1. Introduction
Meteorological drought is a prolonged absence or significant deficiency of precipitation. It is an inherent feature of the climate system and can manifest across various climate regimes, ranging from tropical rainforests to arid regions. Drought is a complex phenomenon that creates cascading impacts through the interaction of meteorological and human processes [1,2,3]. For example, inefficient water supply management can reduce the availability of drinking water, as economic sectors, such as irrigation, livestock, energy, and manufacturing, place increasing pressure on water demand. Additionally, heat waves can exacerbate drought risks by intensifying competition for water among these sectors [4].
Agricultural systems are particularly vulnerable to drought, as both precipitation and soil moisture directly affect water availability in rainfed systems, while irrigated systems depend on surface and groundwater sources that may be depleted during droughts—ultimately threatening crop yields. The Food and Agriculture Organization of the United Nations identifies drought as the primary risk to agriculture and livestock. Between 2008 and 2018, 82% of drought-related damages in low- and lower-middle-income countries occurred in the agriculture sector, resulting in losses of USD 37 billion in crop and livestock production and contributing to food insecurity and malnutrition [5]. Recurrent droughts can also lead to long-term environmental consequences, such as land subsidence, seawater intrusion, reduced water flow, and damage to ecosystems. Furthermore, droughts pose health-related risks by diminishing water availability for sanitation and hygiene [6] and creating conditions that facilitate the spread of disease [5].
The World Drought Atlas [6] identified regions that experienced significant impacts from major drought events between 2022 and 2024. For instance, the western United States and northern Mexico faced challenges related to ecosystems, agriculture, hydropower generation, and water supply. In Brazil’s Amazon Basin and the Extratropical Andes, drought affected inland navigation. In Europe, it impacted agriculture, ecosystems, and public health. West Africa experienced food security issues, while in East Africa, drought was linked to human displacement. Central and Southeast Asia encountered agricultural difficulties, and in East Asia, drought disrupted industrial activity. The study highlights that in 2022 and 2023, one in four people worldwide was affected by drought, with 85% of them living in low- and middle-income countries.
Droughts have affected Mexico throughout history. In both the Pre-Columbian era (1500 BCE–1500 CE) and the colonial period (1500–1821), Mexican society was primarily based on agriculture. Rain deficits at the beginning of the agricultural cycle caused crop losses, leading to food shortages, high prices, economic crises, and even social conflicts. Ref. [7] argues that the drought of 1785–1786 not only caused food shortages and political instability but also led to social problems, making it an explanatory factor for the War of Independence. Historical records do not provide sufficient information to identify the frequency and duration of droughts, as most sources focus on the societal effects of droughts rather than meteorological aspects.
Paleoclimate studies are another source of information about historical droughts. For example, the study by [8] reconstructed hydroclimatic variability during the winter-spring period in northeast Mexico using tree-ring chronologies. Their findings identified severe droughts in the 19th century (1810s, 1870s, and 1890s) and the 20th century (1950s and 1960s). The authors highlighted the influence of El Niño/Southern Oscillation (ENSO), cold fronts, and tropical storms in the Gulf of Mexico on the climatic variability of this region. Ref. [9] presented the Mexican Drought Atlas based on tree-ring chronologies to reconstruct the Palmer Drought Severity Index for the period 1600–2012 on a 0.5° grid. Their results show that northern Mexico experienced higher severity and frequency of decadal droughts than the humid central and southern regions of Mexico and identified ENSO as the most important ocean–atmospheric forcing of moisture detected in the study. In addition, the authors claim that anthropogenic climate change would make nationwide droughts more common.
In this century, droughts have affected Mexico on several occasions. The 1950 drought is considered the most severe drought event since 1900 [10]. Ref. [11] explains that early 21st-century drought conditions in Mexico were influenced by large-scale changes in ocean–atmospheric circulation. However, they also suggest that land cover changes may induce an increase in maximum daily temperatures and contribute to the severity, persistence, and spatial distribution of drought conditions in Mexico during the 1994–2004 period. One of the worst droughts occurred in 2011–2012, affecting 60% of the country’s territory and resulting in the loss of six million hectares of crops [12]. Regarding this episode, ref. [13] classifies it as an extreme event due to its severity and vast coverage, highlighting that the country’s population, particularly rain-dependent crop farmers and marginalized urban communities with limited access to water infrastructure, are especially vulnerable to drought impacts.
