Spatiotemporal hydrological dynamics in the Caribbean Anthropocene: the case of a Puerto Rican coastal urban wetland

Spatiotemporal hydrological dynamics in the Caribbean Anthropocene: the case of a Puerto Rican coastal urban wetland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal hydrological dynamics in the Caribbean Anthropocene: the case of a Puerto Rican coastal urban wetland Solimar Pinto-Pacheco, Elix Hernandez-Figueroa, Gloria Ortiz-Ramirez, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6933337/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The present study aimed to understand how the combined interactions of local weather variability, marine-terrestrial connectivity and/or anthropic modifications influence spatiotemporal hydrological dynamics and ionic concentrations of anthropically impacted urban coastal wetlands. Sampling was carried out in Ciénaga Las Cucharillas Nature Reserve, a palustrine-estuarine wetland located in the northeastern of Puerto Rico with historical hydrological modifications. We conducted monthly sampling from 2018 to 2022 from ten monitoring wells at phreatic level and at 2.5 m depth. Water salinity, and ionic concentrations (Na, Mg, Ca, and K) were measured. Local climate regulated temporal variations in salinity and freshwater inputs, as well as phreatic levels. Extreme rainfall associated with atmospheric disturbances elevated phreatic levels above surface and homogenized salinities throughout the sampling site. A tridimensional hydrological mosaic was observed throughout the study area that stemmed from the deep sub-surface terrestrial-marine connectivity and the presence of natural subsurface channels connecting areas to the coastline. The wetland’s geomorphology, substrate composition, and water flow also contributed to the hydrological dynamics of the wetland as reflected in Mg/Ca and Na/K ratios. The present study provides a valuable framework for modeling impacted urban coastal wetlands. Future monitoring and management strategies should include groundwater salinity measurements, as the results indicate that is equally as important as phreatic salinity and may even obscure evidence of deep-subsurface marine intrusion. urban coastal wetland Puerto Rico terrestrial-marine connectivity sub-surface saline intrusion hydrological dynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Coastal wetland hydrology is determined by the interplay of regional and local weather, freshwater inputs and tidal seawater inflow (Lugo et al. 1988 ; Tiner 2018 ; Manzoni et al. 2020 ). Global climate change amplifies the effects of historical land use changes on wetland hydrodynamics (Baldwin 2000 ; Vilardy et al. 2011 ). The hydrologically modified state of the wetland has a direct impact on marine-terrestrial connectivity (Botero and Mancena-Pineda 1996 ; Crook et al. 2015 ), therefore reshaping the spatiotemporal physicochemical dynamics of these ecologically important ecosystems. Climate change and regional climate variability influences sea surface temperatures, sea level rise, frequency and intensity of atmospheric phenomena, and precipitation patterns (Michener et al. 1997 ; Osland et al. 2016 ). Sea level rise is considered to have the highest impact in coastal wetlands since salinity regimes and flooding are important abiotic drivers within these ecosystems (Morris 2002). Saltwater intrusion, alongside with a reduction of freshwater riverine flow, alterations of subsurface water, and storm surges can result in salinization of wetland ecosystems (Herbert et al. 2015 ; White and Kaplan 2017 ). This is highly relevant in Caribbean ecosystems where the current sea level rise is 3.40 ± 0.3 mm/year, mostly as a result of oceanic thermal expansion (Maitland et al., 2024 ). Furthermore, Caribbean climate is influenced by large scale weather patterns, such as North Atlantic Oscillation (NOA), El Niño Southern Oscillation, the Intertropical Convergence Zone (ITCZ), steady easterly trade winds, the expansion of the western hemisphere warm pools, the intrusion of cold front, and the passage of atmospheric phenomena (Ashby et al. 2005). The degree of interaction among these teleconnections influences precipitation patterns, winds, river discharge and sea level in the Caribbean. Climate change potentiates the degree of teleconnections' effects (Gould et al. 2022 ), reflected in dry and wet extremes (Giannini et al. 2000 ; Vega et al. 2020 ) The Caribbean is recognized as a biodiversity hotspot, being the marine and terrestrial coastal zone key habitats in the conservation of biodiversity (Gould et al. 2020 ). Along the Caribbean basin, historical and present modification of hydrological conditions of coastal wetlands are typical and accompanied by its effects on vegetation structure and function (Ellison & Farnsworth 1996 ; Salazar-Ortiz & Cuevas 2017 ). While current research in Caribbean wetlands has focused on plant structure and water chemistry (Webb and Gómez-Gómez 1998 ; Williams et al. 2013 ; Branoff et al. 2018 ; Hernandez et al. 2021), there is growing recognition that wetland management requires an ecohydrological approach that takes into consideration: 1) how hydrological processes effect the distribution, structure, and function of ecosystems, and2) how biotic processes effect the water cycle (Nuttle, 2002 ). This is relevant in Caribbean urban coastal wetlands where the environmental heterogeneity is often defined by habitat mosaics, instead of gradients (Hernandez et al. 2021). This study uses an integrated multi-level holistic approach to assess the ecohydrological dynamics in a tropical urban coastal wetland. Specifically, we aimed to understand how the combined interactions of local weather variability, marine-terrestrial connectivity and/or anthropic modifications influence spatiotemporal hydrological dynamics and ionic concentrations. This study will assess a) how spatiotemporal dynamics of the water sources determine salinity, phreatic level and ionic concentrations in an urban coastal wetland, and, b) to what extent does the historic and present land use modifications affect the spatiotemporal ionic distribution in the wetland’s water. We hypothesize that 1) marine-derived elements will reflect the spatiotemporal distribution of the water sources, and 2) ionic spatiotemporal distribution will be determined by land use and hydrological alterations. Ciénaga Las Cucharillas Nature Reserve, a palustrine-estuarine wetland located in the northeastern of Puerto Rico, is the largest coastal urban wetland on the island and serves as the model system. As a result of historical hydrological modifications that increased freshwater inflow and a restricted tidal exchange to deep subsurface intrusion, the wetland supports a mosaic of halophytic and palustrine vegetation (Branoff et al 2018 ; Hernandez et al. 2021). Materials and Methods Study Area The Ciénaga las Cucharillas Natural Reserve (18 26’25.27” N, 66 08’08.39” W) is located on the northern coast of the Caribbean Island of Puerto Rico and encompasses the western side of the San Juan Bay (Fig. 1 A). Average annual temperatures range from 24.7–31 ºC. Annual average rainfall is 1289 mm and follows a bimodal distribution with May-June and September-November having the highest precipitation. During our sampling period (January 2018 – October 2022), the average annual temperature was 27°C (20.6°C – 32.2°C) with 1564 mm average annual precipitation. The present conditions of the wetland are a result of multiple anthropogenic activities: a) the creation of drainage channels from the 17th to the mid-20th century for agriculture (Pumarada-O'Neill 1991 ; Kennaway and Helmer 2007 ),b) the 1940s – 1970s allochthonous soil infill for urbanization, c) the dredging and construction of the Malaria Channel in the late 1940's, and, d) the channel’s outflow regulation by pumps and permanent closure from tidal flow at the mouth channel (Seguinot-Baborsa 1983 ; Webb and Gómez-Gómez 1998 ). As a result, tidal interaction in this wetland occurs via deep subsurface flow (Branoff et al 2018 ; Hernandez et al. 2021). Nowadays, low-and middle-income urbanized areas, and industrial and commercial facilities in the Cucharillas microbasin influence water quality of both the channel and the wetland. These anthropogenic activities caused a decrease in wetland cover where 500 ha of the historical wetland remains, a 90% loss of its original cover (Martinuzzi et al. 2009 ; Lugo et al. 2011 ). Ciénaga las Cucharillas is on top of the Aguada Limestone formation of the North Coast Limestone aquifer system which consists of hard granular calcarenite overlain by alternating beds of chalky marl and rubbly limestone (Anderson 1976 ; Giusti and Bennette1976; Monroe 1980 ). Within the wetland, Saladar muck (Sm) series represents the natural organic soil type, while Martin Peña (Mp) series occurs in areas altered by anthropogenic infill. (USDA, 2023). Saladar muck (Sm) series is a Euic, isohyperthermic Typic Haplosa-prists soil consisting of black, highly decomposed (peat) autochthonous vegetation materials, that reach down to bedrock depth in the substrate. Martin Peña (Mp) series is a fine, mixed, superactive, nonacid, isohyperthermic Humaqueptic Fluvaquents soil that contains deposits of organic material (0–20 cm) close to surface over mineral sediments embedded in the organic matrix silty clay loam and clay, 20–45 cm the first and 45–160 cm depth the latter. The mineral sediments were brought from adjacent higher elevation terrestrial sources for shanty town establishment and development from the 1940’s until the late 1970’s. Research area and sample collection The study site is a 2.2 ha area (Fig. 1 B) selected based on geospatial analyses. This area is a mosaic representative of the hydrological and edaphic conditions, the presence of predominant plant assemblages and distance from main freshwater source (Malaria Channel) and marine source (San Juan Bay) (Hernandez et al. 2021; Ortiz-Ramirez et al. 2024). Water samples were collected monthly from January 2018 to June 2022, with interruptions due to seismic activity (Liu et al. 2020 ) and COVID-19 restrictions that limited field access. Additional sampling was carried out in October 2022 to measure the effects of Hurricane Fiona, that directly impacted the island from September 16–19. Water was collected from ten-2.5 m depth- monitoring wells installed in 2014 by the Processes and Function Laboratory of Tropical Ecosystems of the University of Puerto Rico (Ecolab) (Fig. 1 C). Wells were distributed in three parallel 200 m transects: a) W1 – W3 nearest to the Malaria Channel (freshwater input), b) W4, W5, W6 and W9 (in the center), and c) W7, W8 and W10 farthest from the Malaria Channel and nearest the coast (seawater input). Wells four and five are also closest to Juana Matos neighborhood. Well 6 was damaged in 2018 and re-installed in 2021.Water samples were collected from two depths: phreatic level and at 2.5 m depth). Phreatic level is influenced by both in-situ precipitation as well as inflow from the Malaria and Juana Matos channels. Water collected at 2.5 m depth, on the other hand, is heavily influenced by marine sub-surface inflow from the San Juan Bay Estuary. Phreatic level was recorded on site prior sampling collection. After collection, samples were stored in refrigerated conditions in the field and stored at 4°C in the laboratory until analysis. Precipitation and temperature data was retrieved from a U.S. National Oceanic & Atmospheric Administration meteorological station (RQC00669415), part of the micro-basin, located 3.5km away from the wetland in the municipality of Toa Baja ( https://www.ncei.noaa.gov/cdo-web/ ). Sample analysis Refrigerated water samples were allowed to reach room temperature before analyses. Salinity was measured using an Eco Sense EC300A conductivity sensor. For ionic analyses, samples were filtered in two steps: 1) Büchner funnel vacuum filtration and 2) membrane filtration using a 25 mm syringe 0.2µm PTFE filter (John and Reischl 1978 ; Levy and Jornitz 2006 ). Ten milliliters (10mL) of filtered samples were feed into the instrument to determine the concentrations of Chloride (Cl − ) and Sulfate (SO 4 2− ), sodium (Na + ), magnesium (Mg 2+ ), calcium (Ca 2+ ) and potassium (K + ). Samples from 2018–2019 collections were analyzed on a Metrohm’s 930 Ion Chromatographer Flex while samples from 2020–2022 were analyzed using the Dionex ICS-1000 Ion Chromatography System (ICS-1000) to determine ionic concentrations. The Magnesium/Calcium ratio (mol/mol) was used as a marine/terrestrial connectivity indicator: Mg 2+ is marine derived while Ca 2+ in the study area derives from the calcareous bedrock of the basin and the allochthonous infill of the soil (Giusti and Bennett 1976 ). Sodium/Potassium ratio (mol/mol) was used as an evapotranspiration indicator since evaporated briny water have high concentrations of ions, particularly Na (Rhamdani et al. 2019 ). Statistical analysis Statistical analyses were carried out using SAS JMP© Pro 17. One-Way ANOVA tests and Tukey-Kramer HSD range tests were performed to determine significant differences in precipitation, phreatic level, salinity, Mg/Ca and Na/K ratios, and ionic concentrations among wells, between depths, among dates and among years. Contour plots for salinity were computed using an interpolation of the data based on Delauney triangulation. Arc GIS Pro was employed to compile, edit, classify, analyze and extract geospatial features and attributes necessary for generating a composite layer integrating multiple datasets. These included: 1) georeferenced wells locations with corresponding salinity values for both the wettest and driest recorded dates classified according to Cowardin et al. ( 1979 ) into oligohaline (0.5–5 ppt), mesohaline (5–18 ppt), and polyhaline (18–30 ppt) conditions; 2) U.S. Department of Agriculture (USDA, 2023) soil type layers for the study area, specifically “Saladar muck” (Sm) and “Martín Peña” (Mp) soils; 3 a Digital Terrain Model (DTM) layer derived from post-Hurricane Maria LiDAR data (OCM Partners, 2025 ), used to delineate micro-elevation gradients within the study site, and 4) A georeferenced historical photomosaic from 1930 was utilized to identify natural drainage channels that existed prior to the extensive urban expansion of the 1940s (Puerto Rico Georeferencing Initiative, 2025 ). ArcGIS Pro was further utilized to generate a land surface runoff direction map and conduct hot spot analysis to identify areas of statistically significant spatial clustering. Results Climate variability Annual precipitation during the study period ranged from 1260–2055 mm: 2018 was the wettest, while the driest was 2019. Monthly precipitation ranged from 12.2–318 mm. According to the Köppen-Geiger climate classification (Köppen 1936 ; Peel et al. 2007 ) the driest months during the sampling period were April 2019 (12.2 mm), May 2020 (16.7 mm), March 2018 (37.6 mm), January 2019 (56.39 mm) and May 2021 (58.93 mm). The wettest months were February 2022 (318 mm), May 2018 (318.5 mm), July 2020 (282.5 mm), August 2018 (259.6 mm) and September 2021 (256.5 mm) (Fig. 2). No statistically significant differences in precipitation were detected among years (p = 0.1898) or among months within years (p = 0.43), likely due to high interannual climate variability Relevant atmospheric phenomena occurred during the study period, including tropical storms Isaias and Laura in July and August 2020, and Hurricane Fiona in September 2022. These storms, particularly Tropical Storm Isaias resulted in elevated precipitation and widespread flooding across Puerto Rico (Beven 2021 ). Precipitation patterns during the study did not exhibit the typical bimodal distribution characteristic of the Caribbean Region (Fig. 2). Instead, some years, such as 2018, 2021 and 2022, showed three to four precipitation peaks indicating substantial rainfall events in the study area (Fig. 3 B). This variability influenced the duration and timing of dry and wet periods. For example, in February 2022, a month typically associated with dry conditions, a cold front from the northwest brought over 102 mm of precipitation, to the municipality of Cataño, exceeding historical records for February island-wide (Gelpi-Pagan, 2022) and highlighting the pronounced