lower exploitation rates (EI < 0.5). Correlations between the SGI and SPI series were high or very high during June and July for aquifers having high exploitation rates (EI > 0.5; median at R = 0.68 in June, and R = 0.73 in July). For less exploited aquifers (EI < 0.5) the magnitude of the correlations between the SGI and SPI series was more variable, but in most cases was lower than the correlations found for aquifers having higher exploitation rates (especially during July). These results indicate that dependence on climatic conditions during the dry season is exacerbated for those aquifers subject to greater exploitation.

One of the major water-consuming activities in Mallorca is tourism, and the seasonal pattern of tourist arrivals may have (together with climate) a marked effect in reducing groundwater storages, especially during summer. Table 3 shows the correlations between monthly tourist arrivals and groundwater levels in each aquifer for each month and the 3 subsequent months. Negative and significant correlations where observed for every well with respect to any monthly lag. The strongest correlations were recorded for lag months, except for N10, N11, and N12, where the groundwater levels were highly correlated with tourist arrivals in the same month. This short inertia in the groundwater response to precipitation and demand variability can be explained by aquifer size. The strongest correlations between tourist arrivals and groundwater levels were predominantly observed in aquifers that also showed high correlations between the SPI and the SGI during summer (N1, N4, N8, N12; see Fig. 4), highlighting the effect of tourism consumption on the aquifer response to precipitation. < Table 3 here please > 5. DISCUSSION

In this study we assessed the response of groundwater storages to precipitation variability on the island of Mallorca, using a multiple time-scale approach based on the SPI. We undertook this research in Mallorca because it has characteristics that make it a particularly suitable area for the analysis of climate effects on groundwater resources (Candela et al., 2009). The island is a climatic ecotone area located in the transitional subtropical zone and has Mediterranean climate conditions, and the effects of climate change are expected to be more intense on Mallorca than in other areas (Lavorel et al., 1998; Sumner et al., 2003; Diffenbaugh et al., 2007; Homar et al., 2010). Mallorca is also highly prone to meteorological droughts (Lorenzo-Lacruz and Morán-Tejeda,

2016), with trends of decreasing precipitation having been detected, especially during spring (Homar et al., 2010). A recent study showed a shift in the seasonal climatic regime has occurred on the island, with summer climatic conditions extending into spring (Jansà et al., 2017). Other unique characteristics of Mallorca include highly variable precipitation within a very limited spatial extent, contrasting relief, a limited area for runoff generation, a hydrological network composed of temporal streams, and a very high population density, especially during summer, when tourism pressure is substantial (Garcia and Servera, 2003; Hof et al., 2011). For these reasons, groundwater storage is crucial for the maintenance of water supplies on Mallorca, and the island is very vulnerable to the occurrence of droughts. Under these circumstances, maintenance of future water supply is at risk and associated with many unresolved uncertainties. Consequently, assessment of the effects of meteorological droughts and their propagation through groundwater storages is of great importance (Green et al., 2011). This is one of the first studies to have explored the relationship between meteorological variability and groundwater storages, by relating the SPI to standardized series of water table data (height/depth) in the Mediterranean region (see Table 1). The statistical properties of hydrological series, including aquifer piezometric levels and spring discharges, are complex (Mendicino et al., 2008; Shukla et al., 2008; LorenzoLacruz et al., 2010; Telesca et al., 2012; Vicente-Serrano et al., 2012b; Bloomfield et al., 2013). To obtain a robust groundwater index that enabled comparison with the SPI computed at various time scales, it was necessary to use the most appropriate statistical distribution for standardization of the monthly series of water table data (height/depth). We followed the procedure of Vicente-Serrano et al. (2012b) for calculating the SSI, as it is a versatile approach that takes into account 6 probability distributions, which is two more than were considered by Bloomfield et al. (2013). In this way we calculated an

accurate groundwater drought index that was adaptable to the heterogeneous statistical properties of series of piezometric levels (see Fig. 2). These are usually highly biased, especially in areas having high precipitation variability and complexity in the occurrence of meteorological droughts, including Mallorca (Lorenzo-Lacruz and Morán-Tejeda, 2016). This study represents state of the art use of an up-to-date high resolution meteorological and groundwater database for the analysis. This enabled us to investigate differing responses of aquifers to meteorological variability of very limited spatial extent (44 monthly precipitation and 12 water table data series in an area of 3640 km2). Continuous groundwater level series for Spain are rare, which partly explains the absence of studies addressing this subject, despite the region being highly prone to the effects of droughts on various hydrological, agricultural, and groundwater systems. The enormous dependence of Mallorca on groundwater resources, because of its permeable lithological structures and a hydrological network of temporary streams, has in recent decades resulted in systematic sampling and monitoring of aquifer water tables (height/depth) by the Spanish Geological Survey and the Environmental Office of the Balearic Government. The resulting data enabled us to create a representative groundwater database involving series from 12 observation wells, derived by merging the information provided by each of the two agencies. This ensured that the high spatial variability of meteorological droughts in Mallorca (Lorenzo-Lacruz and Morán-Tejeda, 2016) and its effects on groundwater resources were accurately represented, reducing uncertainties related to the inherent spatial complexity of droughts (Vicente-Serrano, 2016). We did not consider the contribution of snow to aquifer recharge in the Tramuntana Range because there are no snow depth/density records for the island, and because snowfalls are very occasional and their contribution to the total amount of precipitation

