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Tracing isotope precipitation patterns across Mexico

  • Ricardo Sánchez-Murillo ,

    Roles Data curation, Formal analysis, Investigation, Software, Writing – original draft

    Affiliation Department of Earth and Environmental Sciences, University of Texas, Arlington, Texas, United States of America

  • Luis González-Hita,

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

    Affiliation Department of Hydrology, Mexican Institute of Water Technology, Jiutepec, Mexico

  • Miguel A. Mejía-González,

    Roles Conceptualization, Data curation, Investigation, Methodology, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

    Affiliation Department of Hydrology, Mexican Institute of Water Technology, Jiutepec, Mexico

  • Blanca Carteño-Martinez,

    Roles Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

    Affiliation Department of Hydrology, Mexican Institute of Water Technology, Jiutepec, Mexico

  • Juan C. Aparicio-González,

    Roles Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

    Affiliation Department of Hydrology, Mexican Institute of Water Technology, Jiutepec, Mexico

  • Dustin Mañón-Flores,

    Roles Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

    Affiliation Department of Hydrology, Mexican Institute of Water Technology, Jiutepec, Mexico

  • Lucía Ortega,

    Roles Funding acquisition, Project administration, Resources, Writing – review & editing

    Affiliation International Atomic Energy Agency, Isotope Hydrology Section, Vienna International Center, Vienna, Austria

  • Milica Stojanovic,

    Roles Formal analysis, Methodology, Software, Validation, Visualization, Writing – review & editing

    Affiliation Centro de Investigación Mariña, Universidade de Vigo, Environmental Physics Laboratory (EPhysLab), Ourense, Spain

  • Raquel Nieto,

    Roles Data curation, Formal analysis, Methodology, Software, Supervision, Validation, Visualization, Writing – review & editing

    Affiliation Centro de Investigación Mariña, Universidade de Vigo, Environmental Physics Laboratory (EPhysLab), Ourense, Spain

  • Luis Gimeno

    Roles Methodology, Resources, Software, Writing – review & editing

    Affiliation Centro de Investigación Mariña, Universidade de Vigo, Environmental Physics Laboratory (EPhysLab), Ourense, Spain


Mexico encompasses a large spectrum of landscapes with topographic, geographic, and climatic factors interacting in a complex ecohydrological setting. For decades, isotope hydrogeological tools have been applied in Mexico using short-term or seasonal local meteoric water lines as valid input functions. Yet, a systematic evaluation of meteoric isotope characteristics is still lacking. Here we report on the spatial and temporal isotope variations of 21 precipitation monitoring stations across Mexico. Our database includes 608 monthly samples collected from 2018 to 2021 over four regions (between 5 and 2,365 m asl): the Pacific coast, the Gulf of Mexico/Caribbean Sea region, and the Central and Northern plateaus. Precipitation δ18O seasonality from the dry (winter) to the wet season (summer) was characterized by a notable W-shaped variability. Monthly precipitation amounts and δ18O compositions exhibited poor to strong linear regressions (Adj. r2<0.01 to 0.75), with inverse (positive) relationships over the northern monsoon-affected region. Low d-excess (5.1 to 9.7‰) corresponded with greater terrestrial moisture contributions (20.5%) over the arid northern regions. Moisture inputs from the Gulf of Mexico/Caribbean Sea and the Pacific Ocean were associated with near-equilibrium or greater d-excess values (8.8 to 14.3‰), respectively. The best-fit linear models for δ18O (Adj. r2 = 0.85) and δ2H (Adj. r2 = 0.88) were determined for topographic and geographical predictors, resulting in an updated high-resolution precipitation isoscape (100 m2 grid) for Mexico. Orographic barriers (-2.10‰ in δ18O/km) coupled with the interaction of tropical cyclones and cold fronts, the evolution of the North American Monsoon system, and the passage of easterly trade winds play a remarkable role in controlling the spatial isotope rainfall variability. Our findings provide a robust baseline for ecohydrological, climatic, forensic, archeological, and paleoclimate studies in North America.

1. Introduction

Historically, the use of water isotopes (δ2H, δ18O) in hydrogeological studies has been site-specific across Mexico, where 1–2 years or even seasonal local meteoric water lines (LMWLs) have often been used as valid input functions in central Mexico [16], in the northern arid landscapes [79], and in the southern coastal wet regions [1012] to underpin rainfall to groundwater and surface water connectivity.

For decades, however, most studies have relied on data archives of two stations from the Global Network of Isotopes in Precipitation [13] operated in Mexico from 1962 to 1988, one in the city of Chihuahua (northern arid region) and a second one in the city of Veracruz (southeastern wet region) [14]. The latter represents a relatively low number of monitoring stations for such a large and heterogeneous country, with abundant rain in the south and scarce precipitation in the north, with rain forests but also with vast deserts, tropical and mid-latitude climates, with coastlines facing the Pacific Ocean, the Gulf of Mexico and the Gulf of California, and the Caribbean Sea, and with an enormous plateau bounded by two mountain ranges where ice caps are still present [15]. Similarly, Mexico is affected by multiple climatic features, such as the influence of cold fronts [16], atmospheric rivers [17], easterly waves [18], tropical cyclones [19], and northerly trade winds. All these topographic, geographic, and weather characteristics are represented in a large spectrum of climatic regions across Mexico [20].