Ref. [11] asserts that any regional or global increase in temperature may contribute to further droughts in Mexico. Therefore, it is necessary to improve and expand the climate observation network, as well as to enhance land conservation efforts to address future drought conditions in the country. In this context, the meteorological station network in Mexico faces significant challenges, including a decline in the number of stations over the last decades and the low density of the network. Furthermore, the network is neither homogeneous nor spatially representative, especially in the northern regions of the country [14]. To address this gap, gridded estimates of precipitation are available from databases constructed using various sources, such as satellite measurements, interpolated observations, and reanalysis data. Several studies have evaluated these databases for their ability to characterize wet and dry periods for drought monitoring purposes in Mexico [15,16,17,18,19,20]. The findings indicate that there is no single best-performing precipitation product, highlighting the need for a validation process that considers the scale and climatic zone of the region being characterized.
Climate model simulations have also been used to evaluate patterns in historical droughts. Ref. [10] evaluated different observational datasets to analyze decadal trends, frequency, duration, and severity of historical droughts, as well as the ability of the regional climate model RegCM4 driven by three global climate models to reproduce spatial patterns (1981–2010) within the Coordinated Regional Climate Downscaling Experiment (CORDEX) domain, which comprises Central America, the Caribbean, and Mexico. The results show that the models consistently identify regions with severe droughts, including the USA–Mexico border, the North American Monsoon subdomain, the Yucatán Peninsula, and northern Central America. The authors point out that droughts in the study regions tend to occur in independent periods.
In the coming decades, changes in precipitation and temperature are expected due to climate change that will lead to changes in drought occurrence. The Intergovernmental Panel on Climate Change (IPCC) [3] projected increased drought severity in Southern Europe and the Mediterranean, Central Europe, Central America and Mexico, northeastern Brazil, and southern Africa. Some studies have evaluated projections of global drought patterns. For example, ref. [21] assessed droughts using projections from 15 CMIP5 (Coupled Model Intercomparison Project Phase 5) global climate models (GCMs). Their results show an increase in the extent, number of events, and average duration of severe droughts in the Central America region (including Mexico) by the end of the 21st century, considering the RCP8.5 pathway (i.e., a high-emission climate scenario predicting significant warming). The authors also highlight discrepancies in the magnitude of the projected changes among the considered GCMs. Ref. [22] evaluated climate hazard projections based on CORDEX simulations under two climate scenarios and found stronger drying and an increased frequency of droughts in Mexico. Moreover, in the North Central America domain, the results show a linear relationship between changes in the number of heat waves and drought frequency. Additionally, ref. [23] assessed changes in meteorological drought frequency and severity using regional climate model projections from CORDEX under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Results show that, under both scenarios, large areas of global land are expected to experience more frequent and severe droughts in the future. The authors identified future drought hotspots, including the North American west coast, most of Mexico, and northern Central America.
In response to the severe 2011–2012 drought, the Mexican government implemented a range of public policies aimed at mitigating the effects of future droughts. For example, the Mexican government introduced the “National Program Against Drought”, a preventive initiative aimed at monitoring and forecasting drought conditions [24]. Furthermore, the program seeks to reduce vulnerability to drought and provide stakeholders with information about economic, social, and environmental drought vulnerability at the municipal level, as well as programs for the prevention and mitigation of drought for several river basin councils [25].
Previous studies suggest an increase in meteorological drought in the coming decades, based on different projections for subdomains that include the Mexican territory. In this study, we present projections of meteorological drought in Mexico using indicators based on the Standardized Precipitation Index (SPI) to assess changes in drought frequency and severity across Mexican hydrological regions. This study uses multi-model projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for two future periods (2040–2069 and 2070–2099) under two Shared Socio-economic Pathway (SSP) scenarios.