on the Tramuntana Range is thought to be negligible for the purposes of the present research. 5.1. Response of groundwater levels to temporal precipitation variability The correlation analysis showed three patterns of response of groundwater levels to precipitation, involving aquifers responding at short, medium, and long SPI time scales. These results are consistent with the findings of Bloomfield et al. (2013), who reported high correlations of the Standardized Groundwater Level Index (SGI) with the SPI at short and medium time scales for 14 observation wells across the UK. In the Mediterranean context, Fiorillo et al. (2010, 2012) found strong relationships between spring discharges and the SPI at the 12-month time scale in central and southern Italy, although correlations higher than R = 0.6 were obtained at much longer time scales. The three observed responses of groundwater levels to precipitation variability on Mallorca may be related to water management, and to varying climate and lithological features. We showed that for aquifer recharge areas where high permeability lithological strata predominated, the time scales at which the maximum correlation between the SGI and SPI series were obtained were considerably longer than for aquifers in recharge areas having fewer high permeability lithological strata. In contrast, where clays predominated the relationship between meteorological and groundwater droughts was weaker, and the highest correlations were obtained at short SPI time scales. This suggests the occurrence of strong surface and subsurface runoff processes, which reduce aquifer recharge and favor the transport of water out of the aquifer recharge area (Strack, 1981; Van der Kamp et al., 1998). 5.2. Monthly differences in aquifer responses to precipitation variability

Monthly analysis revealed differing timing in the occurrence of aquifer recharge and discharge processes. The recharge of aquifers in the south of the island (N10, N11, N12) is very dependent on late autumn precipitation, which is influenced by the negative phases of the Western Mediterranean Oscillation and associated southern humid flows (Martín-Vide et al., 2006; López-Bustins, 2007). Recharge of the aquifers in the center of the island (N6, N7, N8) is closely related to late winter and spring precipitation, which is moderately influenced by the North Atlantic Oscillation (López-Bustins, 2007). The baseline level in the other aquifers is mainly affected in summer, and shows temporal inertia and strong relationships to precipitation accumulated over long periods (Peters et al., 2005); this can be explained by water management and irrigation strategies (Khan et al., 2008). This possibility is discussed in section 5.4. 5.3. Responses of aquifers to the spatial variability of precipitation The analysis of the spatial influence of precipitation variability on groundwater storage revealed two main patterns. For aquifers in the Tramuntana Range the influence of meteorological droughts was limited in spatial extent, suggesting important roles for topography and the occurrence and type of lithological strata in aquifer recharge areas (Custodio, 2010). The presence of densely vegetated areas in the Tramuntana Range may also influence the response to precipitation variability in this zone, by reducing the amount of water available for recharge (Zomlot et al., 2015). For the other aquifers, flat and open topography was related to broad recharge areas, and the influence of precipitation was greater in terms of spatial extent. In these areas, especially on the coast, dense urbanization may reduce the amount of water available for aquifer recharge by generating high runoff rates during intense precipitation episodes, which are very common on the island. The differences in the spatial influence of meteorological droughts noted above highlight the topographic barrier effect of the Tramuntana Range,

which generates two contrasting spatial patterns in the occurrence of meteorological droughts within Mallorca (Lorenzo-Lacruz and Morán-Tejeda, 2016). 5.4. Influence of aquifer characteristics on the response of groundwater levels to precipitation variability Climate processes may influence groundwater recharge–discharge processes in very different ways, but any exclusive attribution to climate variability would be wrong. Understanding the role of other characteristics of the aquifer, including water demand, is a key issue in drought analysis (Kløve et al., 2014). In this section we report the influence of various aquifer characteristics (including lithology, aquifer exploitation, and water demand) on the response of the aquifer to drought occurrence. We observed that karst lithologies influenced the recharge of aquifers by favoring a sustained response during long SPI time scales. As these aquifers are more permeable, a faster response to drought conditions is expected. Nevertheless, the presence of karst lithologies may also play an important role in the creation and presence of wider aquifer cavities. Therefore, the size of an aquifer may be a key characteristic in how it responds to drought, with increasing size causing greater inertia in the aquifer response to accumulated precipitation deficits. However, no reliable information on aquifer volumes was available for analysis in this study. Our observations for those aquifers having a higher level of exploitation (N1, N3, N4, N9) were interesting, as a priori these were expected to show little correlation between climate and water levels. Correlations between water levels and precipitation anomalies showed a bi-modal pattern (i.e. an initial peak in October, when the wet season and aquifer recharge began, and a second peak of correlations for the summer months). The first peak reflected a climate effect on the aquifer levels, which are usually very low

during summer because of the low levels of precipitation, while autumn rainfall increased aquifer recharge. In contrast, the second peak of correlations observed in summer may reflect the influence of human factors superimposed on the climatic signal. In summer the aquifer levels decrease because of the low precipitation levels (over long time scales), but water consumption on the island increases enormously because of tourism, and the associated water extraction further decreases the aquifer level (thus, increasing the level of correlation). Therefore, accumulated precipitation deficits over long periods of time become evident and more intense in aquifer storages during summer. Consequently, during summer the aquifers subject to high exploitation rates become very vulnerable, as aquifer water levels are more sensitive to precipitation variability in this season; this period also coincides with a seasonal decrease in precipitation and the period of greatest groundwater extraction, as water demand increases. Therefore, increasing exploitation of the aquifers may increase their dependence on precipitation, especially during the dry season. In the case of the Estremera aquifer, correlations were found at long time scales, but with few differences among months. This can be explained by the relatively large inertia of inflow, in addition to the continuous and systematic extraction of water to supply the city of Palma. The influence of tourism water demand on groundwater levels may be not negligible, as domestic use (including tourism) represents 52% of the total water consumption on the island. The strongest correlations between tourist arrivals and groundwater levels were mainly observed for those aquifers that also showed high correlations between the SPI and the SGI during summer, reinforcing the hypothesis that in some aquifers high correlations during summer are related to the human signal being superimposed on the climate signal. Although the correlation analysis performed may be influenced by

inherent seasonal cycles, significant negative correlations were found between tourist arrivals and groundwater levels. This highlights the substantial impact of water consumption by tourism in modifying the climate signal effect on aquifer recharge– discharge processes. Detailed analysis of this should be the subject of future studies. 6. CONCLUSION