While numerous catchment-to-basin-scale studies have been conducted across Mexico, only four isotopic geospatial efforts [21] have been reported. Wassenaar et al. [22] interpolated groundwater isotopic values based on the premise that the water isotope ratios of shallow groundwater (N = 234 at near 50 km latitudinal spacing) can be used as a proxy of the stable isotopic composition of long-term seasonally weighted precipitation. This product was later used to construct a feather hydrogen isoscape (i.e., migrant bird isoscape based on feather deuterium compositions) for Mexico [23]. Ammer et al. [24] used 158 tap water samples from 51 towns and cities collected throughout Mexico for six weeks from June to July 2018 to infer the region of origin of unidentified border crossers between Mexico and the United States. Lately, Fan et al. [25] studied δ18O, δ2H, and d-excess (N = 205) in surface water collected in wet and dry seasons from the west to the east coast of Mexico between 22°N and 26°N to understand the controlling factors of isotopic patterns and lapse rates. Nevertheless, a systematic and regional evaluation of isotope meteoric characteristics across Mexico is still lacking.

Here we report on the spatial and temporal isotope variations of 21 precipitation monitoring stations across different physiographic units of Mexico ( These stations are part of the National Network of Isotopes in Precipitation (RENIP), operated by the Department of Hydrology of the Mexican Institute of Water Technology (IMTA). Our database includes 608 monthly samples collected from 2018 to 2021 over four regions: the Pacific coast (PC), the Gulf of Mexico and the Caribbean Sea coast (GCS), and the Central (CP) and Northern plateaus (NP). Monitoring sites are located between 5 and 2,365 m asl with distinct topographic, geographic, and weather characteristics. The main goal of our study is to evaluate the spatial and temporal isotopic variability in precipitation across Mexico. This will be complemented with an atmospheric analysis of the trajectories that reach the region using the FLEXPART Lagrangian model [as in 26]. This analysis differentiates between the monthly contribution of moisture sources from oceanic and terrestrial domains. Emergent relationships resulted in updated precipitation isoscapes (δ18O, δ2H, d-excess) for Mexico. Our spatial and temporal analysis should also serve as a baseline for ecohydrological, climatic, forensic, archeological, and paleoclimate studies in North America.

2. Topographic features

Mexico is part of the northern hemisphere, ranging from 14°30’N to 32°43’N (Fig 1). A fraction of the territory is located within the subtropical zone (north of the Tropic of Cancer), while the southern portion is part of the tropics [15]. A notable orographic feature is an enormous plateau called the Mexican Plateau, bounded to the west by the Sierra Madre Occidental and to the east by the Sierra Madre Oriental [27]. This plateau intercepts the westerly winds that are characteristic of the middle latitudes. Due to their low humidity, these winds produce the characteristic dryness that prevails during the cold season.

Fig 1. Map of Mexico including mean annual precipitation (in mm) (1981–2019; based on CHIRPS data) [46], isotope monitoring sites (monthly sampling frequency; pink circles, dotted), and physiographic regions (dashed-line polygons; obtained from

The number of monitoring sites per physiographic region ranged from 1 to 5.

The Sierra Madre Oriental is a mountain range approximately 1,350 km long and 80 to 100 km wide, extending from the south of the Rio Bravo, parallel to the Gulf of Mexico, to the Neovolcanic Axis, which separates North America from Central America [28]. Headwater elevation oscillates between 2,000 and 3,000 m asl in this mountain range. Under favorable synoptic-scale flow [29], cold winter surges propagate from North America deep into the tropics bounded by the eastern side of the Sierra Madre Oriental [3032]. The Isthmus of Tehuantepec, located in southeastern Mexico, is a narrow region that separates the Gulf of Mexico from the Pacific Ocean. The Sierra Madre del Sur range has an average altitude of 2,000 m asl, but on the Isthmus of Tehuantepec, the elevation drops to 250 m, forming a gap approximately 40 km wide and 220 km long, known as ‘Paso Chivela.’ It is through this gap that cold fronts sometimes leak into the Pacific Ocean.

Another important mountain range is the Sierra Madre Occidental, which extends over 1,400 km from the northern border to the central part of the country, bordering the Pacific Ocean [28]. With an average altitude of 2,000 m asl, it reaches maximum altitudes of 3,300 m in Chihuahua [28]. When atmospheric rivers from the western Pacific Ocean influence the northwestern region, water vapor rises over the Sierra Madre Occidental, generating abundant precipitation [33, 34].

The main physiographic component of central Mexico is the ‘Mesa Central’ or Central Plateau, a region that encompasses the Trans-Mexican Volcanic Belt. This region is bordered to the south by the Balsas Depression, to the east by the Sierra Madre Oriental, to the west by the Sierra Madre Occidental, and to the north by Zacatecas. This region comprises several volcanoes such as Pico de Orizaba (5,363 m asl), Popocatepetl (5,426 m asl), Nevado de Toluca (4,690 m asl), Colima (4,330 m asl), and Cofre de Perote (4,282 m asl). The high relief is one of the main factors influencing the distribution of precipitation in this region, from values greater than 2,500 mm (windward) to 500 mm (leeward) [35] (Fig 1).

The southeast region includes the Yucatan Peninsula with an elevation below 300 m asl [15] surrounded by the Gulf of Mexico and the Tabasqueña lowlands. On the Pacific slope of this region, there is a low area of 600 to 900 m asl, and to the north of this depression, the Central Plateau of Chiapas runs to the coastal plain of the Gulf of Mexico [15].

