Site Description

The karst aquifer system of northern Puerto Rico (KA-NPR) comprises 19% of the island and contains the most extensive and productive aquifers of the island (Padilla et al., 2011; Maihemuti et al., 2015; Torres et al., 2019).

The area of study (Figure 1(a)) is located in the KA-NPR between the municipalities from Arecibo to Toa Baja.

The KA-NPR is comprised by four major hydrogeo-logical units (Figure 1(b)): an upper aquifer, a middle confining unit, a confined aquifer, and a basal confining unit. Structurally, the rocks form a gently north dipping wedge, abutting southward against a volcanic mountain chain and thickening northward to about 1400 meters by the seashore (Ghasemizadeh et al., 2012). The up-per aquifer, contained within the Aguada and Aymamon Limestones (Figure 1), is mostly unconfined and linked to the surface throughout most of its outcrop area (Pa-dilla et al., 2011; Torres et al., 2018). It is characterized by highly variable porosity and permeability properties over short distances reflecting the variable distribution of conduit porosity (Ghasemizadeh et al., 2012). The lower aquifer mostly contained within the Montebello and Lares Limestones (Figure 1) and is confined toward the coastal zone. It outcrops to the south of the upper aquifer, where it is recharged. There is a direct connec-tion between the upper and lower aquifers along the out-crop of the confining unit, allowing groundwater flow from the unconfined part of the lower aquifer into the upper aquifer (Torres-González et al., 1996; Torres et al., 2019).

The KA-NPR is characterized by a karst topography having distinctive surface and subsurface features asso-ciated with sinkholes, springs, caves, as well as sinking, losing and gaining streams. Elevation is highest toward the south, where the lower aquifer outcrops, and low-est toward the coastal area, where outcrops of the upper aquifer intermingle with surface alluvial deposits (Torres et al., 2019).

conduits and high permeability zones that are well con-nected to sinkholes, sinking streams and other surface features provide for direct recharge into the groundwater systems (Padilla and Vesper, 2018; Torres et al., 2019) and often reflect a rapid hydraulic response to rainfall events (Green et al., 2006). Diffuse flow in karst aqui-fers, on the other side of the flow spectrum, may behave similarly to that of porous media, reflecting slower, lami-nar, and relatively uniform flow. Regions dominated by diffuse flow generally show delayed and attenuated re-sponse to rainfall events.

Rainfall and recharge events induce variations in hydrau-lic head potential and gradients dimension of saturated regions and spring discharge. Spatial variations reflect properties of the terrain in which the flow is occurring.

For instance, conduit-controlled regions show rapid wa-ter level and pressure response to recharge events, while those in more diffuse zones show slower response (Bail-ly-Compte et al., 2010).

The response of groundwater to variations in precipita-tion had been studied in different parts of the world. Cai and Ofterdinger (2016) analyzed the response to rainfall, seasonal variations, and estimated groundwater recharge using 19 groundwater level hydrographs from two Irish sites. Using correlation and spectral analysis of rainfall and groundwater level time series, they found a rapid groundwater level response to rainfall, with little season-al variability, suggesting recharge from fast infiltration flow pathways. Lorenzo-Lacruz et al. (2017) also stud-ied the response of groundwater level to precipitation variability and other aquifer characteristics in Mallorca, a Mediterranean island with limited resources and very vulnerable to climate variability. Their study found that aquifers responded in short (<6 months), medium (6–24 months), and long (>24 months) time scales, which were related to multiple factors, including climate, lithol-ogy, and management. Karimi et al. (2018) studied the groundwater flow in different springs to determine the aquifer characteristics of the karst aquifer from western Iran. Through analysis of spring hydrographs, physico-chemical parameters, and geological topographical char-acteristics they determined that the aquifer was domi-nated by a diffuse-conduit flow system.

This study assesses the response of groundwater levels in different wells to hydrologic events and conditions in different areas of the karst groundwater aquifer

sys-Methodology

Precipitation and groundwater levels data were integrat-ed with hydrogeological characteristics of the aquifer to determine the response of groundwater levels to differ-ent hydrologic evdiffer-ents and conditions in the study area, and identify potential areas with conduit or diffuse flow types. Daily precipitation data from the period of 2011–

2018 was collected from multiple National Oceanic and Atmospheric Administration (NOAA) (NOAA, 2019) stations in the study area (Figure 1). Although more sta-tions have precipitation data in the study area, daily pre-cipitation was only available in 5 stations for the period from 2011 to 2018. Depth to water level from 2011–2018 was obtained from the United States Geological Survey (USGS) National Water Information System (USGS, 2019) for multiple wells in the study area (Figure 1).