In this study we analyzed the response of aquifer levels to precipitation variability on the island of Mallorca. To achieve this we identified appropriate SPI time scales at which to monitor groundwater storages in 8 groundwater bodies of the island. The main findings of the work are as follows. 1) Aquifers responded in three ways, with variously high correlations evident at short (< 6 months), medium (6–24 months), and long (> 24 months) SPI time scales. 2) Recharge of some aquifers was closely related to meteorological conditions during the main annual precipitation peak, whereas for other aquifers the recharge was related to the secondary precipitation peak. 3) Intensely exploited aquifers are very vulnerable. For these aquifers, very high correlations were found between aquifer storage and the accumulated precipitation deficits during the dry season. 4) The various responses of aquifer levels to precipitation variability were related to heterogeneous climatic, lithological, and management factors, highlighting the need for a detailed and adaptive water policy for each case. The results of this study and future climate change projections for the Mediterranean region (e.g. Palutikof et al., 2004; Diffenbaugh et al., 2007; Osca et al., 2013) suggest that it will be increasingly difficult to satisfy water demands under the current management strategies; these are highly dependent on groundwater resources, and it is likely that new approaches will be needed. Despite increasing pressure on the availability of water resources and their likely future decline, in 2016 the island of

Mallorca reached a new historical record for tourist arrivals. In this context, if successful mitigation measures are to be implemented, especially during dry periods, water management must include predicting the effect of climate change on groundwater availability. Moreover, new rational and cooperative water policies developed among the social, political, and business sectors will be crucial for maintenance of the future water supplies in vulnerable areas, including Mediterranean regions.

ACKNOWLEDGEMENTS We thank the Spanish National Meteorological Agency (AEMET) for providing the meteorological data used in this study. We also thank the Spanish National Geological Survey (IGME) and the Balearic Environmental Office (Consellería de Medi Ambient) for providing the series of water table data (height/depth) used in this study. Marga Comas (Balearic General Water Authority) helped with the data supply, and analyses of water table (height/depth). Antonio Rodríguez Perea helped us with the interpretation of the results. We want to express our gratitude to the five anonymous reviewers, the Editor and the Associated Editor, for their effort and dedication to substantially improve the quality of this article.

REFERENCES Alexandersson, A., 1986. A homogeneity test applied to precipitation data, International Journal of Climatology 6, 661-675. Alkama, R., Decharme, B., Douville, H., Ribes, A., 2011. Trends in Global and BasinScale Runoff over the Late Twentieth Century: Methodological Issues and Sources of Uncertainty, Journal of Climate 24, 3000-3014. Alpert, P., Krichak, S.O., Shafir, H., Haim, D., Osetinsky, I., 2008. Climatic trends to extremes employing regional modeling and statistical interpretation over the E. Mediterranean. Global and Planetary Change 63, 163-170.

Barker, L. J., Hannaford, J., Chiverton, A., Svensson, C., 2016. From meteorological to hydrological drought using standardised indicators, Hydrology and Earth System Sciences 20(6): 2483-2505. Barón, A., González, C., Rodríguez Perea, A. 1995. Hidrología cárstica de MallorcaKarst hydrology of Mallorca. Endins 20, 45-57. Beguería, S., López-Moreno, J.I., Lorente, A., Seeger, M., García-Ruiz, J.M., 2003. Assessing the effect of climate oscillations and land-use changes on streamflow in the central Spanish Pyrenees. Ambio 32, 283-286. Bhuiyan, C., Singh, R. P., & Kogan, F. N. (2006). Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 8(4), 289-302. Bloomfield, J.P., Marchant, B.P., 2013. Analysis of groundwater drought building on the standardized precipitation index approach, Hydrology and Earth System Sciences 17, 4769–4787. Bloomfield, J. P., Marchant, B. P., Bricker, S. H., & Morgan, R. B. (2015). Regional analysis of groundwater droughts using hydrograph classification. Hydrology and Earth System Sciences, 19(10), 4327-4344. Bonaccorso, B., Bordi, I., Cancelliere, A., Rossi, G., Sutera, A., 2003. Spatial Variability of Drought: An Analysis of the SPI in Sicily. Water Resources Management 17, 273-296. Briffa, K.R., van der Schrier, G., Jones, P.D., 2009. Wet and dry summers in Europe since 1750: evidence of increasing drought. International Journal of Climatology 29 (13), 1894-1905. Cai, Z., & Ofterdinger, U. (2016). Analysis of groundwater-level response to rainfall and estimation of annual recharge in fractured hard rock aquifers, NW Ireland. Journal of Hydrology, 535, 71-84. Candela, L., von Igel, W., Elorza, F.J., Aronica, G., 2009. Impact assessment of combined climate and management secenarios on groundwater resources and associated wetland (Majorca, Spain), Journal of Hydrology 376, 510-527. Castle, S.L., Thomas, B.F., Reager, J.T., Rodell, M., Swenson, S.C., Famiglietti, J.S., 2014. Groundwater depletion during drought threatens future water security of the Colorado River Basin, Geophys. Res. Lett., 41, 5904–5911, doi: 10.1002/2014GL061055. Chen, Z., Grasby, S. E., & Osadetz, K. G. (2002). Predicting average annual groundwater levels from climatic variables: an empirical model. Journal of Hydrology, 260(1), 102-117.