3. Climatic and meteorological characteristics

Among the climatic and meteorological components that influence the annual precipitation cycle in Mexico are the North American high (NAH), the North Atlantic subtropical high (NASH), the North American Monsoon System (NAMS), cold fronts, atmospheric rivers, easterly waves, and tropical cyclones. The NAH is a center of high atmospheric pressure across most of North America during the boreal winter. Cold and dry air masses flow from the NAH, causing cold fronts in the region (September to May) [16]. Depending on the atmospheric conditions, the convergence of the cold fronts with moisture advected from the Gulf of Mexico and the Pacific Ocean could result in rainfall events.

High vertically integrated water vapor bands are formed at the convergence zone of the cold fronts and the warm conveyor belt of extratropical systems [36]. Such moisture bands, called atmospheric rivers (ARs), are formed over the oceans and are typically associated with extratropical cyclones, covering long (thousands of kilometers) and narrow (hundreds of kilometers) transient water vapor corridors [37]. The influence of ARs is relevant in northwestern Mexico, whereby rainfall is produced via this mechanism during the winter [33, 34].

During the summer, the air over the continent warms up more than the air over the ocean, producing a pressure gradient between the continent (low pressure) and the ocean (high pressure). The pressure gradient induces wind currents transporting moisture from the ocean to the continent. The moisture warmed by the terrestrial heat flux is lifted in a strong upward air motion to high altitudes, producing convective rain (i.e., NAMS) [38]. Convective rain provides a substantial fraction of the total annual precipitation in northwestern Mexico (60%-80%) [39].

The NASH is a center of high atmospheric pressure in the Atlantic Ocean, located around 30°N, which varies in extension and intensity during the year [40]. During the winter, it weakens and contracts, and by early May, it begins to strengthen and expand, continuing until the end of July [41]. The winds produced by the NASH, called northerly trade winds, transport moisture from the Caribbean Sea and the Gulf of Mexico towards Central America, Mexico, and the southeastern United States [40].

During the summer, the northerly trade winds converge with the southerly trade winds in the tropical portion of Mexico [42], generating updrafts that condense the moisture and produce abundant rainfall [43]. Another important source of precipitation in the tropics of Mexico is the easterly waves (EWs). They are inverted troughs of low pressure, propagating from east to west (i.e., across the Atlantic Ocean and originated near the western coast of Africa) from May to November, with lengths of 2,000–2,500 km, and within latitudes 5o and 20o N. Easterly waves are highly convective systems, producing at least 20% of southern Mexico’s precipitation [18]. In addition, Mexico is located between the cyclogenetic regions of the northern Atlantic Ocean and the northeastern Pacific Ocean, receiving abundant rains from tropical cyclones (TCs) during the summer. On average, five tropical cyclones enter the national territory yearly [44]. Mexico receives up to 20% of its total annual rainfall from TCs [19].

4. Materials and methods

4.1 Data collection

Monthly precipitation samples (N = 608) in 21 stations (Fig 1) were collected between 2018 to 2021 using passive precipitation collectors (RS2 Palmex, Croatia) [45]. This type of collector prevents secondary evaporation by using the principle of minimum exposure of the collected water surface area to the atmosphere [See 45 for more details]. Samples were filtered using cellulose membranes with 0.45 μm pore size, collected in 60 mL high-density polyethylene bottles with conic and poly seal inserts, filled with no headspace, and stored at 5°C until analysis. Rainfall amounts (mm) were calculated based on the volume of rain collected and the funnel’s diameter. Collectors were installed at the facilities of the National Meteorological Service of the National Water Commission. The network was designed to cover all 15 and 13 physiographic provinces and precipitation zones, respectively (Fig 1).

4.2. Stable isotopes analysis

Stable isotope analyses were conducted at the Isotope Hydrology Laboratory of the Mexican Institute of Water Technology (IMTA), using laser water isotope analyzers Picarro L2110-i and Los Gatos Research LWIA-45EP (S1 Table; also available from Hydroshare at [47]). The long-term analytical precision was ±0.5‰ for δ2H and ±0.13‰ for δ18O. The secondary standards were Popocatepetl Volcano Water, PVW (δ2H = - 81.4‰, δ18O = - 11.79‰), Laguna Verde Water, LVW (δ2H = +17.1‰, δ18O = +3.38‰), and Playa del Carmen Well Water, PCWW (δ2H = -28‰, δ18O = -4.8‰). PVW and LVW standards were used to normalize the results to the VSMOW2-SLAP2 scale, while PCWW was used as a quality control and drift control standard. A least-squares regression of the isotope data was used to obtain the local meteoric water lines. Deuterium excess (d-ex = δ2H-8*δ18O) was used to evaluate secondary evaporation and moisture recycling [48]. Precipitation-weighted (δw) mean values were calculated as δw = ΣPii/PT, where Pi is the precipitation amount, δi is the isotope composition (δ2H or δ18O) of the sample, and PT is the total precipitation amount [49]. Precipitation-weighted d-excess values were also estimated.

4.3. Isoscape modeling and statistical analysis

Isoscapes (100 m2 grid resolution) were generated using precipitation-weighted δ18O and δ2H data (Table 1). The best-fit regressions were determined on topographic (i.e., elevation-Elv) and geographical (i.e., latitude-Lat and longitude-long) regressors following similar isoscape procedures [50, 51]. The regression equations used as suitable predictions for the annual mean δ18O and δ2H in Mexico are: (1) (2)

Table 1. Summary including site name, geographic location (latitude/longitude), elevation, arithmetic and precipitation-weighted (w-subscript) means (δ18O, δ2H, and d-excess), and region classification.