Groundwater levels were calculated by subtracting the depth to water level from the ground elevation. Hydro-Flow in the KA-NPR occurs in conduits and the rock

matrix (Anaya et al., 2014; Torres et al., 2018; 2019).

Groundwater enters the system through surface infiltra-tion and direct injecinfiltra-tion of runoff into karstic conduits via sinkholes. Flow in the upper and lower aquifers moves regionally northward toward the Atlantic Ocean (Ghasemizadeh et al., 2012) and locally to surface streams and wetlands (Renken et al., 2002). Discharge occurs at wells, springs, and through seepage at sur-face features. Springs mostly drain the unconfined parts of the upper and lower aquifers (Rodríguez-Martínez, 1997). Groundwater is mostly extracted from the up-per aquifer because it is the most accessible for drilling and pumping, although several industrial and municipal wells extract from the lower aquifer. These aquifers are the principal groundwater source of water supply in the region (Torres-González et al., 1996; Conde-Costas and Rodríguez-Rodríguez, 1997; Cherry, 2001).

Figure 1. (a) Hydrogeology of the KA-NPR study area; rainfall stations, wells with groundwater levels information, wells, and springs in the study area; (b) Hydrogeology cross section of the area between Barceloneta and Florida (Modified from Renken et al., 2002).

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geological properties and characteristics (Figures 1 and 2) from the study area (aquifer type, sinkhole coverage, and hydraulic conductivity) were collected from Giusti and Bennet (1976) and Renken et al. (2002). Location and information about major documented springs in the study area was also collected from Rodríguez-Martínez, 1997 and the Puerto Rico Testsite for Exploring Con-tamination Threats (PROTECT) Project Database (Tor-res et al., 2019).

Data collected was analyzed using spatial, temporal, and statistical methods. Geographic Information Sys-tem (GIS) was used to locate wells and springs in the study area and to identify the hydrogeological character-istics of the aquifer. Wells with water level information were associated with the closest precipitation station to perform the temporal and statistical analysis between daily groundwater levels in wells and precipitation. The

“Proximity Analysis-Create Thiessen Polygon Tool” in GIS was used to determine which precipitation station was closer to the different wells in the study area.

Data was analyzed using multiple statistical methods, including descriptive statistics, correlation analysis, and time series analysis. Time series analysis was performed for wells that have the most amount of continuous data for the period of study on the different rainfall seasons.

The cross-correlation function (CCF) or r_xy (k) (Minitab, 2019) was used to determine potential relationships be-tween daily precipitation and groundwater levels time series and obtain the response lag-times for different months. The response lag-time was estimated from the maximum of the cross-correlation function and defined as mean response time between water-level in a well to a rainfall event (Cai and Ofterdinger, 2016). The

correla-tion between two time series was identified to be signifi-cant at a 95% confidence interval (α=0.05) when, where n is the number of observations and k is the lag-time (Minitab, 2019). Cross correlation was performed at different months during the study period from 2011–2018 to compare if the lag-times change with different hydrological conditions (rainfall conditions).

Seasonal hydrological conditions were classified as ex-tremely dry, dry, wet, and exex-tremely wet based on the quartiles (Q₁, Q₂, and Q₃) of total monthly precipitation from all the stations in the study area (Figure 1). Total monthly precipitation at the nearest precipitation station to a well was compared to the quartiles to classify the seasonal hydrological conditions for the area around the well in a particular month. If the total monthly precipita-tion (TMP) at the nearest rainfall staprecipita-tion was less than Q₁, the month was classified as “extremely dry”. If TMP was greater than Q₁ but less than or equal to Q₂ it was classified as “dry”. If TMP was greater than Q₂ but less than or equal to Q₃ it was classified as “wet”, and if TMP was greater than Q₃ it was classified as “extremely wet”.

This information was used for the time series analysis, to select the different months, according to the hydrologi-cal conditions in the area surrounding the different wells.

Results and Discussion

Spring Density

The number and density (number of springs per square kilometer) of major springs varies in the study area.

Though minor springs may be present in the study area that have not been reported, it is assumed that these would not affect the overall observed distribution. The number and density of major springs tend to be higher on the western side of the study area than on the

east-Figure 2. (a) Hydraulic conductivities and (b) sinkhole coverage area; location of wells, springs, and rainfall stations in the study area.

ern side (Figure 3). Most of the springs on the western side of the study area are classified as conduit, whereas several springs on the east side are classified as diffused (Rodríguez-Martínez, 1997). Higher number and density of springs are generally associated with areas of greater sinkhole coverage (Figure 2), and potentially to a greater connection to direct recharge into the groundwater sys-tem.