interannual variability in weather conditions at the site. Phreatic level The wetland phreatic level was predominantly superficial (-12.9 ± 21.4 cm), with periods of flooding and low water table conditions (Fig. 4 A). Water levels fluctuated from − 78 cm below the surface (BS) to 47.5 cm above the surface (AS) throughout the sampling period. Significant spatiotemporal variability in phreatic level was observed among wells (p-value < 0.01). Well 4, located 895 m from the bay, had phreatic levels ranging from − 78 BS to 19.5 cm AS. In contrast well 3, situated 985 m from the bay and closest to the Malaria Channel, exhibited levels from − 46 BS to 47.5 cm AS, reflecting the influence of freshwater inputs from the basin. Water accumulation and phreatic level was also influenced by surface runoff direction, with water flowing towards well 3, 2 and 9, and away from wells 4 and 7 (Fig. 5). Salinity, marine/terrestrial connectivity, and ionic concentrations The sampling area exhibited a three-dimensional mosaic defined by location, phreatic depth, and time, with salinity conditions raging from oligohaline (0.5–5 ppt) to polyhaline (18–30 ppt). Observed salinity values spanned from 0.3 to 31.3 ppt (Table 1 ), encompassing zones with freshwater conditions (0–0.4 ppt) as well as areas that reached euhaline levels (30–40 ppt). There were statistically significant differences in salinity among wells (p-value < 0.01), among sampling years (p-value < 0.01), among sampling dates (p-value < 0.01) and between depths (p-value < 0.01) (Figs. 6 and 7 ). Oligohaline conditions were observed across the entire sampling site at the phreatic level in January and May 2018, September and November 2020 and October 2022. These conditions were associated with atmospheric disturbances and significant precipitation events. In general, mesohaline to polyhaline conditions were recorded at deeper depths (2.5 m depth), which is consistent with subsurface marine water intrusion from San Juan Bay. During dry months and consecutive dry periods, the salinity ranged from mesohaline to polyhaline at both depths. Monitoring wells near to the Malaria channel (wells 1–3) exhibited lower salinities, ranging from 0.1 ppt (during wetter months) to 17.7 ppt (during drier months) at phreatic level and 0.5 ppt to 25.8 ppt at deeper depths. Monitoring wells 4 and 5 showed the highest salinity at the phreatic level, 25.6 ppt and 31.3ppt respectively (indicative of polyhaline and euhaline conditions). At deeper depths, wells 5 and 10 exhibited the highest salinities at deeper depths, 31.3 ppt and 33 ppt respectively (euhaline conditions). Table 1 Monitoring wells mean salinity and ranges through the 2018–2022 sampling period. Well Distance from the coast (m) Mean salinity at phreatic level (ppt) Range (ppt) n Mean salinity at 2.5 m depth (ppt) Range (ppt) n 1 995 7.0 ± 4.0 1.3–17.1 47 8.0 ± 3.7 1.0–16.6 44 2 970 2.5 ± 1.1 0.3–6.8 46 3.3 ± 2.5 1.2–17.7 42 3 985 8.9 ± 4.1 0.5–16.1 51 11.1 ± 4.8 0.1–25.8 48 4 895 17.2 ± 8.0 3.2–29.6 47 21.8 ± 6.9 2.4–30.7 44 5 910 16.7 ± 8.7 1.8–31.3 54 21.5 ± 7.9 2.4–31.5 51 6 920 6.6 ± 4.0 1.1–13.1 26 9.2 ± 4.4 1.8–20 25 7 815 10.2 ± 4.7 1.5–17.1 47 13.4 ± 4.0 3.4–24.5 44 8 805 11.8 ± 6.5 1.3–24.6 46 18.6 ± 6.3 4.3–28.1 42 9 870 14.0 ± 6.7 1.3–24.6 47 16.9 ± 5.4 6.3–29.3 44 10 785 11.6 ± 6.8 2.4–24.8 46 21.3 ± 6.6 7.2–33 43 Magnesium/calcium ratios (Mg 2+ /Ca 2+ ) were used to differentiate marine from terrestrial influence (Fig. 8 ). All wells exhibited Mg/Ca ratios below typical seawater (5.4 mol:mol) although temporal variability was evident, with some measurements occasionally exceeding those of sea water. Significant spatiotemporal heterogeneity in Mg/Ca ratios was observed among wells (PL: p-value < 0.01; DD: p-value < 0.01) (Fig. 8 A). There were also significant differences at phreatic level (PL) and deeper depths (DD) for wells 4, 6–10. At phreatic level, monitoring wells 5 and 9 exhibited the highest Mg/Ca from 1.5 to 7.9 mol:mol and 1.6 to 4.2 mol:mol, respectively. At deeper depth, elevated Mg/Ca ratios were observed in four wells, ranked as follows: 10 (2.7–7.9 mol:mol) > 8 (2.1–6.7 mol:mol) > 9 (2.3–4.9 mol:mol) > 5 (1.9–9.2 mol:mol). Water samples collected from well 2, nearest to the Malaria Channel, had the lowest Mg/Ca ratios for both depths, ranging from 0.8 to 3.8 mol:mol (Fig. 8 A). Sodium/Potasium (Na + /K + /) ratios indicated evapotranspiration. These ratios were consistently higher than Mg/Ca ratios, and exceeded the Na/K ratios of seawater collected from San Juan Bay (44.8 ± 4.01). There were statistically significant differences in Na/K ratios among wells (PL: p-value < 0. 01; DD: p-value < 0. 01), with some showing values close to the seawater reference, while others exhibited values nearly twice as high (Fig. 8 B). Notably, wells 2, 6 and 7 recorded the highest Na/K ratio at both phreatic level (PL) and at a deeper depth (DD), while wells 3, 5, 9 and 10 had the lowest. To illustrate the spatiotemporal dynamics found in our study, we compared two representative wells with contrasting Na/K values: well 2 (24.8–156.9), which had the highest ratio at both depths (PL, DD), and well 10(34.4–70.4), which had the lowest. (Fig. 8 B). During dry periods, salt polygons (sodium deposits) were seen on the soil surface of the wetland, particularly in allochthonous areas. Both ratios demonstrated dynamic contrasts: Mg/Ca was consistently higher in well 10 (nearest to the bay), while Na/K was consistently higher in well 2 (closest to the Malaria Channel). Substrate types In Cucharillas’ study site wells, 6, 9 and 10 were installed in organic peat substrates derived from original autochthonous natural plant decomposition, while wells 1, 2,3, 4, 5, 7 and 8 and areas with anthropogenic allochthonous mineral infills (10–40 cm depth), embedded within the organic matrix (Fig. 9 ). Discussion Through this research, we determined how the combined interactions of local weather variability, marine-terrestrial connectivity and/or anthropic modifications influenced the spatiotemporal hydrological dynamics and ionic concentrations in a Caribbean tropical coastal wetland. The ecohydrology of Ciénaga Las Cucharillas is governed by a complex interplay of factors. While climatic variability and marine-terrestrial connectivity are the primary drivers, additional parameters including basin’s geology, soil type, substrate physicochemical properties, microtopography and water flow direction, contribute shaping about a tridimensional spatiotemporal hydrological mosaic in Ciénaga Las Cucharillas. Rainfall variability was primarily driven by differences in intensity, persistence and periodicity of events. This was the case of May 2018 where monthly precipitation was influenced by localized intense rainfall associated with the passing of atmospheric throughs and tropical waves. February 2022, the month with the highest recorded precipitation, also exhibited similar extreme conditions driven by intense rainfall associated with a cold front (Gelpi-Pagan, 2022). This event was unusual on the island, as February is typically associated with drier conditions. These deviations are increasingly common as climate change intensifies teleconnection effects (Vega et al. 2020 ; Mann, 2021 ; Gould et al. 2022 ). Destouches et al. ( 2025 ) found that, for the period of 1985 to 2015, warming sea surface temperatures (SSTs) were associated with an increase in daily precipitation intensity and number of rainy days for the island of Puerto Rico. From 2020 to 2022 there was a higher prevalence of significant rainfall events, such as tropical depressions, storms and hurricanes. Particularly, Tropical Storms Isaias and Laura occurred on July and August 2020, while Hurricane Fiona impacted the island on September 2022. These major events were associated with the highly active 2020 North Atlantic hurricane season, driven by a combination of atmospheric and oceanic conditions, including low vertical wind shear, below-average sea level pressures, elevated sea surface temperatures (SSTs), and positive phases of the Atlantic Multidecadal Oscillation (AMO) and the Atlantic Meridional Mode (AMM) (Beven 2021 ). These conditions coincided with 2020–2023 La Niña “triple dip” phenomenon, characterized by prolonged cooler-than-average sea surface temperatures on the eastern Pacific Ocean. This extended La Niña “triple dip” event was unprecedented since the 1950s and challenges earlier projections from the IPCC models that anticipated El Niño conditions. Emerging evidence suggests that La Niña-like conditions may become more frequent in the future (Hernández-Ayala and Méndez-Tejeda 2022 ; Jiang et al. 2023 ; Iwakiri et al. 2023 ). Local climate regulated temporal variations in salinity and freshwater inputs, as well as phreatic levels. Sustained rainfall and/or extreme precipitation events enhanced the subsurface freshwater flow from the Malaria Channel into the wetland substrate, thereby increasing phreatic water levels and soil water storage at the site. These weather extremes homogenized the wetland’s hydrological conditions as seen on September 2020 were the effects of both Tropical Storms Isaias and Laura, resulted in oligohaline conditions and phreatic levels above soil surface across the entire sampling area. Similar patterns were observed after Hurricane Fiona on 2022, and in early January 2018, after the prolonged flooding associated with the effects of Hurricane Maria on September 2017. Strong capillary action may have produced a substantial capillary fringe facilitating rising water tables rising in response to intense rainfall events (Rosenberry and Winter, 1997 ). A comparable event was documented in Galveston Bay, United States after Hurricane Harvey (Cat 4), which caused prolonged high-water levels and sharp decreases in salinity (Du et al. 2019 ). Dry conditions had the opposite effects: reduced phreatic levels and elevated salinities suggesting an evaporative concentration effect of solutes due to high temperatures and humidity. Spatial hydrological variability was observed throughout the study area. Usually, such variability is characterized by a landward-decreasing salinity gradient from hypersaline to freshwater conditions (Cowardin 1979). This has been reported for multiple decades as seen in numerous studies (Table 2 ). However, in this study, salinity did not conform the expected gradient, but instead was a hydrological mosaic that stemmed from the deep sub-surface terrestrial-marine connectivity. Salinity hotspots analysis indicated that this pattern is not solely the result of the permanent closure from tidal flow at the mouth of the Malaria Channel but also influenced by the presence of natural subsurface channels connecting wells 4 and 5 to the coastline (Fig. 10 ). These findings indicate that the wetland’s original hydrogeological structure continues to shape its ecohydrological dynamics, and highlights the importance of monitoring of groundwater salinity alongside phreatic salinity, as the latter alone may not adequately capture deep-subsurface marine intrusion. Table 2 Salinity gradients for tropical and temperate wetlands around the world. Coastal wetland type Salinity range Location Palustrine-estuarine coastal wetland Salinity did not follow the expected gradient and instead was heterogenous resulting in a hydrological mosaic. Ciénaga las Cucharillas Reserve, Puerto Rico Tidal freshwater lacustrine temperate wetland Salinity ranged from 2–5 ppt due to strong saline intrusion. There were salinity gradients inland. Lake Wailoha, South Island, New Zealand ( Schallenberg et al. 2003 ) Estuarine temperate wetland Salinity ranged from 0–12 ppt. There was a salinity gradient; it decreased upstream the Pamlico River. Floodplains of Jacks Creek, Panico River estuary, North Carolina, United States (Brinson et al. 1985 ) Estuarine humid sub-tropical natural and managed wetlands Natural wetlands ranged from oligohaline to euhaline salinities inland towards the sea. Managed wetlands had gates for water management and control that divided fresh and saltwater. Hobcaw Barony wetlands, South Carolina, United States (Wang et al. 2016 ) Tropical wetland Intertidal and groundwater salinity was higher near the sea (> 10.8 ppt) and lower inland (5 ppt). El Castaño Wetlands System, Chiapas, México (Rincón-Pérez et al. 2020 ) Tropical wetland Salinity goes from 1.5–19 ppt in riverine mangrove areas to 23.9–35.7 ppt in the fringe mangrove area. San Andres Island, Colombia (Caribbean) (Urrego et al. 2009 ) The wetland’s geomorphology, substrate composition, and water flow also contributed to the hydrological dynamics of the wetland. Mg/Ca ratios in Ciénaga Las Cucharillas were higher than those reported for the coastal wetland of Selçuk Plain in Turkey, where both elements are primarily derived from weathering of Ca-Mg silicates from its basin’s geology (Somay and Gemici 2009 ). This highlights that although the weathering of Cucharilla’s bedrock (Monroe 1980 ) and allochthonous infill may contribute to Mg and Ca concentrations, marine inputs play a dominant role. The elevated Na/K ratios in the wetland water (all exceeding 50 mmol:mmol) compared to the ratio of freshwater from the microbasin (25.1 ± 4.3 mmol:mmol), are likely a result of a combination of sea water intrusion and cation exchange involving NaCl solutions (Somay and Filiz 2003 ). This reaffirms the relevant marine influence of magnesium and sodium across the study area, especially in zones closes to the coast or associated with subsurface flow paths potentially connecting to the San Juan Bay. Evapotranspiration and limited leaching capacity in the wetland soils may have also contributed to elevated Na concentrations as seen in the Pantanal wetland in South America (Furquim, and Vidoca 2021 ). Substrate composition played a significant role in regulating phreatic level and salinity dynamics within the Ciénaga Las Cucharillas Reserve. Peats soils are known for their high-water retention capacity. When compared to clay-peat mixtures, peats could retain up to 41% of the soil moisture content 53 days after soils are dried, a significantly higher percentage than clay-peat mixtures (10–15%) (Feustel & Byers, 1936 ). Phreatic levels were highest at well 3, located in a peat dominated substrate, and lowest at well 4, which was underlain by mineral substrate. Land-surface runoff direction corresponded with these patterns, indicating that a combination of substrate type, hydrological inputs, and topography contribute to the complex hydrological dynamics of Ciénaga Las Cucharillas. Substrate composition also played a role in Na/K differences along the sampling site. Well 2, underlain by allochthonous mineral infill exhibited higher Na/K ratios than well 10, characterized by a substrate richer in peat. Compared to peats, clay soils possess finer particles and larger surface areas, enhancing the adsorption of dissolved salts onto the substrate. These properties have been associated with elevated cation retention in both agricultural fields (Taylor and Krüger 2019 ), and wetlands (Chairawiwut et al. 2016 ). Substrate differences, in combination with evapotranspiration, ionic mobility and cation exchange, played a role in the ionic dynamics along the study site. These results support the classification of Ciénaga las Cucharillas as a palustrine-estuarine wetland. The absence of direct marine inflow through the Malaria Channel, coupled with deep subsurface saline intrusion driven by tidal forces and marine water inputs via the natural subsurface channels, facilitates the mixing of freshwater and seawater characteristic of coastal wetlands. This mixing resulted in a mesohaline to polyhaline conditions in the estuarine zone of the wetland mosaic, which support the presence of mangrove species such as white mangrove ( Laguncularia racemosa ) and black mangrove ( Avicennia germinans ) throughout the ecosystem (Hernández et al. 2021 ). In contrast, areas dominated by surficial freshwater inputs maintain oligohaline conditions, supporting their classification as palustrine environments within the broader mosaic. These dynamics underscore the importance of adopting an ecohydrological perspective in wetland assessment and restoration. Coastal wetland’s location at the terrestrial–marine interface makes them vulnerable to increasing rates of