Custodio, E., 2010. Estimation of aquifer recharge by means of atmospheric chloride deposition balance in the soil, Contributions to Sciencee 6(1), 81-97. Diffenbaugh, N.S., Pal, J.S., Giorgi, F., Gao, X., 2007. Heat stress intensification in the Mediterranean climate change hotspot. Geophysical Research Letters 34, L11706. Doi: 10.1029/2007GL030000. Di Lena, B., Vergni, L., Antenucci, F., Todisco, F., Mannocchi, F.,2014. Analysis of drought in the region of Abruzzo (Central Italy) by the Standardized Precipitation Index. Theoretical and Applied Climatology 115 (1-2), 41-52. European Environmental Agency, 2001. Sustainable Water Use in Europe, Part 3. Extreme hydrological events: Floods and droughts. Environmental issue report 21. Famiglietti, J. S. (2014). The global groundwater crisis. Nature Climate Change, 4(11), 945-948. Finger, D., G. Heinrich, A. Gobiet, and A. Bauder, 2012. Projections of future water resources and their uncertainty in a glacierized catchment in the Swiss Alps and the subsequent effects on hydropower production during the 21st century, Water Resour. Res., 48, W02521, doi:10.1029/2011WR010733. Fiorillo, F., Guadagno, F.M., 2010. Karst Spring Discharges Analysis in Relation to Drought Periods, Using the SPI. Water Resources Management 24 (9), 1867-1884. Fiorillo, F., Guadagno, F. M., 2012. Long karst spring discharge time series and drought occurrence in Southern Italy, Environmental Earth Sciences 65, 2273–2283. Garcia, C., Servera, J, 2003. Impacts of tourism development on water demand and beach degradation on the island of Mallorca (Spain), Geografiska Annaler, 85 A(3-4), 287-300. Garcia, C., Amengual, A., Homar, V., Zamora, A. (2017). Losing water in temporary streams on a Mediterranean island: Effects of climate and land-cover changes. Global and Planetary Change, 148, 139-152. Giménez, J., Gelabert, B., Sàbat, F., 2007. The relief of the Balearic Islands, Enseñanza de las Ciencias de la Tierra 15 (2), 175-184. G.I.B. (Govern de les Illes Balears), (2015)., Plan Hidrológico de las Illes Balears 20152021. Memoria, Govern de les Illes Balears. Conselleria d’Agricultura, Medi Ambient i Territori. Available from: http://www.caib.es/sacmicrofront/archivopub.do?ctrl=MCRST259ZI190902&id=19090 2 (Accessed 14 March 2016) Giorgi, F., Lionello, P., 2008. Climate change projections for the Mediterranean region. Global and Planetary Change 63, 90-104.

González-Hidalgo, J.C., Brunetti, M., De Luis, M. 2011. A new tool for monthly precipitation analysis in Spain: MOPREDAS database (monthly precipitation trends December 1945-November 2005), International Journal of Climatology 31(5), 715-731. Green, T. R., Taniguchi, M., Kooi, H., Gurdak, J. J., Allen, D. M., Hiscock, K. M., ... & Aureli, A. (2011). Beneath the surface of global change: Impacts of climate change on groundwater. Journal of Hydrology, 405(3), 532-560. Guttman, N.B., 1999. Accepting the standardized precipitation index: a calculation algorithm. Journal of the American Water Resources Association PAPER 35, 311-322. Hannaford, J., Lloyd-Hughes, B., Keef, C., Parry, S., Prudhomme, C., 2011. Examining the large-scale spatial coherence of European drought using regional indicators of precipitation and streamflow deficit. Hydrological Processes 25, 1146-1162. Haslinger, K., Koffler, D., Schöner, W., & Laaha, G. (2014). Exploring the link between meteorological drought and streamflow: Effects of climate‐catchment interaction. Water Resources Research, 50(3), 2468-2487. Hayes, M., Svoboda, M., Wall, N., Widhalm, M., 2011. The Lincoln declaration on drought indices. Universal Meteorological Drought Index Recommended. Bulletin of the American Meteorological Society, April 2011, 485-488. Doi: 10.1175/2010BAMS3103.1. Hirschi, M., Seneviratne, S., Alexandrov, V., Boberg, F., Boroneant, C., Christensen, O.B., Formayer, H., Orlowsky, B., Stepanek, P. 2011.. Observational evidence for soilmoisture impact on hot extremes in southeastern Europe. Nature Geoscience 4, 17-21.

Hof, A., Schmitt, T., 2011. Urban and tourist land use patterns and water consumption: Evidence from Mallorca, Balearic Islands. Land Use Policy 28 (4), 792-804. Homar, V., Ramis, C., Romero, R., Alonso, S., 2010. Recent trends in temperature and precipitation over the Balearic Islands (Spain). Clim. Change 98, 199–211. Huang, S, Huang, Q., Chang, J., Leng, G., 2016. Linkages between hydrological drought, climate indices and human activities: a case study in the Columbia River basin, International Journal of Climatology 36: 280–290. doi:10.1002/joc.4344 Hughes, J.D., Petrone, K.C., Silberstein, R.P., 2012. Drought, groundwater storage and stream flow decline in southwestern Australia, Geophysical Research Letters 39, L03408, doi:10.1029/2011GL050797 Jansà, A., 2014. El clima de les Illes Balears, Lleonard Muntaner. Jansà, A., Homar, V., Romero, R., Alonso, S., Guijarro, J. A., & Ramis, C. (2017). Extension of summer climatic conditions into spring in the Western Mediterranean area. International Journal of Climatology, 37(4), 1938-1950.