All linear regressions and statistical diagnostics were computed using R [52]. Raster calculations were performed in ArcGIS 10.8.1 (ESRI, USA) using a 10m digital elevation model, and latitude/longitude rasters. Mean annual δ18O and δ2H (‰) predicted residuals were evaluated against a) the observed values and B) a global isoscape product [53, 54] (S2 Table; also available from Hydroshare at [47]. Open access base maps were obtained from ESRI (world ocean base; and Nature Earth (coastlines;

4.4. Budyko framework

To facilitate the understanding of regional water partitioning differences and similarities across the monitoring regions, we computed a representative Budyko framework [55] for the main physiographic units across Mexico. We used regional actual and potential evapotranspiration values (AET and PET, respectively) and precipitation records (P) from previous studies [56]. The physiographic regions are also contextualized within the global Budyko curve (ω = 2.6) [57, 58].

4.5. Computation of monthly moisture source contributions

The isotope monitoring stations were grouped in six moisture sink regions (i.e., Central and Northern plateaus, North and South Pacific, and Northern and Southern Gulf), based on the precipitation patterns (Fig 1) and previous moisture source tracking studies over Mexico and the southwestern USA [5962]. Moisture sinks refer to air parcels that lose humidity through, for instance, precipitation processes.

S1 Fig shows the experimental regional setting for the FLEXPART calculations. The air masses residing over each of the six study regions were tracked backward in time using the FLEXible PARTicle dispersion model (FLEXPART) v9.0 [63, 64] and by moving the particles (atmospheric air masses) considering the optimum integration time was found to be 5 days prior to the time that the precipitation event ended [65]. In this experiment, FLEXPART computes trajectories of nearly 2 million particles with a horizontal resolution of 0.25º, along with the changes of specific humidity (dq), which is calculated for each parcel, based on the budget of the evaporation (E) minus precipitation (P). ERA-Interim wind fields are used to model the positions of the particles and thus the air mass advection. Integrating dq over the vertical column with 61 vertical levels from the surface to 0.1 hPa permits estimating the surface freshwater balance [66]. Along the trajectories, the moisture gained by air masses was computed to determine the sources of moisture.

The sources were calculated by the net positive values of the evaporation minus precipitation budget (E-P>0; where evaporation exceeds precipitation) for the parcels along the vertical column. Four individual sources (i.e., terrestrial, the Pacific Ocean, the Gulf of Mexico, and the Caribbean Sea) were identified. Then, a forward-in-time analysis was performed to compute their moisture contribution to precipitation over each study region, which is assumed as the negative values on the budget of the evaporation minus precipitation (E-P<0; where precipitation exceeds evaporation). This approach has been widely utilized in several previous studies following the methodology designed by [63, 64, 67] for several regions of the world [65, 68, 69], confirming its reliability for the source-sink of atmospheric moisture assessment, and its implication in the hydrological cycle at local, regional, and global scales.

5. Results

5.1. Hydrological framework across Mexico

Moisture recycling and sub-cloud evaporation play a relevant regional and local role in the configuration of meteoric water lines worldwide [70, 71]. Water partitioning, and in particular, the transpiration flux is of interest since it accounts for between 60–90% of global terrestrial evapotranspiration [72, 73]. Fig 2 shows a representative Budyko framework for the main physiographic regions of Mexico. The Chiapas ranges, the southern Gulf coastal lowlands, and the southern Sierra Madre are represented by an aridity ratio<1, meaning precipitation exceeds the evaporative demand (PET), indicating energy-limited areas with substantial runoff [74]. This is typical of humid coastal and tropical high-elevation mountains [75]. Regions such as the northern Gulf lowlands, eastern and western Sierra Madre, and the Neovolcanic axis exhibited an aridity ratio between 1 and 2, with trends towards less runoff and more arid conditions [76]. The Central Plateau is in a transitional Budyko space towards more arid conditions. Conversely, the northern lowlands and ranges, the Sonora and North America plains, the Pacific coast lowlands, and the Baja California peninsula denote a trend in the direction of arid regions with more evaporative demand (PET) than precipitation (>1), a common feature of low elevation landscapes in northern Mexico. Such characteristics are further discussed in section 5.5. from a moisture source perspective.

Fig 2. Budyko dual-space framework for the main physiographic regions (color coded).

The X axis is the ratio of potential evapotranspiration (PET) to precipitation (P) (dryness index), and the Y axis represents the actual evapotranspiration (AET) over P (evaporative index). The blue line shows the ‘Budyko curve’ defined by ω = 2.6 [57, 58]. Grey solid lines define the energy and water limits.

5.2. Seasonal precipitation and isotopic patterns

Mexico exhibits a monsoonal climate, with a rainy season during the summer months (June-September) and a relatively dry season in winter (Fig 3A; Table 1), with fluctuations linked to cold fronts, tropical cyclones (landfall and passages), monsoonal rainfall (e.g., NAMS), and easterly waves passages [77]. Monthly precipitation along the GCS region and to a smaller extent in the CP revealed a bimodal distribution with two maxima in June and September (Fig 3A). During the summer (June-September), trade winds from the north (moisture from the Gulf of Mexico) and trade winds from the south (moisture from the Pacific Ocean) converge in the tropics of Mexico, generating abundant precipitation [43]. The GCS domain also received abundant precipitation in July-October due to the activation of the Atlantic hurricane season. Both regions experienced a precipitation decrease from November to April. In the PC region, precipitation increased from June to September due to the direct/indirect effect of tropical cyclones from the eastern Pacific Ocean and the activation of the NAMS. In the NP region, a precipitation maximum was observed between July and September, largely governed by the NAMS (60–80% of total annual precipitation) [78, 79].