sea-level rise and climate variability. Precipitation models predict a decline in rainfall across tropical and subtropical regions (Solecky et al. 2024), especially on the eastern side of the Caribbean and Puerto Rico (PRCC Council, 2014). The combined effects of sea level rise, which will increase saline intrusion, reduced precipitation and greater drought frequency and intensity, are expected to alter not only wetlands hydrology, but also its vegetation and its distribution. In heavily modified systems, hydrological connectivity may no longer be apparent at the surface but persists belowground. A comprehensive 5-year study, like the one carried out in Cucharillas, is essential for understanding hydrological dynamics and determine valuable management and restoration strategies that takes into account subsurface flow dynamics and legacy geomorphology, that can aid vegetative species adapt to future climates. Conclusions The present study provides a valuable framework for modeling impacted urban coastal wetlands. It assessed the influence of local weather and marine-terrestrial connectivity on wetland hydrological conditions through a 5-year-long monitoring of salinity, phreatic level, precipitation, and ionic concentrations, all which directly influence hydro-regime dynamics. While climatic variability and marine-terrestrial connectivity sustains a tridimensional hydrological spatiotemporal palustrine-estuarine mosaic in Ciénaga Las Cucharillas, the basin’s geology, soil type, substrate physicochemical characteristics, microtopography and water flow direction also play an active role in shaping the wetland’s hydrological dynamics. Belowground flow paths influenced salinity through deep subsurface marine inputs, underscoring the importance of considering both the present and historical states of the system when studying, managing, and rehabilitating. Deep subsurface hydrodynamic processes in enclosed coastal wetlands lacking direct marine inflow are increasingly becoming the norm rather than the exception, due to the extensive hydrological modifications caused by the expanding gray infrastructure. These modifications can result in marked vertical differences in hydrological throughout the wetland water profile, which must be taken into account in the development of effective coastal wetland management and rehabilitation plans. Future monitoring and management strategies should include groundwater salinity measurements, as the results indicate that is equally as important as phreatic salinity which can obscure evidence of deep-subsurface marine intrusion. The use of the natural abundance of stable isotopes of water (δD and δ 18 O) to establish water source mixing dynamics and a more thorough plant-water-soil study can help a better understanding of Ciénaga Las Cucharilla’s ecohydrology. Declarations Competing Interests “The authors have no relevant financial or non-financial interests to disclose.” Funding This research was funded by NSF CREST – Center for Innovation Research and Education in Environmental Nanotechnology (CIRE2N) HRD-1736093 and NSF HRD-1806129. Author Contribution All authors contributed to the study conceptualization, design, and realization. SPP: data curation, formal analysis and methodology, data analysis, geospatial analysis, writing- original draft, writing- review and editing. EC: Maps, formal analysis and methodology, writing- review and editing. GOR: formal analysis and methodology, sample collection and analysis, geospatial analysis, writing- review and editing. EHF: formal analysis and methodology, sample collection and analysis, writing- review and editing. LD: sample collection and analysis. DO: sample collection and analysis. Acknowledgments The authors would like to acknowledge the Center for Applied Tropical Ecology and Conservation (CATEC) for technical assistance. Undergraduate students and volunteers from the Process and Functions Ecosystem Lab who assisted with field and lab work. We also thank Dr. Elvis Torres, Laboratory of Atmospheric Chemistry and Atmospheric Particulate and Marylene Fox of the Laboratory of Tropical Limnology for ionic concentration analysis. Lastly, we thank El Corredor Del Yaguazo Inc., including Pedro Carrion and personnel who assisted with field work, and the University of Puerto Rico’s Environmental Department GIS Lab. References Anderson HR (1976) Ground Water in the San Juan Metropolitan Area, Puerto Rico: U.S. Geological Survey Water-Resources Investigations Report #41–75. USGS Publications Warehouse. https://doi.org/10.3133/wri7541 . Accessed June 10 2019 Ashby SA, Taylor MA, Chen AA Statistical models for predicting rainfall in the Caribbean. Theor Appl Climatol 82: 65–80. https://doi.org/10.1007/s00704-004-0118-8 Baldwin J (2000) Tourism development, wetland degradation and beach erosion in Antigua, West Indies. Tour Geogr 2(2):193–218. https://doi.org/10.1080/14616680050027897 Beven JL (2021) The 2020 Atlantic Hurricane Season: The Most Active Season on Record. Weatherwise 74(5):33–43. https://doi.org/10.1080/00431672.2021.1953905 Botero L, Mancena-Pineda E (1996) Síntesis de los cambios antrópicos ocurridos en los últimos cuarenta años en la Ciénaga Grande de Santa Marta, Colombia. Revista Acad Colomb Ci Exact 20(78):465–474. https://doi.org/10.18257/raccefyn.20(78).1996.3055 Branoff B, Cuevas E, Hernández E (2018) Assessment of urban coastal wetlands vulnerability in Puerto Rico. https://www.drna.pr.gov/wp-content/uploads/2018/09/FEMA-Wetlands-Report.pdf Brinson MM, Bradshaw HD, Jones MN (1985) Transitions in forested wetlands along gradients of salinity and hydroperiod. J Elisha Mitchell Sci Soc, 76–94. https://www.jstor.org/stable/24333326 Chairawiwut W, McMartin DW, Azam S (2016) Salts removal from synthetic solution-potash brine by non-planted constructed wetlands. Water 8(4):113. https://doi.org/10.1016/S0043-1354(02)00484-0 Cowardin LM, Carter V, Golet FC, Laroe ET (1979) Classification of wetlands and deepwater habitats of the United States: U.S. Fish and Wildlife Service Report FWS/OBS – 79/31, 131 p Crook DA, Lowe WH, Allendorf FW, Erős T, Finn DS, Gillanders BM, Hadwen WL, Harrod C, Hermoso V, Jennings S, Kilada RW, Nagelkerken I, Hansen MM, Page TJ, Riginos C, Fry B, Hughes JM (2015) Human effects on ecological connectivity in aquatic ecosystems: Integrating scientific approaches to support management and mitigation. Sci Total Environ 534:52–64. https://doi.org/10.1016/j.scitotenv.2015.04.034 Destouches C, Diedhiou A, Anquetin S, Hingray B, Pierre A, Boisson D, Joseph A (2025) Changes in extreme precipitation patterns over the Greater Antilles and teleconnection with large-scale sea surface temperature. Earth Syst Dyn 16(2):497–512. https://doi.org/10.5194/esd-16-497-2025 Du J, Park K, Dellapenna TM, Clay JM (2019) Dramatic hydrodynamic and sedimentary responses in Galveston Bay and adjacent inner shelf to Hurricane Harvey. Sci Total Environ 653:554–564. https://doi.org/10.1016/j.scitotenv.2018.10.403 Ellison AM, Farnsworth EJ (1996) Anthropogenic Disturbance of Caribbean Mangrove Ecosystems: Past Impacts, Present Trends, and Future Predictions. Biotropica 28(4):549–565. https://doi.org/10.2307/2389096 Feustel IC, Byers HG (1936) The comparative moisture-absorbing and moisture-retaining capacities of peat and soil mixtures (No. 532). US Department of Agriculture Furquim SAC, Vidoca TT (2021) Salt-Affected Soils of Pantanal Wetland. In: Taleisnik E, Lavado RS (eds) Saline and Alkaline Soils in Latin America. Springer, Cham Gelpí-Pagán J (2022) Tu tiempo: febrero 2022 rompe récord de lluvia reportada. Noticentro: Tu Tiempo. https://www.wapa.tv/noticias/tiempo/tu-tiempo--febrero-2022-rompe-record-de-lluvia-reportada_20131122526189.html Accessed 27 October 2023 Giannini A, Kushnir Y, Cane MA (2000) Interannual variability of Caribbean rainfall, ENSO, and the Atlantic Ocean. J Clim 13(2): 297–311. https://doi.org/10.1175/15200442 (2000)013%3C0297:IVOCRE%3E2.0.CO;2 Giusti EV, Bennett GD (1976) Water resources of the north coast limestone area. U.S. Geological Survey Water-Resources Investigation Report 75 – 42, Puerto Rico Gould WA, Castro-Prieto J, Álvarez-Berríos NL (2020) Climate change and biodiversity conservation in the Caribbean islands. In Encyclopedia of the World’s Biomes, First edition; Goldstein M.I., DellaSala D.A., Eds; Elsevier Academic Press: Amsterdam, Netherlands, 2020; vol 1, pp. 114–125 Gould WA, Dias E, Terando A, Jury M, Bowden J, Chardon P, Meléndez-Oyola M, Morell J (2022) Puerto Rico’s state of the climate 2014–2021. Puerto Rico Climate Change Council. https://pubs.usgs.gov/publication/70249920 Accessed 15 February 2025 Herbert ER, Boon P, Burgin AJ, Neubauer SC, Franklin RB, Ardón M, Hopfensperger KN, Lamers LPM, Gell P (2015) A global perspective on wetland salinization: ecological consequences of a growing threat to freshwater wetlands. Ecosphere 6(10):206. http://dx.doi.org/10.1890/ES14-00534.1 Hernández E, Cuevas E, Pinto-Pacheco S, Ortíz-Ramírez G (2021) You Can Bend Me but Can’t Break Me: Vegetation Regeneration After Hurricane María Passed Over an Urban Coastal Wetland in Northeastern Puerto Rico. Front Glob Change 4:752328. https://doi.org/10.3389/ffgc.2021.752328 Hernández-Ayala JJ, Méndez-Tejeda R (2022) The Extremely Active 2020 Hurricane Season in the North Atlantic and Its Relation to Climate Variability and Change. Atmosphere 13(12): 1945. https://doi.org/10.3390/atmos13121945 Iwakiri T, Imada Y, Takaya Y, Kataoka T, Tatebe H, Watanabe M (2023) Triple-Dip La Niña in 2020–23: North Pacific atmosphere drives 2nd year La Niña. Geophys Res Lett 50(22): e2023GL105763. https://doi.org/10.1029/2023GL105763 Jiang S, Zhu C, Hu ZZ, Jiang N, Zheng F (2023) Triple-dip La Niña in 2020–23: understanding the role of the annual cycle in tropical Pacific SST. Environ Res Lett 18(8):084002. http://dx.doi.org/10.1088/1748-9326/ace274 John W, Reischl G (1978) Measurements of the filtration efficiencies of selected filter types. Atmos. Environ. (1967). 12(10): 2015–2019. https://doi.org/10.1016/0004-6981(78)90139-7 Kennaway T, Helmer EH (2007) The forest types and ages cleared for land development in Puerto Rico. GISci Remote Sens 44(4):356–382. https://doi.org/10.2747/1548-1603.44.4.356 Köppen W (1936) Das geographische System der Klimate, Handbuch der Klimatologie [The Geographical System of the Climate, Handbook of Climatology], vol 1. Borntraeger, Berlin Levy RV, Jornitz MW (2006) Types of Filtration. In: Jornitz MW (ed) Sterile Filtration. Advances in Biochemical Engineering, vol 98. Springer, Berlin, Heidelberg Liu C, Lay T, Wang Z, Xiong X (2020) Rupture process of the 7 January 2020, MW 6.4 Puerto Rico earthquake. Geophys Res Lett 47(12). https://doi.org/10.1029/2020GL087718 . e2020GL087718 Lugo AE, Brown S, Brinson MM (1988) Forested wetlands in freshwater and salt-water environments. Limnol Oceanogr 33(4part2):894–909. https://doi.org/10.4319/lo.1988.33.4part2.0894 Lugo AE, Ramos-González OM, Rodríguez-Pedraza C (2011) The Río Piedras watershed and its surrounding environment. FS-980. U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry, p 46 Mann ME (2021) Beyond the hockey stick: Climate lessons from the Common Era. Proc Natl Acad Sci USA 118(39):e2112797118. https://doi.org/10.1073/pnas.2112797118 Maitland DO, Richter K, Raj RP, Bonaduce A, Nisancioglu KH, Taylor MA, Stephenson TS (2024) Determining sea-level rise in the Caribbean: A shift from temperature to mass control. Sci Rep 14:10387. https://doi.org/10.1038/s41598-024-60201-8 Manzoni S, Maneas G, Scaini A, Psiloglou BE, Destouni G, Lyon SW (2020) Understanding coastal wetland conditions and futures by closing their hydrologic balance: the case of the Gialova lagoon. Greece Hydrol Earth Syst 24:3557–3571. https://doi.org/10.5194/hess-24-3557-2020 Martinuzzi S, Gould WA, Lugo AE, Medina E (2009) Conversion and recovery of Puerto Rican mangroves: 200 years of changes. Ecol Manag 257:75–84. https://doi.org/10.1016/j.foreco.2008.08.037 Michener WK, Blood ER, Bildstein KL, Brinson MM, Gardner LR (1997) Climate change, hurricanes and tropical storms, and rising sea level in coastal wetlands. Ecol Appl 7(3): 770–801. https://doi.org/10.1890/1051-0761 (1997)007[0770:CCHATS]2.0.CO;2 Monroe WH (1980) Some Tropical Landforms of Puerto Rico. USGS Publications Warehouse. https://doi.org/10.3133/pp1159 . Accessed September 15 2019 Morris JT, Sundareshwar PV, Nietch CT, Kjerfve B, Cahoon DR (2002) Responses of coastal wetlands to rising sea level. Ecol 83:2869–2877. https://doi.org/10.1890/0012-9658(2002)083 [2869:ROCWTR]2.0.CO;2 Nuttle WK (2002) Is ecohydrology one idea or many? Hydrol Sci J 47(5):805–807. https://doi.org/10.1080/02626660209492983 Partners OCM (2025) : 2018 USGS Lidar DEM: Post Hurricane Maria - Puerto Rico from 2010-06-15 to 2010-08-15. NOAA National Centers for Environmental Information, https://www.fisheries.noaa.gov/inport/item/60105 Ortiz-Ramírez G, Hernández E, Pinto-Pacheco S, Cuevas E (2024) The Dynamics of Soil Mesofauna Communities in a Tropical Urban Coastal Wetland: Responses to Spatiotemporal Fluctuations in Phreatic Level and Salinity. Arthropoda 2:1–27. https://www.preprints.org/manuscript/202309.1767 Osland MJ, Enwright NM, Day RH, Gabler CA, Stagg CL, Grace JB (2016) Beyond just sea-level rise: considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change. Glob Change Biol 22(1):1–11. https://doi.org/10.1111/gcb.13084 Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydro Earth Syst Sci 11:1633–1644. https://doi.org/10.5194/hess-11-1633-2007 Puerto Rico Georeferencing Initiative (2025) Puerto Rico 1930 georeferenced photomosaic: Technical documentation [PDF]. Retrieved from https://prgeoref.weebly.com/uploads/8/6/3/9/86396506/puerto_rico_1930_georeferenced.pdf Pumarada-O'Neill L (1991) Los puentes históricos de Puerto Rico. Centro de Investigación y Desarrollo. Recinto de Mayagüez, Universidad de Puerto Rico Rhamdani AR, Lalasari LH, Sulistyono E, Setiawan I, Firdiyono F (2019) The Effect of Slow Evaporation on Ca, Na, K, and Mg Concentration in Natural Brine from Ciseeng, West Java. IOP Conf Ser Mater Sci Eng 541(1):012040. http://dx.doi.org/10.1088/1757-899X/541/1/012040 Rincón-Pérez M, Infante-Mata D, Moreno-Casasola P, Hernández Alarcón ME, Barba Macías E, García-Alfaro JR (2020) Patrones de distribución y estructura de la vegetación en el gradiente de humedales costeros El Castaño, Chiapas, México. Rev Biol Trop 68(1):242–259. http://dx.doi.org/10.15517/rbt.v68i1.37616 Rosenberry DO, Winter TC (1997) Dynamics of water-table fluctuations in an upland between two prairie-pothole wetlands in North Dakota. J Hydrol 191(1–4):266–289. https://doi.org/10.1016/S0022-1694(96)03050-8 Salazar-Ortiz M, Cuevas E (2017) Land use and land management during the past century determine mangrove dynamics in northwestern Puerto Rico: the case of the Maracayo mangrove. J Environ Eng Sci B 6:593–623. https://doi.org/10.17265/2162-5263/2017.12.002 Schallenberg M, Hall CJ, Burns CW (2003) Consequences of climate-induced salinity increases on zooplankton abundance and diversity in coastal lakes. Mar Ecol Prog Ser 251:181–189. https://doi.org/10.3354/meps Seguinot-Baborsa J (1983) Coastal Modification and Land Transformation In The San Juan Bay Area: Puerto Rico. Dissertation, Louisiana State University and Agricultural and Mechanical College https://uprrp.