Khan, S., Gabriel, H.F., Rana, T., 2008. Standard precipitation index to track drought and assess impact of rainfall on watertables in irrigation areas, Irrigation and Drainage Systems 22, 159–177. Kløve, B., Ala-Aho, P., Bertrand, G., Gurdak, J. J., Kupfersberger, H., Kværner, J., ... & Uvo, C. B. (2014). Climate change impacts on groundwater and dependent ecosystems. Journal of Hydrology, 518, 250-266. Kumar, R., Musuuza, J. L., Van Loon, A. F., Teuling, A. J., Barthel, R., Ten Broek, J., Mai, J., Samaniego, L., and Attinger, S.: Multiscale evaluation of the Standardized Precipitation Index as a groundwater drought indicator, Hydrol. Earth Syst. Sci., 20, 1117-1131, doi:10.5194/hess-20-1117-2016, 2016. Kundzewicz, Z., 2004. Searching for change in hydrological data. Journal of Hydrological Sciences 49, 3–6. Kundzewicz, Z., Robson, A.J., 2004. Change detection in hydrological records—a review of the methodology. Journal of Hydrological Sciences 49, 7–19. Lana, X., Martínez, M.D., Burgueño, A., Serra, C., Martín-Vide, J, Gómez, L. 2006. Distributions of long dry spells in the Iberian Peninsula, years 1951-1990. International Journal of Climatology 26, 1999-2021. López-Bustins, J.A., 2007. The Western Mediterranean Oscillation and rainfall in the Catalan Countries, Phd thesis, University of Barcelona. López-Moreno, J.I., Beguería, S., Vicente-Serrano, S.M., García-Ruiz, J.M., 2007. Influence of the North Atlantic Oscillation on water resources in central Iberia: Precipitation, streamflow anomalies, and reservoir management strategies, Water Resour. Res., 43, W09411, doi:10.1029/2007WR005864. Lorenzo-Lacruz, J., Vicente-Serrano, S.M., López-Moreno, J.I., Beguería, S., GarcíaRuiz, J.M., Cuadrat, J.M., 2010. The impact of droughts and water management of various hydrological systems in the headwaters of the Tagus river (central Spain). Journal of Hydrology 386, 13-26. Lorenzo-Lacruz, J., Vicente-Serrano, S.M., González-Hidalgo, J.C., López-Moreno, J.I., Cortesi, N., 2013. Hydrological drought response to meteorological drought in the Iberian Peninsula. Climate Research 58, 117-131. Lorenzo-Lacruz, J., Morán-Tejeda, E., 2016. Spatio-temporal patterns of meteorological droughts in the Balearic Islands, Cuadernos de Investigación Geográfica 42 (1), 49-66. Martín-Vide, J., López-Bustins, J.A., 2006. The Western Mediterranean Oscillation and rainfal in the Iberian Peninsula, International Journal of Climatology 26, 1455–1475.

McKee, T.B.N., Doesken, J., Kleist, J. 1993. The relationship of drought frequency and duration to time scales. In: Eight Conference on Applied Climatology. American Meteorological Society, Anaheim, CA, 179-184. Mendicino, G., Senatore, A., Versace, P., 2008. A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a Mediterranean climate, Journal of Hydrology 357, 282– 302. Meixner, T., Manning, A. H., Stonestrom, D. A., Allen, D. M., Ajami, H., Blasch, K. W., ... & Flint, A. L. (2016). Implications of projected climate change for groundwater recharge in the western United States. Journal of Hydrology, 534, 124-138. Morán-Tejeda, E., Ceglar, A., Medved-Cvikl, B., Vicente-Serrano, S.M., LópezMoreno, J.I., González-Hidalgo, J.C., Revuelto, J., Lorenzo-Lacruz, J., Camarero, J., Pasho, E., 2013. Assessing the capability of multiscale drought datasets to quantify drought severity and to identify drought impacts: an example in the Ebro Basin. International Journal of Climatology 33 (8), 1884-1897. Nicault, A., Alleaume, S., Brewer, S., Carrer, M., Nola, P., Guiot, J., 2008. Mediterranean drought fluctuation during the last 500 years based on tree-ring data. Climate Dynamics 31, 227-245. Obasi, G.O.P., 1994. WMO’s role in the International Decade for natural disaster reduction. Bulletin of the American Meteorological Society 75, 1655-1661. Osca, J., Romero, R., Alonso, S. 2013. Precipitation projections for Spain by means of a weather typing statistical method. Global and Planetary Change 109, 46-63. Palutikof, J.P., Holt, T., 2004. Climate change and the occurrence of extremes: some implications for the Mediterranean Basin. In A. Marquina (ed.), Environmental Challenges in the Mediterranean 2000- 2050. Kluwer Academic Publishers, pp. 61-73. Pasho, E., Camarero, J.J., de Luis, M., Vicente-Serrano, S.M., 2011. Impacts of drought at different time scales on forest growth across a wide climatic gradient in north-eastern Spain. Agricultural and Forest Meteorology 151 (12), 1800-1811. Patel, N.R., Chopra, P., Dadhwala, V.K., 2007. Analyzing spatial patterns of meteorological drought using standardized precipitation index. Meteorological Applications 14, 329-336. Peters, E. (2003): Propagation of drought through groundwater systems: illustrated in the Pang (UK) and Upper-Guadiana (ES) catchments, phd thesis, Wageningen University, The Netherlands. Peters, E., van Lanen, H.A.J., Torfs, P.J.J.F., Bier, G., 2005. Drought in groundwater drought distribution and performance indicators, Journal of Hydrology 306, 302–317.