Fig 3.

Monthly box plots of (a) precipitation (mm), (b) δ18O (‰), and (c) d-excess (‰) for all monitoring sites in the Central Plateau (CP), the Gulf of Mexico and the Caribbean Sea coast (GCS), the Northern Plateau (NP), and the Pacific coast (PC).

Precipitation δ18O (similar patterns are observed in δ2H) seasonality across Mexico from the dry to the wet season was characterized by a notable W-shaped variability (Fig 3B). This pattern is similar to the observed intra-seasonal variability of Central American rainfall, which typically results in two or three depletions during the wet season (boreal summer) and more enriched values during the strongest trade winds period (boreal winter) [50]. Across the GCS domain (windward slope), median δ18O values varied between -7.0 (June) and 0.0‰ (Dec-Apr) with a more attenuated W-trend as a result of the incipient orographic distillation (proximity to the coast) [22] (Fig 3B). During the winter season, median δ18O values are nearly uniform within the GCS domain (ranging from -2.5 to -1.0 ‰). In contrast, strong orographic distillation within central Mexico is denoted by the most depleted δ18O compositions (up to -20‰) during the summer (wet season) (Fig 3B). The Pacific domain was characterized by a similar trend with median values near -10‰ in δ18O. In the NP, depleted values were observed during the winter season (Jan-Feb), whereas more enriched values occurred during the influence of the NAMS (June-September) (Fig 3B) [80, 81].

Consistently, the lowest d-excess values were reported in northern Mexico (Fig 3C). Across this arid region (Fig 2; water-limited), a larger temperature seasonality, low relative humidity, and small rainfall events resulted in d-excess values as low as -12‰. In this region, February-March (dry season; cold fronts) and September (monsoon) exhibited the largest d-excess variability (from -10 up to 20‰) among all sites. The PC sites also resulted in low d-excess values but with a remarkably reduced variability (Fig 3C), indicating a potential constant moisture source from the Pacific Ocean. In general, the wet season (boreal summer) was characterized by a nearly uniform d-excess around 10‰ (CP, PC, and GCS domains) and greater values and variability during the winter season (Fig 3C).

5.3. Regional precipitation amount and isotope relationships

Recent studies across tropical and subtropical regions have shown that condensation levels, weather types, and stratiform fractions better predict the precipitation isotope composition than the classical precipitation amount [8285]. This can be observed in the inherent complexity of monthly precipitation relationships across Mexico (Fig 4). For example, in northern Mexico (Northern Plateau), positive regressions between precipitation amount and δ18O are a characteristic feature of NAMS-dominated regions, ranging from +0.4 up to +2.3‰ per 100 mm of precipitation (Fig 4). In low-elevation PC sites, poor regressions were reported (-0.01 to +0.2‰ per 100 mm; r2 = 0.01–0.23), except for the Chilpancingo site (-0.8‰ per 100 mm; r2 = 0.69), located in the southern Pacific coast and above 1,000 m asl. A tropical cyclone bias was also detected in the North Pacific lowland stations (e.g., Culiacan and Loreto). The strongest amount effect was exhibited in central Mexico (-1.0 to -2.2‰ per 100 mm; r2 = 0.31–0.70) and the GCS domain (-0.4 to -1.1‰ per 100 mm; r2 = 0.55–0.75). Monterrey (-0.4‰ per 100 mm; r2 = 0.15) and Villahermosa (-0.2‰ per 100 mm; r2 = 0.01) sites resulted in weak regressions.

Fig 4. Monthly precipitation amount and δ18O (‰) relationship per site across Mexico.

Regions are color-coded.

The strongest isotope lapse rate was reported within the windward slope (over the Sierra Madre Oriental) of the Gulf of Mexico and the Caribbean Sea, -2.6‰ in δ18O per km of elevation (Adj. r2 = 0.58, p = 0.030) (Fig 5). This lapse rate agrees with earlier reported values in rainfall, groundwater, and surface water within the Caribbean Sea slope of Costa Rica (windward over the central Cordillera), -2.5‰ in δ18O per km of elevation [54]. In the CP, the isotopic lapse rate is weaker, -0.90‰ in δ18O per km of elevation (Adj. r2 = 0.11, p = 0.657). In the PC and NP, moderate isotopic lapse rates ranged from -1.11‰ (Adj. r2 = 0.73, p = 0.141) to -1.74‰ (Adj. r2 = 0.45, p = 0.211) in δ18O per km of elevation, respectively. The nationwide isotopic lapse rate (including monitoring sites influenced by TCs; Loreto, Culiacan, and Hermosillo) corresponds to -1.75‰ in δ18O/km (Adj. r2 = 0.65, p-value <0.001). The direct influence of TCs passages and landfalls across the Pacific coast resulted in depleted monthly compositions in low-elevation coastal sites during the study period. The precipitation-weighted isotope lapse rate (including all sites) resulted in a similar slope -2.10‰ in δ18O/km (Adj.r2 = 0.72, p-value <0.001). The lapse rate obtained in the isoscape modeling is -1.72 δ18O/km. No significant trend was found between d-excess and elevation (Fig 5). Overall, monitoring sites along the Gulf of Mexico and Caribbean Sea basins resulted in greater d-excess values, whereas high-elevation sites in the Northern Plateau exhibited lower d-excess values.