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/coastal-modification-land-transformation-san-juan/docview/303167572/se-2 Solecki W, Roberts D, Seto KC (2024) Strategies to improve the impact of the IPCC Special Report on Climate Change and Cities. Nat Clim Change 14(7):685–691. https://doi.org/10.1038/s41558-024-02060-9 Somay MA, Filiz S (2003) Hydrology, hydrogeology and hydrochemistry of wetlands: a case study in Izmir Bird Paradise, Turkey. Environ Geol 43:825–835. https://doi.org/10.1007/s00254-002-0697-6 Somay MA, Gemici Ü (2009) Assessment of the salinization process at the coastal area with hydrogeochemical tools and geographical information systems (GIS): Selçuk plain, Izmir, Turkey. Water Air Soil Poll 201:55–74. https://doi.org/10.1007/s11270-008-9927-1 Taylor M, Krüger N (2019) Changes in salinity of a clay soil after a short-term salt water flood event. Geoderma Reg 19:e00239. https://doi.org/10.1016/j.geodrs.2019.e00239 Tiner RW (2018) Hydrology of coastal wetlands. The wetland book I: Structure and function, management and methods. Springer, Netherlands, pp 1–14 Urrego LE, Polanía J, Buitrago MF, Cuartas LF, Lema A (2009) Distribution of mangroves along environmental gradients on San Andres Island (Colombian Caribbean). Bull Mar Sci 85(1):27–43. https://www.ingentaconnect.com/content/umrsmas/bullmar/2009/00000085/00000001/art00003 Vega MJ, Alvarez-Silva O, Restrepo JC, Ortiz JC, Otero LJ (2020) Interannual variability of wave climate in the Caribbean Sea. Ocean Dyn 70:965–976. https://doi.org/10.1007/s10236-020-01377-1 Vilardy SP, González JA, Martín-López B, Montes C (2011) Relationships between hydrological regime and ecosystem services supply in a Caribbean coastal wetland: a social-ecological approach. Hydrol Sci J 56(8):1423–1435. https://doi.org/10.1080/02626667.2011.631497 Webb RM, Gómez-Gómez F (1998) Synoptic survey of water quality and bottom sediments, San Juan Bay estuary system, Puerto Rico, December 1994-July 1995. Water Resour Investigations Rep 974144. https://doi.org/10.3133/wri974144 Wang JJ, Jiao Y, Rhew RC, Chow AT (2016) Haloform formation in coastal wetlands along a salinity gradient at South Carolina, United States. Environ Chem 13(4):745–756. https://doi.org/10.1071/EN15145 White E, Kaplan D (2017) Restore or retreat? saltwater intrusion and water management in coastal wetlands. Ecosyst Health Sustain 2017 3(1):e01258. https://doi.org/10.1002/ehs2.1258 Williams CO, Lowrance R, Bosch DD, Williams JR, Benham E, Dieppa A, Hubbard R, Ma E, Potter T, Sotomayor D, Steglich EM, Strickland T, Williams RG (2013) Hydrology and water quality of a field and riparian buffer adjacent to a mangrove wetland in Jobos Bay watershed, Puerto Rico. Ecol Eng 56:60–68. https://doi.org/10.1016/j.ecoleng.2012.09.005 United States Department of Agriculture (USDA) (2023) NRCS Web Soils Survey. Ciénaga las Cucharillas. Available online: https://websoilsurvey.usda.gov/ Accessed 16 June 2023 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 11 Jul, 2025 Editor invited by journal 04 Jul, 2025 Editor assigned by journal 24 Jun, 2025 First submitted to journal 23 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6933337","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483828502,"identity":"3c9743af-3b26-4b70-97bc-dff6103e3c58","order_by":0,"name":"Solimar Pinto-Pacheco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYFAC5oYDEBpEVEDEJPBrYYRrYWxgOEOkFgSLsY0ILfLtjY2HbtTcYzBn5z3+4OO8w3nmDcwHb/Pg0WJw5mDD4ZxjxQyWzXyJjTO3HS6WOcCWbI1Xi0QiUAtbAoPBYR7DZt5thxNnMPCYSePTIj8DpOUfTMsckBb+b3i1MNwAasltg2lpANvChlcL2C+5fQk8ls08hjNnHEsvlmBmM7acg89h7c2HP+d8S5Az5z9j8OFDjXWeBHvzwxtv8DkMCngMoIwESDIgBiC0jIJRMApGwShAAwCPUUvPAb5cSgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-7004-5262","institution":"University of Puerto Rico Rio Piedras: Universidad de Puerto Rico Recinto de Rio Piedras","correspondingAuthor":true,"prefix":"","firstName":"Solimar","middleName":"","lastName":"Pinto-Pacheco","suffix":""},{"id":483828503,"identity":"aa9b31ff-fc8b-4df1-b6d4-8b81b5ef8b37","order_by":1,"name":"Elix Hernandez-Figueroa","email":"","orcid":"","institution":"University of Puerto Rico Rio Piedras: Universidad de Puerto Rico Recinto de Rio Piedras","correspondingAuthor":false,"prefix":"","firstName":"Elix","middleName":"","lastName":"Hernandez-Figueroa","suffix":""},{"id":483828504,"identity":"34fc8476-fa3c-47df-8ba9-b8b9a907e1db","order_by":2,"name":"Gloria Ortiz-Ramirez","email":"","orcid":"","institution":"University of Puerto Rico Rio Piedras: Universidad de Puerto Rico Recinto de Rio Piedras","correspondingAuthor":false,"prefix":"","firstName":"Gloria","middleName":"","lastName":"Ortiz-Ramirez","suffix":""},{"id":483828505,"identity":"60349175-7c8a-49ea-811a-07447c477d29","order_by":3,"name":"Diego Otero","email":"","orcid":"","institution":"University of Puerto Rico Rio Piedras: Universidad de Puerto Rico Recinto de Rio Piedras","correspondingAuthor":false,"prefix":"","firstName":"Diego","middleName":"","lastName":"Otero","suffix":""},{"id":483828506,"identity":"676daee8-1127-4430-80a7-f2299cd579da","order_by":4,"name":"Larry Diaz","email":"","orcid":"","institution":"University of Puerto Rico Rio Piedras: Universidad de Puerto Rico Recinto de Rio Piedras","correspondingAuthor":false,"prefix":"","firstName":"Larry","middleName":"","lastName":"Diaz","suffix":""},{"id":483828507,"identity":"420709b7-69e3-4714-be5d-efab87aa8bc5","order_by":5,"name":"Elvira Cuevas","email":"","orcid":"","institution":"University of Puerto Rico Rio Piedras: Universidad de Puerto Rico Recinto de Rio Piedras","correspondingAuthor":false,"prefix":"","firstName":"Elvira","middleName":"","lastName":"Cuevas","suffix":""}],"badges":[],"createdAt":"2025-06-19 17:40:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6933337/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6933337/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86769336,"identity":"88922077-bdb8-42e0-a1ae-3619d66f9314","added_by":"auto","created_at":"2025-07-15 11:33:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":313278,"visible":true,"origin":"","legend":"\u003cp\u003eA) Ciénaga Las Cucharillas located in the northwestern side of the San Juan Bay, B) Study area (2.2 ha), and C) Monitoring wells. The wells in the blue transect are closest to the Malaria Channel (freshwater input), while the ones in brown are closest to the coast (marine input). The ones in the yellow transect are in between both water sources.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/b56630d3724cb81ee51eb13a.png"},{"id":86770767,"identity":"af54a763-c485-4b69-849a-1a451335b27b","added_by":"auto","created_at":"2025-07-15 11:41:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":275581,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Rainfall Accumulation from January 2018 to October 2022 (sampling period). Data was retrieved from the Toa Baja, Levittown, PR RQC00669415 weather station of the U.S Department of Commerce and NOAA. Red bars represent driest sampling months, while blue bars represent the wettest. Red line represents Köppen-Geiger climate classification (dry month \u0026lt; 60 mm of rain).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/3aa2feee83d38a22b566cc00.png"},{"id":86769341,"identity":"7c75caa6-7fdc-4dd0-92fb-b4b9090adea0","added_by":"auto","created_at":"2025-07-15 11:33:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128834,"visible":true,"origin":"","legend":"\u003cp\u003eA) Mean ± SE precipitation for a 5-year period in our study area (blue line; average monthly precipitation (1983 – 2004) in the Caribbean (gray line drawn from Angeles et al. (2010)) B) Monthly precipitation for each year during our study period.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/d65436726f1d3cc835f308d6.png"},{"id":86770773,"identity":"96221301-5092-485c-b1e3-6929cdeb87c6","added_by":"auto","created_at":"2025-07-15 11:41:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":474601,"visible":true,"origin":"","legend":"\u003cp\u003eA) Phreatic level by wells throughout our sampling. Letters correspond to Tukey Multiple Comparison Test. B) Phreatic level and monthly precipitation throughout the sampling period. Blue line represents the mean phreatic level for our study site. Red line represents Köppen-Geiger climate classification (dry month \u0026lt; 60 mm of rain)\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/ee07bf15ddc4af6763623dac.png"},{"id":86769334,"identity":"f07ae46f-7a5f-42cc-9c59-610222534930","added_by":"auto","created_at":"2025-07-15 11:33:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":384703,"visible":true,"origin":"","legend":"\u003cp\u003eGeospatial map illustrating land surface runoff direction. Blue circles correspond wells while arrows correspond runoff direction.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/38966da70be14114894ee2a6.png"},{"id":86769342,"identity":"629f83fe-b859-4020-8273-c83d395bd5ee","added_by":"auto","created_at":"2025-07-15 11:33:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":841812,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal and spatial variations of phreatic salinity in our study area. Contrasting conditions were chosen from our study period to show how salinity varies through time.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/2c1ff7f90c7031532b1f8611.png"},{"id":86769340,"identity":"727dde97-8f10-4115-9247-94ce30f184f6","added_by":"auto","created_at":"2025-07-15 11:33:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":830144,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal and spatial variations of deep salinity (2.5 m of depth) in our study area. Contrasting conditions were chosen from our study period to show how salinity varies through time.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/0f4724d44f71a4d6864a1dd1.png"},{"id":86769345,"identity":"4828d18f-2f55-48db-ae29-a57dfa661af4","added_by":"auto","created_at":"2025-07-15 11:33:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":506425,"visible":true,"origin":"","legend":"\u003cp\u003eMagnesium to calcium (Mg/Ca) and Sodium to potassium values for our study area: A) Mg/Ca among wells at both phreatic level (PL) and deeper depths (DD; 2.5 m of depth). B) Na/K among wells at both phreatic level (PL) and deeper depths (DD; 2.5 m of depth). Temporal variations in C) Mg/Ca for well 2 (with the lowest Mg/Ca), d) Mg/Ca for well 10 (the highest Mg/Ca), e) Na/K for well 2 (the highest Na/K) and F) Na/K for well 10 (the lowest Na/K). The blue lines rep-resent the Mg/Ca and Na/K ratios of seawater collected from the coast near our study area.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/ea7d956e257e03bb9d0bb647.png"},{"id":86771326,"identity":"24c08116-ca70-45db-8da5-2737fb80bbe4","added_by":"auto","created_at":"2025-07-15 11:49:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":441617,"visible":true,"origin":"","legend":"\u003cp\u003eComposite map of Ciénaga las Cucharillas study area. Relationship between phreatic salinity of our study area and wetland’s soil type, microelevation and natural channel.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/d8b6136766eb8572eec4b65a.png"},{"id":86769364,"identity":"c5e3ea00-d1b6-4206-ab82-d8f6ac03ab7c","added_by":"auto","created_at":"2025-07-15 11:33:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":82157,"visible":true,"origin":"","legend":"\u003cp\u003eHotspot analysis of Ciénaga Las Cucharillas salinity. Salinity was measured in 40 random points in the 2.2 ha study area during October 2019. Results were superposed with a 1930 georeferenced aerial photograph of the study area that shows natural channels coincide with the presence of natural below ground channels that connect the site to the coast.\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/d6faac160a439307edb36478.png"},{"id":86772556,"identity":"a9335447-e962-4ba1-899a-08437ef5ac7b","added_by":"auto","created_at":"2025-07-15 12:05:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5011421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6933337/v1/e10e2155-3aaa-4305-b78b-25e10dff3dbb.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eSpatiotemporal hydrological dynamics in the Caribbean Anthropocene: the case of a Puerto Rican coastal urban wetland\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoastal wetland hydrology is determined by the interplay of regional and local weather, freshwater inputs and tidal seawater inflow (Lugo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Tiner \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Manzoni et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Global climate change amplifies the effects of historical land use changes on wetland hydrodynamics (Baldwin \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Vilardy et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The hydrologically modified state of the wetland has a direct impact on marine-terrestrial connectivity (Botero and Mancena-Pineda \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Crook et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), therefore reshaping the spatiotemporal physicochemical dynamics of these ecologically important ecosystems.\u003c/p\u003e\u003cp\u003eClimate change and regional climate variability influences sea surface temperatures, sea level rise, frequency and intensity of atmospheric phenomena, and precipitation patterns (Michener et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Osland et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Sea level rise is considered to have the highest impact in coastal wetlands since salinity regimes and flooding are important abiotic drivers within these ecosystems (Morris 2002). Saltwater intrusion, alongside with a reduction of freshwater riverine flow, alterations of subsurface water, and storm surges can result in salinization of wetland ecosystems (Herbert et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; White and Kaplan \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This is highly relevant in Caribbean ecosystems where the current sea level rise is 3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 mm/year, mostly as a result of oceanic thermal expansion (Maitland et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, Caribbean climate is influenced by large scale weather patterns, such as North Atlantic Oscillation (NOA), El Ni\u0026ntilde;o Southern Oscillation, the Intertropical Convergence Zone (ITCZ), steady easterly trade winds, the expansion of the western hemisphere warm pools, the intrusion of cold front, and the passage of atmospheric phenomena (Ashby et al. 2005). The degree of interaction among these teleconnections influences precipitation patterns, winds, river discharge and sea level in the Caribbean. Climate change potentiates the degree of teleconnections' effects (Gould et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), reflected in dry and wet extremes (Giannini et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Vega et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe Caribbean is recognized as a biodiversity hotspot, being the marine and terrestrial coastal zone key habitats in the conservation of biodiversity (Gould et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Along the Caribbean basin, historical and present modification of hydrological conditions of coastal wetlands are typical and accompanied by its effects on vegetation structure and function (Ellison \u0026amp; Farnsworth \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Salazar-Ortiz \u0026amp; Cuevas \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While current research in Caribbean wetlands has focused on plant structure and water chemistry (Webb and G\u0026oacute;mez-G\u0026oacute;mez \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Williams et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Branoff et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hernandez et al. 2021), there is growing recognition that wetland management requires an ecohydrological approach that takes into consideration: 1) how hydrological processes effect the distribution, structure, and function of ecosystems, and2) how biotic processes effect the water cycle (Nuttle, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This is relevant in Caribbean urban coastal wetlands where the environmental heterogeneity is often defined by habitat mosaics, instead of gradients (Hernandez et al. 2021).\u003c/p\u003e\u003cp\u003eThis study uses an integrated multi-level holistic approach to assess the ecohydrological dynamics in a tropical urban coastal wetland. Specifically, we aimed to understand how the combined interactions of local weather variability, marine-terrestrial connectivity and/or anthropic modifications influence spatiotemporal hydrological dynamics and ionic concentrations. This study will assess a) how spatiotemporal dynamics of the water sources determine salinity, phreatic level and ionic concentrations in an urban coastal wetland, and, b) to what extent does the historic and present land use modifications affect the spatiotemporal ionic distribution in the wetland\u0026rsquo;s water. We hypothesize that 1) marine-derived elements will reflect the spatiotemporal distribution of the water sources, and 2) ionic spatiotemporal distribution will be determined by land use and hydrological alterations.