Peters, E., Bier, G., Van Lanen, H. A. J., & Torfs, P. J. J. F. (2006). Propagation and spatial distribution of drought in a groundwater catchment. Journal of Hydrology, 321(1), 257-275. Quiring, S.M., 2009. Developing objective operational definitions for monitoring drought. Journal of Applied Meteorology and Climatology 48, 1217-1229. Raje, D., Mujumdar, P.P., 2010. Reservoir performance under uncertainty in hydrologic impacts of climate change, Advances in Water Resources 33 (3), 312-326. Ridgley, M.A., (1991). Drought, groundwater management and land use planning: the case of central Oahu, Hawaii, Applied Geography 11 (4), 289-307. Robledo Ardial, P., Mateos Ruiz, R., López García, J.M. 2004. Evolución espaciotemporal del contenido en ión nitrato en la zona vulnerable del llano de Inca – Sa Pobla. IGME. VIII Simposio de Hidrogeologia. Tomo 26, 121-135. Rodell, M, Velicogna, I., Famiglietti, J.S., 2009. Satellite-based estimates of groundwater depletion in India, Nature 460, 999-1002. Russo, T. A., & Lall, U. (2017). Depletion and response of deep groundwater to climate-induced pumping variability. Nature Geoscience, 10(2), 105-108. Sheffield, J., Wood, E. F., & Roderick, M. L., 2012. Little change in global drought over the past 60 years. Nature, 491(7424), 435-438. Shukla, S., Wood, A.W., 2008. Use of a standardized runoff index for characterizing hydrologic drought, Geophysical Research letters 35, L02405. doi:10.1029/ 2007GL032487. Sims, A.P., Niyogi, D.S., Raman, S., 2002. Adopting drought indices for estimating soil moisture: A North Carolina case study. Geophysical Research Letters 29 (8), 1183. Spinoni, J., Naumann, G., Vogt, J., & Barbosa, P. (2015). European drought climatologies and trends based on a multi-indicator approach. Global and Planetary Change, 127, 50-57. Strack, O.D.L., 1981. Flow in aquifers with clay laminae: 1. The comprehensive potential, Water Resources Research, 10.1029/WR017i004p00985 Sumner, G.N., Romero, R., Homar, V., Ramis, C., Alonso, S., Zorita, E., 2003. An estimate of the effects of climate change on the rainfall of Mediterranean Spain by the late twenty first century. Climate Dynamics 20, 789-805. Tallaksen, L.M., Hisdal, H., Van Lanen, H.A.J., 2009. Space-time modelling of catchment scale drought characteristics, Journal of Hydrology 375, 363-372. Telesca, L., Lovallo, M., López-Moreno, J.I., Vicente-Serrano, S.M., 2012. Investigation of scaling properties in monthly streamflow and Standardized Streamflow

Index (SSI) time series in the Ebro basin (Spain), Physica A: Statistical Mechanics and its Applications 391(4), 1662-1678. van der Kamp, G., Hayashi, M., 1998. The groundwater recharge function of small wetlands in the semi-arid northern prairies, Great Plains Research 8 (1), 39-56. Van Loon, A. F. (2015). Hydrological drought explained. Wiley Interdisciplinary Reviews: Water, 2(4), 359-392. Van Loon, A.F., Gleeson, T., Clark, J., Van Dijk, A., Stahl, K., Hannaford, J., Di Baldassarre, G., Teuling, A., Tallaksen, L.M., Uijlenhoet, R., Hannah, D.M., Sheffield, J., Svoboda, M., Verbeiren, B., Wagener, T., Rangecroft, S., Wanders, N., Van Lanen, H.A.J. 2016. Drought in the Anthropocene. Nature Geoscience 9, 89-91. Doi: 10.1038/ngeo2646. Vicente-Serrano, S.M., López-Moreno, J.I., 2005. Hydrological response to different time scales of climatological drought: an evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin. Hydrology and Earth System Sciences 9, 523-533. Vicente-Serrano, S.M., 2006. Differences in spatial patterns of drought on different time scales: an analysis of the Iberian Peninsula. Water Resources Management 20, 37-60. Vicente-Serrano, S.M., Beguería, S., Lorenzo-Lacruz, J., Camarero, J.J., LópezMoreno, J.I., Azorin-Molina, C., Revuelto, J., Morán-Tejeda, E., Sánchez-Lorenzo, A., 2012a. Performance of drought índices for ecological, agricultural and hydrological applications. Earth Interactions 16 (10), 1-27. Vicente-Serrano, S.M., López-Moreno, J. I., Beguería, S., Lorenzo-Lacruz, J., AzorínMolina, C., Morán-Tejeda, E., 2012b. Accurate computation of a streamflow drought Index, J Hydrol Eng 17, 318−332. Vicente-Serrano, S.M., 2016. Foreword: drought complexity and assessment under climate change conditions, Cuadernos de Investigación Geográfica 42(1), 7-11. Wilhite, D.A., 2000. Drought as a natural hazard: concepts and definitions. In D. Wilhite (ed.), Drought: A Global Assessment. Vol 1, Routledge, London, pp. 3-18. Zomlot, Z., Verbeiren, B., Huysmans, M., & Batelaan, O. (2015). Spatial distribution of groundwater recharge and base flow: Assessment of controlling factors. Journal of Hydrology: Regional Studies, 4, 349-368.