Fig 5. Elevation (m asl), δ18O (‰), and d-excess (‰) relationship across Mexico.

Regional isotopic lapse rates are color-coded and range from -0.90 to -2.60 ‰ in δ18O per km.

5.4. Regional meteoric water lines

Fig 6 shows dual isotope diagrams for all monitoring sites. The arid conditions in the NP (Fig 2) resulted in the lowest intercepts and slopes, ranging from 0.92 to 1.60 and from 5.1 to 7.4, respectively. Overall, the rainfall isotopic composition within the NP agrees with previous LMWLs described for Chihuahua (northern Mexico), Tucson, Arizona [80, 81], and the Sonora River basin [8]. In the GCS domain, isotope compositions were right skewed toward more enriched compositions. However, sporadic depleted monthly δ18O compositions (< -10‰) were reported in Monterrey, Villahermosa, and Tuxtla Gutierrez. Within this region, LMWLs are characterized by slopes close to the GMWL and relatively high intercepts (11.2 to 14.0). The latter is a common feature of monitoring sites across the Caribbean coast of Central America [50, 54]. Intercepts below the global mean (10‰) were reported in Tuxtla Gutierrez (9.4) and Merida (8.7). In the CP, LMWLs represent equilibrium conditions with slopes (7.6 to 8.0) and intercepts (9.9 to 10.2) near the GMWL. In this region, Queretaro exhibited a relatively lower intercept (6.8). The CP received depleted precipitation events below -15‰ in δ18O because of strong orographic effects during convective summer precipitation events. The PC domain is characterized by moderate slopes (7.2 to 7.9), but relatively low intercepts (5.8 to 8.7), reflecting the arid conditions of the northern Pacific domain (Loreto and Culiacan; Fig 2).

Fig 6. Local meteoric water lines across Mexico between 2018–2021.

Regions are color-coded.

5.5. Regional moisture sources

Fig 7A shows the location of six study regions (pink boxes) and their major annual climatological sources of moisture (E-P>0; in mm d-1). To identify the sources, the air masses residing over each of the study regions were tracked backward in time from 1980–2018. Areas shaded by reddish colors in the (E-P) pattern represent regions where evaporation exceeded precipitation in the net moisture budget (E-P > 0) and denote the most relevant moisture sources throughout the hydrological year. According to this, parts of the Gulf of Mexico, the Caribbean Sea, the Pacific Ocean, and also terrestrial regions are identified as the main sources of moisture contributors to the precipitation over the Mexican target regions. Afterward, a forward-in-time analysis of air masses residing over the sources was performed to compute their contribution to the precipitation over each study region. This contribution is assumed as the negative values on a budget of the evaporation minus precipitation (E-P<0; where precipitation exceeds evaporation). Fig 7B represents the long-term annual climatological precipitation cycle (mm/month) (1980–2018) for each of the six sink regions across Mexico and the overall moisture source contribution from each domain. In the Northern Plateau, the NAMS is clearly depicted by greater precipitation amounts between June and September, with the largest terrestrial moisture input (20.5%) [60, 86]. In the North Pacific region, the combination of tropical storms, monsoonal rainfall, and terrestrial sources governed precipitation inputs mainly between May and November [8, 87]. The Northern Gulf region exhibited nearly uniform moisture contributions from the Pacific Ocean and terrestrial sources throughout the year, and a major input from the Gulf of Mexico basin (38.7%) from May to October [60, 88]. In the Central Plateau, where the City of Mexico is located, the bimodal precipitation cycle is constrained by a major moisture contribution from the Gulf of Mexico (44.2%), and similar inputs from the Pacific Ocean (27.0%) and the Caribbean basin (27.1%). In the wettest southern lowland region, moisture sources from the Pacific Ocean (33.4%) and the Gulf of Mexico (39.1%) play a predominant role in rainfall generation, with less influence from the Caribbean basin (16.4%) [68]. A similar pattern is observed for the South Pacific domain with minimal terrestrial inputs and large oceanic contributions.

Fig 7.

Atmospheric moisture source-sink analysis: A) Spatial distribution of annual moisture source (E-P>0) (in mm d-1) for the study regions (pink polygons). Red areas denote moisture sources. B) Climatological annual cycle of precipitation (mm/month) per region: i) Central Plateau, ii) North Pacific coast, iii) Northern Gulf coast, iv) Northern Plateau, v) South Pacific coast, and vi) Southern Gulf coast. Moisture sources are color-coded: Caribbean basin (yellow), Gulf of Mexico (green), Pacific Ocean (cyan), and terrestrial (blue). The pie charts show the moisture source contribution to the annual precipitation cycle. Period of study: 1980–2018. Open access base maps were obtained from Nature Earth (coastlines;