\u003c/p\u003e\u003cp\u003eCi\u0026eacute;naga Las Cucharillas Nature Reserve, a palustrine-estuarine wetland located in the northeastern of Puerto Rico, is the largest coastal urban wetland on the island and serves as the model system. As a result of historical hydrological modifications that increased freshwater inflow and a restricted tidal exchange to deep subsurface intrusion, the wetland supports a mosaic of halophytic and palustrine vegetation (Branoff et al \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hernandez et al. 2021).\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area\u003c/h2\u003e\u003cp\u003eThe Ci\u0026eacute;naga las Cucharillas Natural Reserve (18 26\u0026rsquo;25.27\u0026rdquo; N, 66 08\u0026rsquo;08.39\u0026rdquo; W) is located on the northern coast of the Caribbean Island of Puerto Rico and encompasses the western side of the San Juan Bay (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAverage annual temperatures range from 24.7\u0026ndash;31 \u0026ordm;C. Annual average rainfall is 1289 mm and follows a bimodal distribution with May-June and September-November having the highest precipitation. During our sampling period (January 2018 \u0026ndash; October 2022), the average annual temperature was 27\u0026deg;C (20.6\u0026deg;C \u0026ndash; 32.2\u0026deg;C) with 1564 mm average annual precipitation.\u003c/p\u003e\u003cp\u003eThe present conditions of the wetland are a result of multiple anthropogenic activities: a) the creation of drainage channels from the 17th to the mid-20th century for agriculture (Pumarada-O'Neill \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Kennaway and Helmer \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e),b) the 1940s \u0026ndash; 1970s allochthonous soil infill for urbanization, c) the dredging and construction of the Malaria Channel in the late 1940's, and, d) the channel\u0026rsquo;s outflow regulation by pumps and permanent closure from tidal flow at the mouth channel (Seguinot-Baborsa \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Webb and G\u0026oacute;mez-G\u0026oacute;mez \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). As a result, tidal interaction in this wetland occurs via deep subsurface flow (Branoff et al \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hernandez et al. 2021). Nowadays, low-and middle-income urbanized areas, and industrial and commercial facilities in the Cucharillas microbasin influence water quality of both the channel and the wetland. These anthropogenic activities caused a decrease in wetland cover where 500 ha of the historical wetland remains, a 90% loss of its original cover (Martinuzzi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lugo et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCi\u0026eacute;naga las Cucharillas is on top of the Aguada Limestone formation of the North Coast Limestone aquifer system which consists of hard granular calcarenite overlain by alternating beds of chalky marl and rubbly limestone (Anderson \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Giusti and Bennette1976; Monroe \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Within the wetland, Saladar muck (Sm) series represents the natural organic soil type, while Martin Pe\u0026ntilde;a (Mp) series occurs in areas altered by anthropogenic infill. (USDA, 2023). Saladar muck (Sm) series is a Euic, isohyperthermic Typic Haplosa-prists soil consisting of black, highly decomposed (peat) autochthonous vegetation materials, that reach down to bedrock depth in the substrate. Martin Pe\u0026ntilde;a (Mp) series is a fine, mixed, superactive, nonacid, isohyperthermic Humaqueptic Fluvaquents soil that contains deposits of organic material (0\u0026ndash;20 cm) close to surface over mineral sediments embedded in the organic matrix silty clay loam and clay, 20\u0026ndash;45 cm the first and 45\u0026ndash;160 cm depth the latter. The mineral sediments were brought from adjacent higher elevation terrestrial sources for shanty town establishment and development from the 1940\u0026rsquo;s until the late 1970\u0026rsquo;s.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch area and sample collection\u003c/h3\u003e\n\u003cp\u003eThe study site is a 2.2 ha area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) selected based on geospatial analyses. This area is a mosaic representative of the hydrological and edaphic conditions, the presence of predominant plant assemblages and distance from main freshwater source (Malaria Channel) and marine source (San Juan Bay) (Hernandez et al. 2021; Ortiz-Ramirez et al. 2024). Water samples were collected monthly from January 2018 to June 2022, with interruptions due to seismic activity (Liu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and COVID-19 restrictions that limited field access. Additional sampling was carried out in October 2022 to measure the effects of Hurricane Fiona, that directly impacted the island from September 16\u0026ndash;19.\u003c/p\u003e\u003cp\u003eWater was collected from ten-2.5 m depth- monitoring wells installed in 2014 by the Processes and Function Laboratory of Tropical Ecosystems of the University of Puerto Rico (Ecolab) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Wells were distributed in three parallel 200 m transects: a) W1 \u0026ndash; W3 nearest to the Malaria Channel (freshwater input), b) W4, W5, W6 and W9 (in the center), and c) W7, W8 and W10 farthest from the Malaria Channel and nearest the coast (seawater input). Wells four and five are also closest to Juana Matos neighborhood. Well 6 was damaged in 2018 and re-installed in 2021.Water samples were collected from two depths: phreatic level and at 2.5 m depth). Phreatic level is influenced by both \u003cem\u003ein-situ\u003c/em\u003e precipitation as well as inflow from the Malaria and Juana Matos channels. Water collected at 2.5 m depth, on the other hand, is heavily influenced by marine sub-surface inflow from the San Juan Bay Estuary. Phreatic level was recorded on site prior sampling collection. After collection, samples were stored in refrigerated conditions in the field and stored at 4\u0026deg;C in the laboratory until analysis. Precipitation and temperature data was retrieved from a U.S. National Oceanic \u0026amp; Atmospheric Administration meteorological station (RQC00669415), part of the micro-basin, located 3.5km away from the wetland in the municipality of Toa Baja (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncei.noaa.gov/cdo-web/\u003c/span\u003e\u003cspan address=\"https://www.ncei.noaa.gov/cdo-web/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSample analysis\u003c/h3\u003e\n\u003cp\u003eRefrigerated water samples were allowed to reach room temperature before analyses. Salinity was measured using an Eco Sense EC300A conductivity sensor. For ionic analyses, samples were filtered in two steps: 1) B\u0026uuml;chner funnel vacuum filtration and 2) membrane filtration using a 25 mm syringe 0.2\u0026micro;m PTFE filter (John and Reischl \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Levy and Jornitz \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Ten milliliters (10mL) of filtered samples were feed into the instrument to determine the concentrations of Chloride (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) and Sulfate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e), sodium (Na\u003csup\u003e+\u003c/sup\u003e), magnesium (Mg\u003csup\u003e2+\u003c/sup\u003e), calcium (Ca\u003csup\u003e2+\u003c/sup\u003e) and potassium (K\u003csup\u003e+\u003c/sup\u003e). Samples from 2018\u0026ndash;2019 collections were analyzed on a Metrohm\u0026rsquo;s 930 Ion Chromatographer Flex while samples from 2020\u0026ndash;2022 were analyzed using the Dionex ICS-1000 Ion Chromatography System (ICS-1000) to determine ionic concentrations. The Magnesium/Calcium ratio (mol/mol) was used as a marine/terrestrial connectivity indicator: Mg\u003csup\u003e2+\u003c/sup\u003e is marine derived while Ca\u003csup\u003e2+\u003c/sup\u003e in the study area derives from the calcareous bedrock of the basin and the allochthonous infill of the soil (Giusti and Bennett \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Sodium/Potassium ratio (mol/mol) was used as an evapotranspiration indicator since evaporated briny water have high concentrations of ions, particularly Na (Rhamdani et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were carried out using SAS JMP\u0026copy; Pro 17. One-Way ANOVA tests and Tukey-Kramer HSD range tests were performed to determine significant differences in precipitation, phreatic level, salinity, Mg/Ca and Na/K ratios, and ionic concentrations among wells, between depths, among dates and among years. Contour plots for salinity were computed using an interpolation of the data based on Delauney triangulation.\u003c/p\u003e\u003cp\u003eArc GIS Pro was employed to compile, edit, classify, analyze and extract geospatial features and attributes necessary for generating a composite layer integrating multiple datasets. These included: 1) georeferenced wells locations with corresponding salinity values for both the wettest and driest recorded dates classified according to Cowardin et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) into oligohaline (0.5\u0026ndash;5 ppt), mesohaline (5\u0026ndash;18 ppt), and polyhaline (18\u0026ndash;30 ppt) conditions; 2) U.S. Department of Agriculture (USDA, 2023) soil type layers for the study area, specifically \u0026ldquo;Saladar muck\u0026rdquo; (Sm) and \u0026ldquo;Mart\u0026iacute;n Pe\u0026ntilde;a\u0026rdquo; (Mp) soils; 3 a Digital Terrain Model (DTM) layer derived from post-Hurricane Maria LiDAR data (OCM Partners, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), used to delineate micro-elevation gradients within the study site, and 4) A georeferenced historical photomosaic from 1930 was utilized to identify natural drainage channels that existed prior to the extensive urban expansion of the 1940s (Puerto Rico Georeferencing Initiative, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). ArcGIS Pro was further utilized to generate a land surface runoff direction map and conduct hot spot analysis to identify areas of statistically significant spatial clustering.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eClimate variability\u003c/h2\u003e\n \u003cp\u003eAnnual precipitation during the study period ranged from 1260\u0026ndash;2055 mm: 2018 was the wettest, while the driest was 2019. Monthly precipitation ranged from 12.2\u0026ndash;318 mm. According to the K\u0026ouml;ppen-Geiger climate classification (K\u0026ouml;ppen \u003cspan class=\"CitationRef\"\u003e1936\u003c/span\u003e; Peel et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) the driest months during the sampling period were April 2019 (12.2 mm), May 2020 (16.7 mm), March 2018 (37.6 mm), January 2019 (56.39 mm) and May 2021 (58.93 mm). The wettest months were February 2022 (318 mm), May 2018 (318.5 mm), July 2020 (282.5 mm), August 2018 (259.6 mm) and September 2021 (256.5 mm) (Fig. 2). No statistically significant differences in precipitation were detected among years (p\u0026thinsp;=\u0026thinsp;0.1898) or among months within years (p\u0026thinsp;=\u0026thinsp;0.43), likely due to high interannual climate variability\u003c/p\u003e\n \u003cp\u003eRelevant atmospheric phenomena occurred during the study period, including tropical storms Isaias and Laura in July and August 2020, and Hurricane Fiona in September 2022. These storms, particularly Tropical Storm Isaias resulted in elevated precipitation and widespread flooding across Puerto Rico (Beven \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Precipitation patterns during the study did not exhibit the typical bimodal distribution characteristic of the Caribbean Region (Fig. 2). Instead, some years, such as 2018, 2021 and 2022, showed three to four precipitation peaks indicating substantial rainfall events in the study area (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). This variability influenced the duration and timing of dry and wet periods. For example, in February 2022, a month typically associated with dry conditions, a cold front from the northwest brought over 102 mm of precipitation, to the municipality of Cata\u0026ntilde;o, exceeding historical records for February island-wide (Gelpi-Pagan, 2022) and highlighting the pronounced interannual variability in weather conditions at the site.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePhreatic level\u003c/h3\u003e\n\u003cp\u003eThe wetland phreatic level was predominantly superficial (-12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;21.4 cm), with periods of flooding and low water table conditions (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Water levels fluctuated from \u0026minus;\u0026thinsp;78 cm below the surface (BS) to 47.5 cm above the surface (AS) throughout the sampling period. Significant spatiotemporal variability in phreatic level was observed among wells (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Well 4, located 895 m from the bay, had phreatic levels ranging from \u0026minus;\u0026thinsp;78 BS to 19.5 cm AS. In contrast well 3, situated 985 m from the bay and closest to the Malaria Channel, exhibited levels from \u0026minus;\u0026thinsp;46 BS to 47.5 cm AS, reflecting the influence of freshwater inputs from the basin. Water accumulation and phreatic level was also influenced by surface runoff direction, with water flowing towards well 3, 2 and 9, and away from wells 4 and 7 (Fig. 5).\u003c/p\u003e\n\u003ch3\u003eSalinity, marine/terrestrial connectivity, and ionic concentrations\u003c/h3\u003e\n\u003cp\u003eThe sampling area exhibited a three-dimensional mosaic defined by location, phreatic depth, and time, with salinity conditions raging from oligohaline (0.5\u0026ndash;5 ppt) to polyhaline (18\u0026ndash;30 ppt). Observed salinity values spanned from 0.3 to 31.3 ppt (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), encompassing zones with freshwater conditions (0\u0026ndash;0.4 ppt) as well as areas that reached euhaline levels (30\u0026ndash;40 ppt).\u003c/p\u003e\n\u003cp\u003eThere were statistically significant differences in salinity among wells (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), among sampling years (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), among sampling dates (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and between depths (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Oligohaline conditions were observed across the entire sampling site at the phreatic level in January and May 2018, September and November 2020 and October 2022. These conditions were associated with atmospheric disturbances and significant precipitation events. In general, mesohaline to polyhaline conditions were recorded at deeper depths (2.5 m depth), which is consistent with subsurface marine water intrusion from San Juan Bay. During dry months and consecutive dry periods, the salinity ranged from mesohaline to polyhaline at both depths. Monitoring wells near to the Malaria channel (wells 1\u0026ndash;3) exhibited lower salinities, ranging from 0.1 ppt (during wetter months) to 17.7 ppt (during drier months) at phreatic level and 0.5 ppt to 25.8 ppt at deeper depths. Monitoring wells 4 and 5 showed the highest salinity at the phreatic level, 25.6 ppt and 31.3ppt respectively (indicative of polyhaline and euhaline conditions). At deeper depths, wells 5 and 10 exhibited the highest salinities at deeper depths, 31.3 ppt and 33 ppt respectively (euhaline conditions).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMonitoring wells mean salinity and ranges through the 2018\u0026ndash;2022 sampling period.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistance from the coast (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean salinity at phreatic level (ppt)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003cp\u003e(ppt)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean salinity at 2.5 m depth (ppt)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003cp\u003e(ppt)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u0026ndash;17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u0026ndash;16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u0026ndash;6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u0026ndash;17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u0026ndash;16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u0026ndash;25.