Study

Data/model used

Location/study area size

Key results

1. Chen et al., 2002

82 observation wells, 1 weather station. Groundwater levelprecipitation relationship analyzed through Fourier analysis and cross-correlation. 10 groundwater simulated series, 1 weather station (37 years, daily scale). Threshold level approach used to pinpoint groundwater droughts.

Southern Manitoba aquifer (Canada). 18000 km².

1. Groundwater level responses to precipitation vary among wells. 2. The response of groundwater to precipitation is produced with a time delay.

Pang catchment (United Kingdom). 170 km².

1. Propagation of meteorological drought to groundwater decreases the number of droughts, but increases its severity. 2. Formulation and performance of groundwater drought indicators.

3. Peters et al., 2006

Simulated series of groundwater levels, recharge and discharge. Threshold level approach used to pinpoint groundwater droughts.

Pang catchment (United Kingdom). 170 km².

1. More droughts in the recharge and discharge than in groundwater level, although the last are more severe. 2. Drought conditions vary across the catchment. 3. Groundwater level near intermittent streams may lead to high-normalized drought deficits.

4. Bhuiyan et al., 2006

SWI (Standardized Water-level Index) to monitor aquifer recharge deficit.

Aravalli region (India). 25000km² .

5. Mendicino et al., 2008

Formulation of the GRI (Groundwater resources Index). Cross-spectral analysis to relate GRI and SPI introducing lags.

Calabria region (Italy). 3 river basins. 2079km².

6. Fiorillo et al., 2010

3 spring discharge series, 4 weather stations. SPI-spring dicharges cross-correlations. 3 spring discharge series, 4 weather stations. SPI-spring dicharges cross-correlations, introducing lags. 6 groundwater storage series, interpolated precipitation data.

Western Campania region (Italy). Caposele, Cassano and Serino spring discharges. 248km². Western Campania region (Italy). Caposele, Cassano and Serino spring discharges. 248km².

14 groundwater level series, gridded precipitation. Formulation of the Standardized Grounwater level Index (SGI).SPI-SGI cross-correlations, introducing lags. 74 observation boreholes, gridded precipitation. SPI-SGI cross-correlations, with lags. Kmeans clustering.

14 boreholes across major UK aquifers.

19 groundwater level series and 2 rainfall series with 15 minutes interval. Correlations between rainfall and water levels. 1991 and 49 observation wells in two regions. Variable series length (at least 10 years of monthly series). Non-parametric method (Kernel density estimator). Cross-correlations between SPI (1 to 48mo.) and SGI with lags. 12 water table series, 44 observational precipitation series. SPI-SGI cross-correlations. Exploration of aquifer characteristics on groundwater response.

West and Northwest Ireland.

2. Peters et al., 2005

7. Fiorillo et al., 2012

8. Hughes et al., 2012

9. Bloomfield et al., 2013

10. Bloomfield et al., 2015

12. Cai et al., 2016

13. Kumar et al., 2016

This study

9 catchments close to the Murray river basin (Western Australia). 172km².

Lincolnshire (UK).

1. SWI is an indicator of water-table decline and an indirect measure of recharge 2. A deficient rainfall as per the SPI index does not always correspond to hydrological or vegetative drought. 3. No clear relationship was found between SPI and other drought indices. 1. Highest correlations between GRI and SPI were found at 6, 12 and 24mo. SPI time scales, in three different basins. 2. Better forecasting capabilities of the GRI compared with the SPI. 1. Highest correlations vary among basins (9 and 12 SPI time-scale), with high correlations until 24 time-scale. 1. The best SPI scales to monitor spring discharges droughts are the 9 and 12mo. 2. Aquifers in karst systems act as “big reservoirs” smoothing meteorological droughts effects. 1. Annual rainfall greater than 1050–1400 mm required to measurably increase groundwater storage. 2. Where the groundwater remains connected to the stream bed, runoff ratio is strongly correlated to groundwater storage. 3. When a groundwater drought occurs karst systems may find it difficult to recover a normal response to the hydrological cycle. 1. In most cases, the highest correlations between the SPI and the SGI were recorded between the 6 and 12 SPI time-scales. 2. In four aquifers, the highest correlations between SGI and SPI were recorded at SPI time scales > 20 months. 1. The highest correlation (R=0.84) for all 74 series where recorded at 12-months SPI. 2. 6 groups identified by cluster classification. None of them registered the highest correlation between SPI-SGI at time scales longer than 20mo. 1. Not comparable.

Southern Germany and the Central Netherlands.

1. Maximum SPI-SGI correlations are R>0.4 and R<0.87. 2. The optimal SPI accumulation period vary between 3 and 24 SPI mo.

8 groundwater bodies across a Mediterranean island. 3640km².

1. 3 response patterns: high SGI-SPI correlations at short (<6mo.), medium and long (>24mo.) SPI time scales. 2. Recharge of some aquifers was related to drought conditions during the main annual precipitation peak, whereas in other aquifers the recharge was related to the secondary precipitation peak. 3. Intense aquifer exploitation creates a critical dependence on precipitation during the dry season. 4. Different responses of aquifer levels to precipitation variability were related to heterogeneous climatic, lithological, and management factors, highlighting the need of a detailed and adaptive water policy for each case.

Table1. Summary of relevant studies assessing groundwater response to precipitation variability (the current study is added in the final row for completeness), with especial focus on the use of the SPI to relate climate conditions with groundwater variables.