5.6. Isoscape models

Isoscape models based on topographic and geographic regressors accurately captured the isotopic variability across the complex physiographic units of Mexico (Fig 8). Depleted values are distributed across both main cordilleras and southernmost ranges, while enriched values are commonly found across the coastal domains and northern regions. The δ18O isoscape ranged from -13.39 to -2.75‰ (Fig 8A), whereas δ2H varied from -96.83 to -9.89‰ (Fig 8D). Residual analysis denoted a strong agreement with the observed isotope ratios (Adj. r2 = 0.84 for δ18O and Adj. r2 = 0.86 for δ2H; Fig 8C and 8F). Previous global isoscape products [53] based on absolute latitude and elevation (i.e., temperature-driven effects) regressors (Fig 8B and 8E) exhibited a slightly lower agreement with the observed isotope ratios (Adj. r2 = 0.77 for δ18O and Adj. r2 = 0.77 for δ2H; Fig 8C and 8F). The latter might be explained by the coarser resolution of the global grid [53] compared to the updated isoscapes presented in this study (100 m2 grid). However, this global product reported more enriched compositions across the Pacific coast and the Gulf of California than the observed values. The spatial d-excess variability was computed based on δ18O and δ2H raster outputs (Fig 9). In this study, d-excess varied from 8.35‰ (northern regions) to 13.22‰ (southern regions), whereas the global product varied from -4.78‰ (northern regions) to 17.87‰ (Central and Northern plateaus) (Fig 9A and 9B). Overall, both models performed poorly compared to the precipitation-weighted d-excess values (Fig 9C), highlighting the need to constrain terrestrial moisture inputs in the evolution of precipitation systems across Mexico [89].

Fig 8.

Mean annual isoscape models for δ18O and δ2H (this study; A and D) versus a global product (B and E) from [53]. Panels C and F show the goodness-of-fit between observed (precipitation-weighted) and predicted δ18O/δ2H values. Isoscape models based on topographic (elevation) and geographical predictors (latitude and longitude) (orange squares) resulted in an overall improvement (δ18O; Adj. r2 = 0.84 and δ2H; Adj. r2 = 0.86) when compared to global products (gray-dots) based on absolute latitude and elevation (i.e., temperature-driven effects) regressors. Open access base maps were obtained from ESRI (world ocean base;

Fig 9.

Mean annual isoscape model for d-excess (this study; A) versus a global product (B) from [53]. Panel C shows the goodness-of-fit between observed (precipitation-weighted) and predicted d-excess values. Both models exhibited poor performance when compared to precipitation-weighted d-excess values across Mexico. Open access base maps were obtained from ESRI (world ocean base;

6. Discussion

6.1. Spatial isotopic variations in precipitation across Mexico

Isotope variations in meteoric water are a product of the interactions between topography (e.g., mountain ranges, depressions, inter-mountainous valleys), vapor transport (e.g., Pacific Ocean, Gulf of Mexico, Caribbean Sea, and terrestrial sources), sub-cloud evaporation, and the influence of recycled moisture (i.e., evapotranspiration) (Fig 2) [9092]. These features interplay in a complex array of physiographic units across Mexico, resulting in complicated spatial isotope patterns (Figs 8 and 9).

Tropical cyclones contribute up to 40% to the annual rainfall in the coastal regions of northwestern Mexico (e.g., Loreto and Hermosillo; Fig 1) [19], whereas annual contributions range between 20–30% along the Gulf of Mexico coast. During active hurricane seasons, these storms can result in abundant and enriched rainfall in the Gulf of Mexico and Caribbean Sea coast and in depleted compositions inland (i.e., orographic distillation) and along the northwestern Pacific coast [8, 93]. Cold fronts and atmospheric rivers are also responsible for depleted isotopic compositions across the northwestern region, with δ18O down to -15‰ during the boreal winter in the Northern Plateau (Fig 3B). The NAMS activation commonly results in large precipitation events, highly localized, and enriched across the northwestern and north central regions of Mexico [22, 80, 81]. The latter is clearly depicted by the inverse (positive) precipitation amount relationships (Fig 4). In contrast, the northerly trade winds and easterly waves result in enriched compositions along the Gulf of Mexico and Yucatan peninsula [10, 12]. The convergence of the northerly trade winds with the southerly trade winds across orographic barriers results in depleted compositions during the summer over central Mexico (Fig 3B).

The stronger rainout effect (i.e., a progressive isotope depletion in precipitation with increasing distance from the ocean) observed over the GCS domain is most likely related to the direct influence of the trade winds and nearby moisture transport from the Gulf of Mexico and the Caribbean Sea basins (Fig 6). In the intra-mountainous plateaus and the Pacific slope, the combination of the rain shadow effect (i.e., an area of significantly reduced precipitation behind a mountainous region), more complex and rugged topography, recycled evapotranspiration, deep convective activity (June-September), and indirect influence of tropical cyclones results in weaker and more complex spatial trends [71]. In this regard, the isotopic bias introduced in low-elevation coastal stations affected by tropical storms within the Mesoamerican region should be considered when determining isotopic lapse rates for hydrogeological applications (e.g., mean recharge elevations). For instance, low-elevation sites (i.e., Loreto, Culiacan, and Hermosillo; Fig 1) across the northern Pacific coast were affected by strong hurricane landfalls and passages during the monitoring period (e.g., hurricanes Rosa-2018 and Sergio-2018, Lorena-2019, Nora-2021, and Pamela-2021, among other tropical storms). Remarkably, [8] reported a relatively weak isotope lapse rate within the Sonora River basin (PC to NP) during the wet season (-1.1‰ in δ18O per km of elevation) but noted that the isotope lapse rate increased (intra-seasonally) due to tropical cyclone events (-2.6‰ in δ18O per km of elevation; Adj. r2 = 0.86; p<0.001).

In Mexico, the nationwide isotopic lapse rate (based on arithmetic means) can be defined as -1.75‰ in δ18O per km of elevation (Adj. r2 = 0.65, p<0.001; Fig 5), whereas the precipitation-weighted lapse rate can be described as -2.10‰ in δ18O per km of elevation (72% of the total variance explained, p<0.001; Fig 5). The lapse rate obtained in the isoscape modeling resulted in -1.72 δ18O/km.