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2\u0026ndash;29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4\u0026ndash;30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u0026ndash;31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4\u0026ndash;31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u0026ndash;13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u0026ndash;17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4\u0026ndash;24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u0026ndash;24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.3\u0026ndash;28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u0026ndash;24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u0026ndash;29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4\u0026ndash;24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u0026ndash;33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMagnesium/calcium ratios (Mg\u003csup\u003e2+\u003c/sup\u003e/Ca\u003csup\u003e2+\u003c/sup\u003e) were used to differentiate marine from terrestrial influence (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). All wells exhibited Mg/Ca ratios below typical seawater (5.4 mol:mol) although temporal variability was evident, with some measurements occasionally exceeding those of sea water. Significant spatiotemporal heterogeneity in Mg/Ca ratios was observed among wells (PL: p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; DD: p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). There were also significant differences at phreatic level (PL) and deeper depths (DD) for wells 4, 6\u0026ndash;10. At phreatic level, monitoring wells 5 and 9 exhibited the highest Mg/Ca from 1.5 to 7.9 mol:mol and 1.6 to 4.2 mol:mol, respectively. At deeper depth, elevated Mg/Ca ratios were observed in four wells, ranked as follows: 10 (2.7\u0026ndash;7.9 mol:mol)\u0026thinsp;\u0026gt;\u0026thinsp;8 (2.1\u0026ndash;6.7 mol:mol)\u0026thinsp;\u0026gt;\u0026thinsp;9 (2.3\u0026ndash;4.9 mol:mol)\u0026thinsp;\u0026gt;\u0026thinsp;5 (1.9\u0026ndash;9.2 mol:mol). Water samples collected from well 2, nearest to the Malaria Channel, had the lowest Mg/Ca ratios for both depths, ranging from 0.8 to 3.8 mol:mol (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eSodium/Potasium (Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e/) ratios indicated evapotranspiration. These ratios were consistently higher than Mg/Ca ratios, and exceeded the Na/K ratios of seawater collected from San Juan Bay (44.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01). There were statistically significant differences in Na/K ratios among wells (PL: p-value\u0026thinsp;\u0026lt;\u0026thinsp;0. 01; DD: p-value\u0026thinsp;\u0026lt;\u0026thinsp;0. 01), with some showing values close to the seawater reference, while others exhibited values nearly twice as high (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB). Notably, wells 2, 6 and 7 recorded the highest Na/K ratio at both phreatic level (PL) and at a deeper depth (DD), while wells 3, 5, 9 and 10 had the lowest. To illustrate the spatiotemporal dynamics found in our study, we compared two representative wells with contrasting Na/K values: well 2 (24.8\u0026ndash;156.9), which had the highest ratio at both depths (PL, DD), and well 10(34.4\u0026ndash;70.4), which had the lowest. (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB). During dry periods, salt polygons (sodium deposits) were seen on the soil surface of the wetland, particularly in allochthonous areas. Both ratios demonstrated dynamic contrasts: Mg/Ca was consistently higher in well 10 (nearest to the bay), while Na/K was consistently higher in well 2 (closest to the Malaria Channel).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSubstrate types\u003c/h2\u003e\n \u003cp\u003eIn Cucharillas\u0026rsquo; study site wells, 6, 9 and 10 were installed in organic peat substrates derived from original autochthonous natural plant decomposition, while wells 1, 2,3, 4, 5, 7 and 8 and areas with anthropogenic allochthonous mineral infills (10\u0026ndash;40 cm depth), embedded within the organic matrix (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough this research, we determined how the combined interactions of local weather variability, marine-terrestrial connectivity and/or anthropic modifications influenced the spatiotemporal hydrological dynamics and ionic concentrations in a Caribbean tropical coastal wetland. The ecohydrology of Ci\u0026eacute;naga Las Cucharillas is governed by a complex interplay of factors. While climatic variability and marine-terrestrial connectivity are the primary drivers, additional parameters including basin\u0026rsquo;s geology, soil type, substrate physicochemical properties, microtopography and water flow direction, contribute shaping about a tridimensional spatiotemporal hydrological mosaic in Ci\u0026eacute;naga Las Cucharillas.\u003c/p\u003e\u003cp\u003eRainfall variability was primarily driven by differences in intensity, persistence and periodicity of events. This was the case of May 2018 where monthly precipitation was influenced by localized intense rainfall associated with the passing of atmospheric throughs and tropical waves. February 2022, the month with the highest recorded precipitation, also exhibited similar extreme conditions driven by intense rainfall associated with a cold front (Gelpi-Pagan, 2022). This event was unusual on the island, as February is typically associated with drier conditions. These deviations are increasingly common as climate change intensifies teleconnection effects (Vega et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mann, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gould et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Destouches et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that, for the period of 1985 to 2015, warming sea surface temperatures (SSTs) were associated with an increase in daily precipitation intensity and number of rainy days for the island of Puerto Rico.\u003c/p\u003e\u003cp\u003eFrom 2020 to 2022 there was a higher prevalence of significant rainfall events, such as tropical depressions, storms and hurricanes. Particularly, Tropical Storms Isaias and Laura occurred on July and August 2020, while Hurricane Fiona impacted the island on September 2022. These major events were associated with the highly active 2020 North Atlantic hurricane season, driven by a combination of atmospheric and oceanic conditions, including low vertical wind shear, below-average sea level pressures, elevated sea surface temperatures (SSTs), and positive phases of the Atlantic Multidecadal Oscillation (AMO) and the Atlantic Meridional Mode (AMM) (Beven \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These conditions coincided with 2020\u0026ndash;2023 La Ni\u0026ntilde;a \u0026ldquo;triple dip\u0026rdquo; phenomenon, characterized by prolonged cooler-than-average sea surface temperatures on the eastern Pacific Ocean. This extended La Ni\u0026ntilde;a \u0026ldquo;triple dip\u0026rdquo; event was unprecedented since the 1950s and challenges earlier projections from the IPCC models that anticipated El Ni\u0026ntilde;o conditions. Emerging evidence suggests that La Ni\u0026ntilde;a-like conditions may become more frequent in the future (Hern\u0026aacute;ndez-Ayala and M\u0026eacute;ndez-Tejeda \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Iwakiri et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLocal climate regulated temporal variations in salinity and freshwater inputs, as well as phreatic levels. Sustained rainfall and/or extreme precipitation events enhanced the subsurface freshwater flow from the Malaria Channel into the wetland substrate, thereby increasing phreatic water levels and soil water storage at the site. These weather extremes homogenized the wetland\u0026rsquo;s hydrological conditions as seen on September 2020 were the effects of both Tropical Storms Isaias and Laura, resulted in oligohaline conditions and phreatic levels above soil surface across the entire sampling area. Similar patterns were observed after Hurricane Fiona on 2022, and in early January 2018, after the prolonged flooding associated with the effects of Hurricane Maria on September 2017. Strong capillary action may have produced a substantial capillary fringe facilitating rising water tables rising in response to intense rainfall events (Rosenberry and Winter, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). A comparable event was documented in Galveston Bay, United States after Hurricane Harvey (Cat 4), which caused prolonged high-water levels and sharp decreases in salinity (Du et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Dry conditions had the opposite effects: reduced phreatic levels and elevated salinities suggesting an evaporative concentration effect of solutes due to high temperatures and humidity.\u003c/p\u003e\u003cp\u003eSpatial hydrological variability was observed throughout the study area. Usually, such variability is characterized by a landward-decreasing salinity gradient from hypersaline to freshwater conditions (Cowardin 1979). This has been reported for multiple decades as seen in numerous studies (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, in this study, salinity did not conform the expected gradient, but instead was a hydrological mosaic that stemmed from the deep sub-surface terrestrial-marine connectivity. Salinity hotspots analysis indicated that this pattern is not solely the result of the permanent closure from tidal flow at the mouth of the Malaria Channel but also influenced by the presence of natural subsurface channels connecting wells 4 and 5 to the coastline (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e10\u003c/span\u003e). These findings indicate that the wetland\u0026rsquo;s original hydrogeological structure continues to shape its ecohydrological dynamics, and highlights the importance of monitoring of groundwater salinity alongside phreatic salinity, as the latter alone may not adequately capture deep-subsurface marine intrusion.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSalinity gradients for tropical and temperate wetlands around the world.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoastal wetland type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSalinity range\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePalustrine-estuarine coastal wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSalinity did not follow the expected gradient and instead was heterogenous resulting in a hydrological mosaic.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCi\u0026eacute;naga las Cucharillas Reserve, Puerto Rico\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal freshwater lacustrine temperate wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSalinity ranged from 2\u0026ndash;5 ppt due to strong saline intrusion. There were salinity gradients inland.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLake Wailoha, South Island, New Zealand ( Schallenberg et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstuarine temperate wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSalinity ranged from 0\u0026ndash;12 ppt. There was a salinity gradient; it decreased upstream the Pamlico River.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFloodplains of Jacks Creek, Panico River estuary, North Carolina, United States (Brinson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1985\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstuarine humid sub-tropical natural and managed wetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNatural wetlands ranged from oligohaline to euhaline salinities inland towards the sea. Managed wetlands had gates for water management and control that divided fresh and saltwater.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHobcaw Barony wetlands, South Carolina, United States (Wang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntertidal and groundwater salinity was higher near the sea (\u0026gt;\u0026thinsp;10.8 ppt) and lower inland (5 ppt).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEl Casta\u0026ntilde;o Wetlands System, Chiapas, M\u0026eacute;xico (Rinc\u0026oacute;n-P\u0026eacute;rez et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSalinity goes from 1.5\u0026ndash;19 ppt in riverine mangrove areas to 23.9\u0026ndash;35.7 ppt in the fringe mangrove area.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSan Andres Island, Colombia (Caribbean) (Urrego et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe wetland\u0026rsquo;s geomorphology, substrate composition, and water flow also contributed to the hydrological dynamics of the wetland. Mg/Ca ratios in Ci\u0026eacute;naga Las Cucharillas were higher than those reported for the coastal wetland of Sel\u0026ccedil;uk Plain in Turkey, where both elements are primarily derived from weathering of Ca-Mg silicates from its basin\u0026rsquo;s geology (Somay and Gemici \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This highlights that although the weathering of Cucharilla\u0026rsquo;s bedrock (Monroe \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) and allochthonous infill may contribute to Mg and Ca concentrations, marine inputs play a dominant role. The elevated Na/K ratios in the wetland water (all exceeding 50 mmol:mmol) compared to the ratio of freshwater from the microbasin (25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 mmol:mmol), are likely a result of a combination of sea water intrusion and cation exchange involving NaCl solutions (Somay and Filiz \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This reaffirms the relevant marine influence of magnesium and sodium across the study area, especially in zones closes to the coast or associated with subsurface flow paths potentially connecting to the San Juan Bay. Evapotranspiration and limited leaching capacity in the wetland soils may have also contributed to elevated Na concentrations as seen in the Pantanal wetland in South America (Furquim, and Vidoca \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSubstrate composition played a significant role in regulating phreatic level and salinity dynamics within the Ci\u0026eacute;naga Las Cucharillas Reserve. Peats soils are known for their high-water retention capacity. When compared to clay-peat mixtures, peats could retain up to 41% of the soil moisture content 53 days after soils are dried, a significantly higher percentage than clay-peat mixtures (10\u0026ndash;15%) (Feustel \u0026amp; Byers, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1936\u003c/span\u003e). Phreatic levels were highest at well 3, located in a peat dominated substrate, and lowest at well 4, which was underlain by mineral substrate. Land-surface runoff direction corresponded with these patterns, indicating that a combination of substrate type, hydrological inputs, and topography contribute to the complex hydrological dynamics of Ci\u0026eacute;naga Las Cucharillas. Substrate composition also played a role in Na/K differences along the sampling site. Well 2, underlain by allochthonous mineral infill exhibited higher Na/K ratios than well 10, characterized by a substrate richer in peat. Compared to peats, clay soils possess finer particles and larger surface areas, enhancing the adsorption of dissolved salts onto the substrate. These properties have been associated with elevated cation retention in both agricultural fields (Taylor and Kr\u0026uuml;ger \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and wetlands (Chairawiwut et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Substrate differences, in combination with evapotranspiration, ionic mobility and cation exchange, played a role in the ionic dynamics along the study site.