Exploitation Index (pumping/rainf all recharge)

Hydraulic conductivity m/day

Transmissivity m2/day

Observati on well (ID)

Hydrogeol ogical unit (km2)

N1

Almadra va (S-33)

18.05 Almadra va (69)

M3 L’Arboçar (8.12)

7.2

Lower Jurassic sandstone, dolomite and breccia

0.2

5-22

1-5

200

N2

Estremer a (S-2)

18.08 S’Estrem era (80)

M1 Bunyola (47.8)

44.2

Lower Jurassic sandstone, dolomite and breccia

0.72

40-161

100

50,000

N3

Massanel la blue

18.09 Alaró (79)

M1 Lloseta (34.8)

24.7

Lower Jurassic sandstone, dolomite and breccia

0.53

75-161

5

1,000

Map code

N4

Groundwater body (km2 )

Estimated recharge area (km2 )

Oscillatio n of the waterlevel depth in the well (m)

Massanel la red

Aquifer lithology

76-190

N5

Can Bajoca (S-1)

18.10 Ufanes (52)

M1 Caimari (49)

44.2

Lower Jurassic sandstone, dolomite and breccia

0.02

112-180

1

100

N6

S-3

M1 Sa Pobla (130.42)

124.5

19-29

5

1,000

S-12

Plioquaternary sand and calcarenites

0.43

N7

18.11 Inca-Sa Pobla (358.4)

N8

S-14

N9

S-7

N10

Can Guilleme t

N11

Son Cosmet

N12

14-41 17-66

18.21 Llucmajo r-Campos (638)

M2 Llubí (89.4)

89

Miocene (Tortonian) carbonate sandstone

0.75

42-47

10

2,000

M2 Pla de Campos (253.4)

253.4

Quaternary sand and gravels

0.37

36.7-41

10

1

M3 Son Mesquida (62)

61.7

Miocene (Serravalian) calcarenites

0.66

32.6-33.7

10

1,000

Son Mesquida

Table 2. Aquifer descriptions and characteristics.

56.5-65

N1

N2

N3

N4

N5

N6

N7

N8

N9

N10

N11

N12

ONGOING MONTH

-0,27

0,08

-0,25

-0,54

-0,09

-0,28

-0,41

-0,47

-0,15

-0,27

-0,31

-0,70

1-MONTH LAG

-0,56

-0,07

-0,38

-0,72

-0,19

-0,51

-0,65

-0,68

-0,28

-0,20

-0,25

-0,61

2-MONTHS LAG

-0,72

-0,22

-0,35

-0,64

0,07

-0,58

-0,57

-0,64

-0,26

-0,11

-0,13

-0,37

3-MONTHS LAG

-0,65

-0,30

-0,23

-0,42

0,13

-0,45

-0,38

-0,44

-0,21

0,00

0,03

-0,06

Table 3. Correlation coefficients (Spearman’s ρ) between monthly tourist arrivals and aquifer level of the current month and the 3 subsequent months. Significant correlations (ρ = 0.05) are shown in bold and strongest correlations in red.

Figure 1: Top left: location of Mallorca and mean annual precipitation during the period 1974–2014. Bottom left: topography of Mallorca and location of the weather stations used in the study. Top right: Mallorca lithological map superimposed on a digital elevation model (vertical exaggeration: ×5). Bottom right: interpolated depth of observation wells (vertical exaggeration: ×10), and location of those used in this study.

Figure 2: a) L-moments ratio diagram including examples of Estremera (N2), Massanella red (N3), Sa Pobla (N6) and Llubí (N9) piezometric level series (top). b) Temporal evolution of the Standardized Groundwater Index (SGI) for the Estremera aquifer (N2) and the SPI at the nearest weather station, computed at the scale (44 months) for which the maximum correlation was obtained (bottom).

Figure 3: Pearson R correlation coefficients for the 1- to 48-month SPI and series of the Standardized Groundwater Index (SGI). Arrows depict the SPI time scales at which the maximum correlations were obtained. Dashed line in graph N5 depicts correlations considering the aquifer water table (height/depth) at Can Bajoca and the standardized stream flow of the Sant Miquel spring, flowing from the Ufanes aquifer (N5 Can Bajoca well series).

Figure 4: Pearson R correlation coefficients between monthly SGI series and monthly SPI values at various time scales (1 to 48 months). Significant correlations (ρ = 0.05) correspond to R values greater than 0.25.

Figure 5: Spatial distributions of correlations between the SGI series registered at each well (N1–N6) and the SPI series computed at various time scales, corresponding to 44 weather stations distributed across the island.

Figure 6: Spatial distributions of correlations between SGI series registered at each well (N7–N12) and the SPI series computed at various time scales, corresponding to 44 weather stations distributed across the island.

Figure 7: a) Time scales for which the highest correlation between the SPI and SGI series were obtained, classified according to the percentage of permeable lithological strata within the corresponding aquifer recharge area. b) Magnitude of the maximum correlation obtained between the SGI and the SPI series (at any time scale), classified according to the presence/absence of clay within the corresponding aquifer recharge area. Summary of monthly correlations between the SGI and the SPI series computed at various time scales (1 to 48 months), grouped by aquifer exploitation level for June (c), July (d), and August (e). Red line: median; box borders: 25th and 75th percentiles; whiskers: 10th and 90th percentiles; dots: 5th and 95th percentiles. The p-values at the bottom of the plots indicate whether the differences observed between populations are significant (p-value < 0.05) by means of a two-sample Wilcoxon Mann-Whitney test.

We assessed the response of 8 Majorcan groundwater bodies to precipitation variability using the SPI and the SGI. Enhanced aquifer responses to precipitation were detected at short, medium and long SPI time scales. Seasonal differences were related to climate variability and the degree of aquifer exploitation. Recharge of some aquifers is related to the influence of precipitation that is of limited spatial extent. Our findings highlight the importance of using high spatial resolution hydro-climatic databases.