Precipitation Data Analysis

Analysis of precipitation data showed the mean of total monthly precipitation for all stations in the study area to vary between 114 ± 78 mm at the Arecibo 5.2 ESE station and 187 ± 105 mm at the Arecibo Observatory station (Figure 4) during the 2011–2018 period. In 2011, mean total monthly precipitation varied from 176 ± 93 mm in Manatí 2E station to 205 ± 102 mm in Are-cibo Observatory station. Mean total monthly precipita-tion in the rainfall staprecipita-tions decreased during the next few years, with Manatí 2E station showing the lowest mean total monthly precipitation in 2012 (164 ± 78 mm), 2013 (157 ± 88 mm) and 2014 (113.48 ± mm), and the Arecibo Observatory station showing the highest average pre-cipitation values in 2012 (210 ± 103 mm), 2013 (175 ± 110 mm), and 2014 (159.63 ± 120 mm). In 2015, all the stations, except for Arecibo Observatory and Palmarejo stations showed mean total monthly precipitations be-low 100 mm (Figure 4). The be-lowest average precipitation in 2015 was observed in Arecibo 5.2 ESE station (59 ±

56 mm). This decrease in precipitation corresponded to a drought that occurred in Puerto Rico from 2015–2016, with the most intense period of drought occurring in Au-gust 2015 (NIDIS, 2019). In 2016, mean total monthly precipitation increased compared to the previous year, with values ranging from 128 ± 62 mm in Manatí 2E sta-tion to 201 ± 80 mm in the Arecibo Observatory stasta-tion.

In 2017, average precipitation show an increase, com-pared to 2016, with the highest total monthly precipita-tion observed in September 2017 (Figure 4), when Hur-ricane María hit Puerto Rico, the strongest hurHur-ricane to make landfall on the island since 1928, breaking rainfall records (Keellings and Hernández Ayala, 2019). In 2018, average decreased in all stations, except for Palmarejo station in Vega Baja (Figure 4).

Groundwater levels Analysis

Groundwater levels from 11 wells were analyzed from the period of 2011–2018, but the results from two sites are presented and discussed here. These two sites rep-resent a wide range of observed hydraulic responses, which are related to the wide spectrum of flow modes in the study area.

Well #1, as identified in Figure 1, shows rapid increase in water levels for periods when precipitation increased rapidly, as observed in September 2011, December 2011, May 2012, May 2013, August 2013, August 2014, Sep-tember 2017, and December 2017 (Figure 5 and

Fig-Figure 3. Number of springs per square-kilometer, determined by using the “Create Fishnet Tool” in GIS, rainfall stations, wells with groundwater levels information, wells and springs in the study area.

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ure S1). Water levels in this well tend to have a smooth response to precipitation during periods of low precipita-tion, as observed during the periods of January–March 2011, 2013, 2014, and 2017. In 2015 (Figure 5c), when the peak of the drought was occurring, Well #1 show lower groundwater levels than the previous years, with maximum water levels at approximately 1.2 m above mean sea level (amsl). In general, precipitation and water levels near this well show a decreasing tendency through time.

Lag-times in Well #1 during the analysis period (Figure 6) range between 1 and 11 days, with an average of 2 days.

In general, the lag-times are similar for different hydro-logical conditions, except in 2015 when the lag-times are higher than the other years. In December 2013, which is considered a dry month according to the calculated statistics for seasonal classification, Well #1 also shows a lag-time higher than the other months in the same year and the previous year. These higher lag-times are gener-ally associated with low CCF values and lack of correla-tions between water levels and precipitation (Figure 6).

The high lag-time in groundwater levels during 2015 and December 2013 is mostly attributed to diffuse flow com-ing from storage durcom-ing drought conditions. Any rainfall during these periods would most likely be retained in the Figure 4. Total monthly precipitation from 2011–2018 at NOAA stations in the study area.

Figure 5. Time series plot of daily precipitation (upper graphs) and groundwater levels (m amsl) (lower graphs) for Well #1 for: (a) 2011–

2012; (b) 2013–2014; (c) 2015–2016; and (d) 2017–2018.

Figure 6. Lag-time and cross correlation for different hydrological conditions in the area surrounding Well #1. The values

of show the significance of the correlation between rainfall and groundwater levels.

epikarst before reaching the saturated zone and affecting water levels. Though it was not possible to calculate the lag-time and observe the response of groundwater levels prior to the Hurricane María (September 2017) because of the lack of precipitation data in that area for that pe-riod of time, water levels show rapid increase. Lag-times in extremely wet conditions ranged from 0 to 2 days, except for May 2013, when it was 4 days. Lag-times for dry and extremely-dry months ranged from 0 to 2 days when preceded by wet or extremely wet months, except for the 2015 and 2016 period and December 2012. This is attributed to sufficient antecedent moisture in the epi-karst to support recharge into the saturated zone.