Overall, the reported isotope lapse rates agree with previous studies (-2.3‰/km) based on a combined approach of regressing the mean δ18O from GNIP stations against geographical and climatic regressors and applying the resulting function onto gridded climate data [50]. For instance, [71] reported a pantropical spectrum of isotopic lapse rates ranging from -3.5 to -0.5‰/km, with a pantropical mean of -2.2‰/km. Similarly, [92] reported a global isotopic lapse rate of -2.8‰/km. A previous groundwater isoscape effort in Mexico [22] evaluated 19 linear regression models, from a single variable model (elevation) to more complex iterations including drainage or slope (Pacific versus Gulf of Mexico and the Caribbean Sea basins), distance to coast, latitude, annual precipitation, and interaction among these variables. In the best model, elevation explained only 44% of the total variance in groundwater samples, while precipitation amount (10%), slope (Pacific versus GCS) (12%), and latitude (10%) explained a third of the total variance. Landscape damping effects in transitional regions from tropical to arid sub-tropical biomes can explain the relatively weaker elevation relationship in groundwater (e.g., secondary soil evaporation). In Costa Rica and Guatemala/Belize, [54, 94] reported similar lapse rates in precipitation, groundwater, and surface water (-2.5% in δ18O per km) across the wet Caribbean slope, whereas weaker relationships have been reported across the dry Corridor of Central America (-1.0% in δ18O per km) [50].

The updated spatial and temporal coverage of the Mexican isotope network (i.e., RENIP) facilitated a more robust representation of the meteoric isotope variability. In our model, 85% (δ18O) and 88% (δ2H) of the total variance is explained by topographic (elevation) and geographical regressors (latitude and longitude), with δ18O and δ2H residuals varying from +2.0‰ (Ciudad Victoria; northern region) to -1.2‰ (Tulancingo; central region). On average, the predicted mean annual δ18O compositions were more depleted across the central (0.04‰) and northern (0.27‰) plateaus, except for Hermosillo (coastal site affected by tropical storms) (more enriched by 0.58‰) and Pachuca (high elevation site; 2,365 m asl) (more enriched by 1.06‰) sites. The larger residual variability was observed across the Gulf of Mexico and Caribbean Sea sites with residuals ranging from 2.0‰ (Ciudad Victoria; northern coast) to -0.90‰ (Mérida; southern coast). In contrast, the predicted mean annual δ18O compositions across the Pacific coast sites were consistently under-predicted on average by -0.48‰, indicating the tropical cyclone bias affecting this region.

7. Conclusions

This synthesis of stable isotope compositions in precipitation across distinct landscapes of Mexico shows how complex atmospheric processes and moisture contributions from terrestrial and maritime (the Pacific Ocean, Gulf of Mexico, and the Caribbean Sea basin) domains interplay with topographic features, resulting in isotopic spatial and temporal variations throughout the hydrological year. In this regard, water vapor sampling or high-frequency discrete samples [95, 96] are still needed to clearly separate the role of terrestrial (e.g., local and remote transpiration, ground evaporation, and canopy evaporation) [89] versus oceanic moisture in controlling the observed spatial isotope variability, with particular interest across the north Pacific coast and Northern Plateau region.

The inherent complexity of observed monthly precipitation relationships across Mexico invokes the need to underpin the local (e.g., precipitable water column) and regional weather processes governing the spatial trends. The latter also precludes using the classical amount effect as a valid regressor. In the northern regions, terrestrial and maritime moisture sources resulted in enriched isotope compositions during the monsoon season. The latter is further evidenced by inverse (positive) precipitation amount and δ18O relationship in Torreon, Hermosillo, Piedras Negras, Durango, and Chihuahua (+0.4 to +2.3‰ in δ18O/100 mm). Windward (Gulf of Mexico coast) and high mountainous regions exhibited moderate to strong effects (-0.2 to -2.2‰ in δ18O/100 mm). Lowland arid regions across the north Pacific resulted in poor relationships (-0.8 to +0.01‰ in δ18O/100 mm) mostly affected by the indirect effect of tropical cyclones. In the central, southern Gulf coast, and south Pacific regions oceanic moisture sources were dominant (Gulf of Mexico>Pacific Ocean>Caribbean Sea). On the northeastern coast, moisture from the Gulf of Mexico predominated as the main contributor.

The first attempt at building a high-resolution (100 m2 grid) nationwide rainfall isoscape highlighted areas with significant spatial variability such as the Pacific coast and the northern arid regions, which highlights the need for additional monitoring efforts (per region) focused on the temporal bias produced by the indirect or direct effect of tropical cyclones, monsoon activity, and evapotranspiration moisture inputs. Our results fill a recognized historical gap in the precipitation isotope monitoring in North America and will provide a baseline to pursue more detailed ecohydrological, climatic, forensic, archeological, and paleoclimate studies across Mexico.

Supporting information

S1 Fig. Regional source of atmospheric moisture considered in the experimental setting for the FLEXPART model.

Source regions: Pacific Ocean, Gulf of Mexico, Caribbean Sea, and Terrestrial. The red polygons denote the sink regions according to the spatial distribution of the isotope monitoring stations presented in Fig 1.


S1 Table. RENIP characteristics including site name, location, elevation, sampling date, water volume collected, and precipitation recorded, domain, and isotopic values.


S2 Table. Observed, precipitation-weighted, raster-derived (i.e., this study and global product) mean compositions and residuals (‰).


S1 Text. Inclusivity in global research statements.



The authors thank the helping hands contributing to precipitation sampling across Mexico.


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