\u003c/p\u003e\u003cp\u003eThese results support the classification of Ci\u0026eacute;naga las Cucharillas as a palustrine-estuarine wetland. The absence of direct marine inflow through the Malaria Channel, coupled with deep subsurface saline intrusion driven by tidal forces and marine water inputs via the natural subsurface channels, facilitates the mixing of freshwater and seawater characteristic of coastal wetlands. This mixing resulted in a mesohaline to polyhaline conditions in the estuarine zone of the wetland mosaic, which support the presence of mangrove species such as white mangrove (\u003cem\u003eLaguncularia racemosa\u003c/em\u003e) and black mangrove (\u003cem\u003eAvicennia germinans\u003c/em\u003e) throughout the ecosystem (Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, areas dominated by surficial freshwater inputs maintain oligohaline conditions, supporting their classification as palustrine environments within the broader mosaic. These dynamics underscore the importance of adopting an ecohydrological perspective in wetland assessment and restoration.\u003c/p\u003e\u003cp\u003eCoastal wetland\u0026rsquo;s location at the terrestrial\u0026ndash;marine interface makes them vulnerable to increasing rates of sea-level rise and climate variability. Precipitation models predict a decline in rainfall across tropical and subtropical regions (Solecky et al. 2024), especially on the eastern side of the Caribbean and Puerto Rico (PRCC Council, 2014). The combined effects of sea level rise, which will increase saline intrusion, reduced precipitation and greater drought frequency and intensity, are expected to alter not only wetlands hydrology, but also its vegetation and its distribution. In heavily modified systems, hydrological connectivity may no longer be apparent at the surface but persists belowground. A comprehensive 5-year study, like the one carried out in Cucharillas, is essential for understanding hydrological dynamics and determine valuable management and restoration strategies that takes into account subsurface flow dynamics and legacy geomorphology, that can aid vegetative species adapt to future climates.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study provides a valuable framework for modeling impacted urban coastal wetlands. It assessed the influence of local weather and marine-terrestrial connectivity on wetland hydrological conditions through a 5-year-long monitoring of salinity, phreatic level, precipitation, and ionic concentrations, all which directly influence hydro-regime dynamics. While climatic variability and marine-terrestrial connectivity sustains a tridimensional hydrological spatiotemporal palustrine-estuarine mosaic in Ci\u0026eacute;naga Las Cucharillas, the basin\u0026rsquo;s geology, soil type, substrate physicochemical characteristics, microtopography and water flow direction also play an active role in shaping the wetland\u0026rsquo;s hydrological dynamics.\u003c/p\u003e\u003cp\u003eBelowground flow paths influenced salinity through deep subsurface marine inputs, underscoring the importance of considering both the present and historical states of the system when studying, managing, and rehabilitating. Deep subsurface hydrodynamic processes in enclosed coastal wetlands lacking direct marine inflow are increasingly becoming the norm rather than the exception, due to the extensive hydrological modifications caused by the expanding gray infrastructure. These modifications can result in marked vertical differences in hydrological throughout the wetland water profile, which must be taken into account in the development of effective coastal wetland management and rehabilitation plans.\u003c/p\u003e\u003cp\u003eFuture monitoring and management strategies should include groundwater salinity measurements, as the results indicate that is equally as important as phreatic salinity which can obscure evidence of deep-subsurface marine intrusion. The use of the natural abundance of stable isotopes of water (δD and δ\u003csup\u003e18\u003c/sup\u003eO) to establish water source mixing dynamics and a more thorough plant-water-soil study can help a better understanding of Ci\u0026eacute;naga Las Cucharilla\u0026rsquo;s ecohydrology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;The authors have no relevant financial or non-financial interests to disclose.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was funded by NSF CREST \u0026ndash; Center for Innovation Research and Education in Environmental Nanotechnology (CIRE2N) HRD-1736093 and NSF HRD-1806129.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conceptualization, design, and realization. SPP: data curation, formal analysis and methodology, data analysis, geospatial analysis, writing- original draft, writing- review and editing. EC: Maps, formal analysis and methodology, writing- review and editing. GOR: formal analysis and methodology, sample collection and analysis, geospatial analysis, writing- review and editing. EHF: formal analysis and methodology, sample collection and analysis, writing- review and editing. LD: sample collection and analysis. DO: sample collection and analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge the Center for Applied Tropical Ecology and Conservation (CATEC) for technical assistance. Undergraduate students and volunteers from the Process and Functions Ecosystem Lab who assisted with field and lab work. We also thank Dr. Elvis Torres, Laboratory of Atmospheric Chemistry and Atmospheric Particulate and Marylene Fox of the Laboratory of Tropical Limnology for ionic concentration analysis. Lastly, we thank El Corredor Del Yaguazo Inc., including Pedro Carrion and personnel who assisted with field work, and the University of Puerto Rico\u0026rsquo;s Environmental Department GIS Lab.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson HR (1976) Ground Water in the San Juan Metropolitan Area, Puerto Rico: U.S. Geological Survey Water-Resources Investigations Report #41\u0026ndash;75. USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri7541\u003c/span\u003e\u003cspan address=\"10.3133/wri7541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed June 10 2019\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshby SA, Taylor MA, Chen AA Statistical models for predicting rainfall in the Caribbean. Theor Appl Climatol 82: 65\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00704-004-0118-8\u003c/span\u003e\u003cspan address=\"10.1007/s00704-004-0118-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaldwin J (2000) Tourism development, wetland degradation and beach erosion in Antigua, West Indies. Tour Geogr 2(2):193\u0026ndash;218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14616680050027897\u003c/span\u003e\u003cspan address=\"10.1080/14616680050027897\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeven JL (2021) The 2020 Atlantic Hurricane Season: The Most Active Season on Record. Weatherwise 74(5):33\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00431672.2021.1953905\u003c/span\u003e\u003cspan address=\"10.1080/00431672.2021.1953905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBotero L, Mancena-Pineda E (1996) S\u0026iacute;ntesis de los cambios antr\u0026oacute;picos ocurridos en los \u0026uacute;ltimos cuarenta a\u0026ntilde;os en la Ci\u0026eacute;naga Grande de Santa Marta, Colombia. Revista Acad Colomb Ci Exact 20(78):465\u0026ndash;474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18257/raccefyn.20(78).1996.3055\u003c/span\u003e\u003cspan address=\"10.18257/raccefyn.20(78).1996.3055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBranoff B, Cuevas E, Hern\u0026aacute;ndez E (2018) Assessment of urban coastal wetlands vulnerability in Puerto Rico. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.drna.pr.gov/wp-content/uploads/2018/09/FEMA-Wetlands-Report.pdf\u003c/span\u003e\u003cspan address=\"https://www.drna.pr.gov/wp-content/uploads/2018/09/FEMA-Wetlands-Report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrinson MM, Bradshaw HD, Jones MN (1985) Transitions in forested wetlands along gradients of salinity and hydroperiod. J Elisha Mitchell Sci Soc, 76\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jstor.org/stable/24333326\u003c/span\u003e\u003cspan address=\"https://www.jstor.org/stable/24333326\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChairawiwut W, McMartin DW, Azam S (2016) Salts removal from synthetic solution-potash brine by non-planted constructed wetlands. Water 8(4):113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0043-1354(02)00484-0\u003c/span\u003e\u003cspan address=\"10.1016/S0043-1354(02)00484-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCowardin LM, Carter V, Golet FC, Laroe ET (1979) Classification of wetlands and deepwater habitats of the United States: U.S. Fish and Wildlife Service Report FWS/OBS \u0026ndash;\u0026thinsp;79/31, 131 p\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrook DA, Lowe WH, Allendorf FW, Erős T, Finn DS, Gillanders BM, Hadwen WL, Harrod C, Hermoso V, Jennings S, Kilada RW, Nagelkerken I, Hansen MM, Page TJ, Riginos C, Fry B, Hughes JM (2015) Human effects on ecological connectivity in aquatic ecosystems: Integrating scientific approaches to support management and mitigation. Sci Total Environ 534:52\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2015.04.034\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2015.04.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDestouches C, Diedhiou A, Anquetin S, Hingray B, Pierre A, Boisson D, Joseph A (2025) Changes in extreme precipitation patterns over the Greater Antilles and teleconnection with large-scale sea surface temperature. Earth Syst Dyn 16(2):497\u0026ndash;512. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/esd-16-497-2025\u003c/span\u003e\u003cspan address=\"10.5194/esd-16-497-2025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDu J, Park K, Dellapenna TM, Clay JM (2019) Dramatic hydrodynamic and sedimentary responses in Galveston Bay and adjacent inner shelf to Hurricane Harvey. Sci Total Environ 653:554\u0026ndash;564. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2018.10.403\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2018.10.403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEllison AM, Farnsworth EJ (1996) Anthropogenic Disturbance of Caribbean Mangrove Ecosystems: Past Impacts, Present Trends, and Future Predictions. Biotropica 28(4):549\u0026ndash;565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2389096\u003c/span\u003e\u003cspan address=\"10.2307/2389096\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeustel IC, Byers HG (1936) The comparative moisture-absorbing and moisture-retaining capacities of peat and soil mixtures (No. 532). US Department of Agriculture\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFurquim SAC, Vidoca TT (2021) Salt-Affected Soils of Pantanal Wetland. In: Taleisnik E, Lavado RS (eds) Saline and Alkaline Soils in Latin America. Springer, Cham\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGelp\u0026iacute;-Pag\u0026aacute;n J (2022) Tu tiempo: febrero 2022 rompe r\u0026eacute;cord de lluvia reportada. Noticentro: Tu Tiempo. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wapa.tv/noticias/tiempo/tu-tiempo--febrero-2022-rompe-record-de-lluvia-reportada_20131122526189.html\u003c/span\u003e\u003cspan address=\"https://www.wapa.tv/noticias/tiempo/tu-tiempo--febrero-2022-rompe-record-de-lluvia-reportada_20131122526189.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 27 October 2023\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiannini A, Kushnir Y, Cane MA (2000) Interannual variability of Caribbean rainfall, ENSO, and the Atlantic Ocean. J Clim 13(2): 297\u0026ndash;311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/15200442\u003c/span\u003e\u003cspan address=\"10.1175/15200442\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(2000)013%3C0297:IVOCRE%3E2.0.CO;2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiusti EV, Bennett GD (1976) Water resources of the north coast limestone area. U.S. Geological Survey Water-Resources Investigation Report 75\u0026thinsp;\u0026ndash;\u0026thinsp;42, Puerto Rico\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGould WA, Castro-Prieto J, \u0026Aacute;lvarez-Berr\u0026iacute;os NL (2020) Climate change and biodiversity conservation in the Caribbean islands. In Encyclopedia of the World\u0026rsquo;s Biomes, First edition; Goldstein M.I., DellaSala D.A., Eds; Elsevier Academic Press: Amsterdam, Netherlands, 2020; vol 1, pp. 114\u0026ndash;125\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGould WA, Dias E, Terando A, Jury M, Bowden J, Chardon P, Mel\u0026eacute;ndez-Oyola M, Morell J (2022) Puerto Rico\u0026rsquo;s state of the climate 2014\u0026ndash;2021. Puerto Rico Climate Change Council. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubs.usgs.gov/publication/70249920\u003c/span\u003e\u003cspan address=\"https://pubs.usgs.gov/publication/70249920\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 15 February 2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerbert ER, Boon P, Burgin AJ, Neubauer SC, Franklin RB, Ard\u0026oacute;n M, Hopfensperger KN, Lamers LPM, Gell P (2015) A global perspective on wetland salinization: ecological consequences of a growing threat to freshwater wetlands. Ecosphere 6(10):206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1890/ES14-00534.1\u003c/span\u003e\u003cspan address=\"10.1890/ES14-00534.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHern\u0026aacute;ndez E, Cuevas E, Pinto-Pacheco S, Ort\u0026iacute;z-Ram\u0026iacute;rez G (2021) You Can Bend Me but Can\u0026rsquo;t Break Me: Vegetation Regeneration After Hurricane Mar\u0026iacute;a Passed Over an Urban Coastal Wetland in Northeastern Puerto Rico. Front Glob Change 4:752328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/ffgc.2021.752328\u003c/span\u003e\u003cspan address=\"10.3389/ffgc.2021.752328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHern\u0026aacute;ndez-Ayala JJ, M\u0026eacute;ndez-Tejeda R (2022) The Extremely Active 2020 Hurricane Season in the North Atlantic and Its Relation to Climate Variability and Change. Atmosphere 13(12): 1945. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos13121945\u003c/span\u003e\u003cspan address=\"10.3390/atmos13121945\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIwakiri T, Imada Y, Takaya Y, Kataoka T, Tatebe H, Watanabe M (2023) Triple-Dip La Ni\u0026ntilde;a in 2020\u0026ndash;23: North Pacific atmosphere drives 2nd year La Ni\u0026ntilde;a. Geophys Res Lett 50(22): e2023GL105763. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2023GL105763\u003c/span\u003e\u003cspan address=\"10.1029/2023GL105763\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang S, Zhu C, Hu ZZ, Jiang N, Zheng F (2023) Triple-dip La Ni\u0026ntilde;a in 2020\u0026ndash;23: understanding the role of the annual cycle in tropical Pacific SST. Environ Res Lett 18(8):084002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1088/1748-9326/ace274\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/ace274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohn W, Reischl G (1978) Measurements of the filtration efficiencies of selected filter types. Atmos. Environ. (1967). 12(10): 2015\u0026ndash;2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0004-6981(78)90139-7\u003c/span\u003e\u003cspan address=\"10.1016/0004-6981(78)90139-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKennaway T, Helmer EH (2007) The forest types and ages cleared for land development in Puerto Rico. GISci Remote Sens 44(4):356\u0026ndash;382. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2747/1548-1603.44.4.356\u003c/span\u003e\u003cspan address=\"10.2747/1548-1603.44.4.356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK\u0026ouml;ppen W (1936) Das geographische System der Klimate, Handbuch der Klimatologie [The Geographical System of the Climate, Handbook of Climatology], vol 1. Borntraeger, Berlin\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevy RV, Jornitz MW (2006) Types of Filtration. In: Jornitz MW (ed) Sterile Filtration. Advances in Biochemical Engineering, vol 98. Springer, Berlin, Heidelberg\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu C, Lay T, Wang Z, Xiong X (2020) Rupture process of the 7 January 2020, MW 6.4 Puerto Rico earthquake. Geophys Res Lett 47(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020GL087718\u003c/span\u003e\u003cspan address=\"10.1029/2020GL087718\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. e2020GL087718\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLugo AE, Brown S, Brinson MM (1988) Forested wetlands in freshwater and salt-water environments. Limnol Oceanogr 33(4part2):894\u0026ndash;909. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4319/lo.1988.33.4part2.0894\u003c/span\u003e\u003cspan address=\"10.4319/lo.1988.33.4part2.0894\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLugo AE, Ramos-Gonz\u0026aacute;lez OM, Rodr\u0026iacute;guez-Pedraza C (2011) The R\u0026iacute;o Piedras watershed and its surrounding environment. FS-980. U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry, p 46\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMann ME (2021) Beyond the hockey stick: Climate lessons from the Common Era. Proc Natl Acad Sci USA 118(39):e2112797118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2112797118\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2112797118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaitland DO, Richter K, Raj RP, Bonaduce A, Nisancioglu KH, Taylor MA, Stephenson TS (2024) Determining sea-level rise in the Caribbean: A shift from temperature to mass control. Sci Rep 14:10387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-60201-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-60201-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManzoni S, Maneas G, Scaini A, Psiloglou BE, Destouni G, Lyon SW (2020) Understanding coastal wetland conditions and futures by closing their hydrologic balance: the case of the Gialova lagoon. Greece Hydrol Earth Syst 24:3557\u0026ndash;3571. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/hess-24-3557-2020\u003c/span\u003e\u003cspan address=\"10.5194/hess-24-3557-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartinuzzi S, Gould WA, Lugo AE, Medina E (2009) Conversion and recovery of Puerto Rican mangroves: 200 years of changes. Ecol Manag 257:75\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foreco.2008.08.037\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2008.08.037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichener WK, Blood ER, Bildstein KL, Brinson MM, Gardner LR (1997) Climate change, hurricanes and tropical storms, and rising sea level in coastal wetlands. Ecol Appl 7(3): 770\u0026ndash;801. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/1051-0761\u003c/span\u003e\u003cspan address=\"10.1890/1051-0761\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(1997)007[0770:CCHATS]2.0.CO;2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMonroe WH (1980) Some Tropical Landforms of Puerto Rico. USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/pp1159\u003c/span\u003e\u003cspan address=\"10.3133/pp1159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed September 15 2019\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorris JT, Sundareshwar PV, Nietch CT, Kjerfve B, Cahoon DR (2002) Responses of coastal wetlands to rising sea level. Ecol 83:2869\u0026ndash;2877. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/0012-9658(2002)083\u003c/span\u003e\u003cspan address=\"10.1890/0012-9658(2002)083\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[2869:ROCWTR]2.0.CO;2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNuttle WK (2002) Is ecohydrology one idea or many? Hydrol Sci J 47(5):805\u0026ndash;807. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02626660209492983\u003c/span\u003e\u003cspan address=\"10.1080/02626660209492983\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePartners OCM (2025) : 2018 USGS Lidar DEM: Post Hurricane Maria - Puerto Rico from 2010-06-15 to 2010-08-15. NOAA National Centers for Environmental Information, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fisheries.noaa.gov/inport/item/60105\u003c/span\u003e\u003cspan address=\"https://www.fisheries.noaa.gov/inport/item/60105\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrtiz-Ram\u0026iacute;rez G, Hern\u0026aacute;ndez E, Pinto-Pacheco S, Cuevas E (2024) The Dynamics of Soil Mesofauna Communities in a Tropical Urban Coastal Wetland: Responses to Spatiotemporal Fluctuations in Phreatic Level and Salinity. Arthropoda 2:1\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.preprints.org/manuscript/202309.1767\u003c/span\u003e\u003cspan address=\"https://www.preprints.org/manuscript/202309.1767\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsland MJ, Enwright NM, Day RH, Gabler CA, Stagg CL, Grace JB (2016) Beyond just sea-level rise: considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change. Glob Change Biol 22(1):1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.13084\u003c/span\u003e\u003cspan address=\"10.1111/gcb.13084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeel MC, Finlayson BL, McMahon TA (2007) Updated world map of the K\u0026ouml;ppen-Geiger climate classification. Hydro Earth Syst Sci 11:1633\u0026ndash;1644. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/hess-11-1633-2007\u003c/span\u003e\u003cspan address=\"10.5194/hess-11-1633-2007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuerto Rico Georeferencing Initiative (2025) Puerto Rico 1930 georeferenced photomosaic: Technical documentation [PDF]. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prgeoref.weebly.com/uploads/8/6/3/9/86396506/puerto_rico_1930_georeferenced.pdf\u003c/span\u003e\u003cspan address=\"https://prgeoref.weebly.com/uploads/8/6/3/9/86396506/puerto_rico_1930_georeferenced.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePumarada-O'Neill L (1991) Los puentes hist\u0026oacute;ricos de Puerto Rico. Centro de Investigaci\u0026oacute;n y Desarrollo. Recinto de Mayag\u0026uuml;ez, Universidad de Puerto Rico\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRhamdani AR, Lalasari LH, Sulistyono E, Setiawan I, Firdiyono F (2019) The Effect of Slow Evaporation on Ca, Na, K, and Mg Concentration in Natural Brine from Ciseeng, West Java. IOP Conf Ser Mater Sci Eng 541(1):012040. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1088/1757-899X/541/1/012040\u003c/span\u003e\u003cspan address=\"10.1088/1757-899X/541/1/012040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRinc\u0026oacute;n-P\u0026eacute;rez M, Infante-Mata D, Moreno-Casasola P, Hern\u0026aacute;ndez Alarc\u0026oacute;n ME, Barba Mac\u0026iacute;as E, Garc\u0026iacute;a-Alfaro JR (2020) Patrones de distribuci\u0026oacute;n y estructura de la vegetaci\u0026oacute;n en el gradiente de humedales costeros El Casta\u0026ntilde;o, Chiapas, M\u0026eacute;xico. Rev Biol Trop 68(1):242\u0026ndash;259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.15517/rbt.v68i1.37616\u003c/span\u003e\u003cspan address=\"10.15517/rbt.v68i1.37616\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenberry DO, Winter TC (1997) Dynamics of water-table fluctuations in an upland between two prairie-pothole wetlands in North Dakota. J Hydrol 191(1\u0026ndash;4):266\u0026ndash;289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0022-1694(96)03050-8\u003c/span\u003e\u003cspan address=\"10.1016/S0022-1694(96)03050-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalazar-Ortiz M, Cuevas E (2017) Land use and land management during the past century determine mangrove dynamics in northwestern Puerto Rico: the case of the Maracayo mangrove. J Environ Eng Sci B 6:593\u0026ndash;623. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17265/2162-5263/2017.12.002\u003c/span\u003e\u003cspan address=\"10.17265/2162-5263/2017.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchallenberg M, Hall CJ, Burns CW (2003) Consequences of climate-induced salinity increases on zooplankton abundance and diversity in coastal lakes. Mar Ecol Prog Ser 251:181\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3354/meps\u003c/span\u003e\u003cspan address=\"10.3354/meps\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeguinot-Baborsa J (1983) Coastal Modification and Land Transformation In The San Juan Bay Area: Puerto Rico. Dissertation, Louisiana State University and Agricultural and Mechanical College \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://uprrp.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/coastal-modification-land-transformation-san-juan/docview/303167572/se-2\u003c/span\u003e\u003cspan address=\"https://uprrp.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/coastal-modification-land-transformation-san-juan/docview/303167572/se-2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSolecki W, Roberts D, Seto KC (2024) Strategies to improve the impact of the IPCC Special Report on Climate Change and Cities. Nat Clim Change 14(7):685\u0026ndash;691. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41558-024-02060-9\u003c/span\u003e\u003cspan address=\"10.1038/s41558-024-02060-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSomay MA, Filiz S (2003) Hydrology, hydrogeology and hydrochemistry of wetlands: a case study in Izmir Bird Paradise, Turkey. Environ Geol 43:825\u0026ndash;835. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00254-002-0697-6\u003c/span\u003e\u003cspan address=\"10.1007/s00254-002-0697-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSomay MA, Gemici \u0026Uuml; (2009) Assessment of the salinization process at the coastal area with hydrogeochemical tools and geographical information systems (GIS): Sel\u0026ccedil;uk plain, Izmir, Turkey. Water Air Soil Poll 201:55\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11270-008-9927-1\u003c/span\u003e\u003cspan address=\"10.1007/s11270-008-9927-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaylor M, Kr\u0026uuml;ger N (2019) Changes in salinity of a clay soil after a short-term salt water flood event. Geoderma Reg 19:e00239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geodrs.2019.e00239\u003c/span\u003e\u003cspan address=\"10.1016/j.geodrs.2019.e00239\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTiner RW (2018) Hydrology of coastal wetlands. The wetland book I: Structure and function, management and methods. Springer, Netherlands, pp 1\u0026ndash;14\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUrrego LE, Polan\u0026iacute;a J, Buitrago MF, Cuartas LF, Lema A (2009) Distribution of mangroves along environmental gradients on San Andres Island (Colombian Caribbean). Bull Mar Sci 85(1):27\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ingentaconnect.com/content/umrsmas/bullmar/2009/00000085/00000001/art00003\u003c/span\u003e\u003cspan address=\"https://www.ingentaconnect.com/content/umrsmas/bullmar/2009/00000085/00000001/art00003\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVega MJ, Alvarez-Silva O, Restrepo JC, Ortiz JC, Otero LJ (2020) Interannual variability of wave climate in the Caribbean Sea. Ocean Dyn 70:965\u0026ndash;976. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10236-020-01377-1\u003c/span\u003e\u003cspan address=\"10.1007/s10236-020-01377-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVilardy SP, Gonz\u0026aacute;lez JA, Mart\u0026iacute;n-L\u0026oacute;pez B, Montes C (2011) Relationships between hydrological regime and ecosystem services supply in a Caribbean coastal wetland: a social-ecological approach. Hydrol Sci J 56(8):1423\u0026ndash;1435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02626667.2011.631497\u003c/span\u003e\u003cspan address=\"10.1080/02626667.2011.631497\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWebb RM, G\u0026oacute;mez-G\u0026oacute;mez F (1998) Synoptic survey of water quality and bottom sediments, San Juan Bay estuary system, Puerto Rico, December 1994-July 1995. Water Resour Investigations Rep 974144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri974144\u003c/span\u003e\u003cspan address=\"10.3133/wri974144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang JJ, Jiao Y, Rhew RC, Chow AT (2016) Haloform formation in coastal wetlands along a salinity gradient at South Carolina, United States. Environ Chem 13(4):745\u0026ndash;756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/EN15145\u003c/span\u003e\u003cspan address=\"10.1071/EN15145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhite E, Kaplan D (2017) Restore or retreat? saltwater intrusion and water management in coastal wetlands. Ecosyst Health Sustain 2017 3(1):e01258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ehs2.1258\u003c/span\u003e\u003cspan address=\"10.1002/ehs2.1258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams CO, Lowrance R, Bosch DD, Williams JR, Benham E, Dieppa A, Hubbard R, Ma E, Potter T, Sotomayor D, Steglich EM, Strickland T, Williams RG (2013) Hydrology and water quality of a field and riparian buffer adjacent to a mangrove wetland in Jobos Bay watershed, Puerto Rico. Ecol Eng 56:60\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoleng.2012.09.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoleng.2012.09.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnited States Department of Agriculture (USDA) (2023) NRCS Web Soils Survey. Ci\u0026eacute;naga las Cucharillas. Available online: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://websoilsurvey.usda.gov/\u003c/span\u003e\u003cspan address=\"https://websoilsurvey.usda.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 16 June 2023\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"urban coastal wetland, Puerto Rico, terrestrial-marine connectivity, sub-surface saline intrusion, hydrological dynamics","lastPublishedDoi":"10.21203/rs.3.rs-6933337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6933337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study aimed to understand how the combined interactions of local weather variability, marine-terrestrial connectivity and/or anthropic modifications influence spatiotemporal hydrological dynamics and ionic concentrations of anthropically impacted urban coastal wetlands. Sampling was carried out in Ci\u0026eacute;naga Las Cucharillas Nature Reserve, a palustrine-estuarine wetland located in the northeastern of Puerto Rico with historical hydrological modifications. We conducted monthly sampling from 2018 to 2022 from ten monitoring wells at phreatic level and at 2.5 m depth. Water salinity, and ionic concentrations (Na, Mg, Ca, and K) were measured. Local climate regulated temporal variations in salinity and freshwater inputs, as well as phreatic levels. Extreme rainfall associated with atmospheric disturbances elevated phreatic levels above surface and homogenized salinities throughout the sampling site. A tridimensional hydrological mosaic was observed throughout the study area that stemmed from the deep sub-surface terrestrial-marine connectivity and the presence of natural subsurface channels connecting areas to the coastline. The wetland\u0026rsquo;s geomorphology, substrate composition, and water flow also contributed to the hydrological dynamics of the wetland as reflected in Mg/Ca and Na/K ratios. The present study provides a valuable framework for modeling impacted urban coastal wetlands. Future monitoring and management strategies should include groundwater salinity measurements, as the results indicate that is equally as important as phreatic salinity and may even obscure evidence of deep-subsurface marine intrusion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Spatiotemporal hydrological dynamics in the Caribbean Anthropocene: the case of a Puerto Rican coastal urban wetland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 11:33:19","doi":"10.21203/rs.3.rs-6933337/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-07-11T05:24:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Wetlands","date":"2025-07-04T12:12:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-24T07:25:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands","date":"2025-06-23T10:07:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"39a0088d-8881-4d9a-996b-4ed645736d2d","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T16:28:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 11:33:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6933337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6933337","identity":"rs-6933337","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}