Well #1 is located in the upper aquifer in areas having low-to-intermediate hydraulic conductivities (Figure 2).

Although Well #1 is located in areas classified with low sinkhole coverage (Figure 3), there are areas with higher sinkhole coverage upstream (south) of the well. It is, therefore, likely connected to sinkholes that may cause the rapid response of groundwater levels to precipitation.

Slow response of groundwater levels during extremely dry conditions, as observed in the period of 2015–2016, is attributed to the low-to-intermediate hydraulic con-ductivities.

Well #2 shows a slow response of groundwater levels to changes in precipitation (Figure 7 and Figure S2), having smooth curves in the time series plots. Even with high pre-cipitation changes, groundwater levels peaks are not well defined as in the case of Well #1 (Figure 5). The time se-ries analysis of groundwater levels and precipitation data in Well #2 indicate highly variable lag-times for different hydrologic conditions, with an average lag-time of approxi-mately 5 days and a maximum value of 12 days (Figure 8).

Similar to Well #1, the higher lag-times are generally as-sociated with low CCF values and lack of correlations be-tween water levels and precipitation (Figure 6).

Well #2 is located in the upper aquifer on the eastern side of the study area, in regions of low hydraulic conductivi-ties, low sinkhole coverage, and spring density (Figures 2 and 3). Sinkhole coverage upstream of Well #2 is also low. Low sinkhole coverage near and upstream of this well reflects low connectivity to surface features and suggests diffuse recharge. The low and slow response of groundwater levels to precipitation in this well are at-tributed to lack of direct connection to surface features and low hydraulic conductivities.

Time series analysis for all the wells show that average lag-times in the wells varied from 2 to more than 8 days (Figure 9 and Figure S3 to Figure S12). The wells are widespread over the study area, but do not show a par-ticular spatial pattern. From all sites analyzed, most of the sites (55%) show average lag-times between 4 and Figure 7. Time series plot of daily precipitation (upper graphs) and groundwater levels (m amsl) (lower graphs) for Well #2 for: (a) 2011–

2012; (b) 2013–2014; (c) 2015–2016; and (d) 2017–2018.

Figure 8. Lag-time and cross correlation for different hydrological conditions in the area surrounding Well #2.

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6 days, and suggest mixed flow modes. Those sites are located in areas with different hydraulic conductivities but low sinkhole coverage. Wells with faster average lag-times are located at or near areas of higher sinkhole coverage. A well showing a lag-time greater than 8 is located in an area with many pumping wells that may be capturing flow and affecting water levels artificially.

Results, thus, suggest that rapid response associated with conduit-flow are more likely to occur in wells located near high sinkhole coverage. This is attributed to greater potential for direct recharge.

It is important to recognize that other variables, not taken into consideration in this study, may further influence the hydraulic response of the groundwater system, and should be considered for a better characterization of the aquifer in relation to the response of groundwater levels to precipitation. These include proximity to and influ-ence of pumping wells. Uncertainty on the areas that contribute recharge to observation wells may pose some

limitations in using the closest rainfall stations for the analysis. Enhancement of the method to better character-ize the system thus requires greater temporal and spatial resolution and continuous measurements of rainfall and groundwater level data in the watershed that influence the recharge of a particular well.

Conclusions

Temporal analysis of precipitation and groundwater levels data from multiple wells and rainfall stations in the karst aquifer of northern Puerto Rico show vari-able hydraulic response of the ground water levels to precipitation. The variability is associated with a wide spectrum of flow modes, ranging from conduit- to dif-fuse-dominated flows. It is also related to the connectiv-ity of the flow region around the well to sinkholes and other surface features that promote direct recharge into the groundwater system. Wells showing rapid response (low lag-times values) are generally near areas with high sinkhole coverage, whereas those with slow response are located in areas with low sinkhole coverage. Results also show slow response time and lack of correlations between water levels and precipitation during extended dry conditions, such as the drought of 2015–2016, and periods of low-preceding precipitation. These results are indicative of diffuse flow coming out of storage from the rock matrix and antecedent moisture conditions in which any rainfall would be taken into storage in the epikarst.

The results obtained from this study will help to assess the factors affecting the hydraulic response of ground-water in karst systems characterized by high primary and secondary porosity, such as those found in northern Puerto Rico.

Acknowledgements

Support of the work described is provided by the

Support of the work described is provided by the