Meteorological droughts are a severe and growing global risk, affecting water availability, agriculture, ecosystems, and socio-economic activities. In Mexico, region-specific projections in the context of climate change remain limited. Although several global studies suggest that the frequency and intensity of droughts may shift as precipitation patterns change under future climate scenarios, there is a need for regionally relevant information to support public policies related to drought risk management. Therefore, this study is motivated by the need to provide insights into how drought patterns may evolve across Mexico’s hydrological regions, each of which presents distinct climatic conditions that influence water availability and usage. By using CMIP6 climate projections to assess future drought frequency and severity, this research aims to provide decision-makers with the information necessary to develop more effective and adaptive public policies in the coming decades.
2. Materials and Methods
The Mexican National Water Commission (CONAGUA) organized the country’s basins into 37 Hydrological Regions (HR; [26]), and this characterization was considered to conduct this study (Figure 1). This scale of analysis has been used to evaluate the vulnerability to climate change [27] and meteorological drought over Mexico [28].
The hydrological regions group the basins according to their morphological, orographic, and hydrological characteristics and are generally defined by the country’s main hydrographic basins. The Mexican hydrological basins drain into either the Pacific Ocean or the Gulf of Mexico, except for a group of northern endorheic basins located in HR34, 35, 36, and 37. Natural availability of water (i.e., precipitation minus potential evapotranspiration, PET) varies by season and the location of the HR. For example, most precipitation occurs during summer, except in the northern Baja California Peninsula, where maximum precipitation occurs in winter [29]. Regarding location, the basins in northern and central Mexico have low natural water availability due to low precipitation; however, economic activities and population are concentrated in these regions [30]. According to CONAGUA, renewable water availability in the southeast is already seven times greater than in the rest of Mexico, with values of 10,718 and 1581 m3 per inhabitant per year, respectively [26].
Historical data and future precipitation projections were used to evaluate meteorological droughts. Monthly precipitation data were obtained from the Climatic Research Unit gridded Time Series (CRU) at a 0.5° resolution [31] to evaluate the meteorological droughts in the historical period (1985–2014). Projections of future simulated precipitation were obtained from 26 global climate models (Table 1) under the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) scenarios [32]. Two Shared Socio-economic Pathways were selected: SSP2-4.5 and SSP5-8.5 (hereafter S45 and S85, respectively), which correspond to moderate and high levels of radiative forcing, with higher values representing stronger climate change effects. The future precipitation simulation data were provided by the Canadian Climate Data and Scenarios (CCDS) website (
The model data were interpolated to match the observational grid resolution. Interpolation was performed using Pyresample (a Python package for resampling geospatial image data;
Drought indicators are based on the Standard Precipitation Index (SPI), a statistical measure that quantifies deviations from normal precipitation conditions [33]. The computation of the SPI requires long-term monthly precipitation time-series, which are fitted to a probability distribution (the gamma distribution), which is then transformed into a normal distribution, so the mean SPI is zero [34]. Therefore, positive SPI values indicate wetter-than-median conditions, while negative values reflect drier-than-median conditions [2]. The classification of wet and dry periods, based on the SPI, is shown in Table 2.
In this study, the 12-month Standardized Precipitation Index (SPI-12) was selected to evaluate drought indicators. This timescale allows the comparison of the precipitation for 12 consecutive months with that precipitation recorded in the same 12 consecutive months in all the previous years of available data. SPI-12 reflects long-term precipitation patterns, and this scale is closely linked to streamflow, reservoir storage, and groundwater levels [2]. The SPI-12 was computed as follows: firstly, the 1985–2014 period was used to compare the drought indicators obtained with climate simulations with those obtained with CRU data during the historical period; secondly, the SPI-12 was evaluated using climate simulations for the entire 1985–2099 period. This approach allows us to assess differences in drought indicators between the historical period and the near future period (2040–2069) and the far future period (2070–2099), hereafter referred to as F1 and F2, respectively.
The drought indicators in this study are based on the methodology used by ref. [23], which defines the start of a drought event when the SPI-12 value falls below −1 for at least two consecutive months and ends when the SPI-12 value turns positive. The following indicators were used: Drought frequency, defined as the number of drought events in a given period; Drought duration, estimated as the sum of all months between the start and the end of the event; Maximum drought duration, defined as the longest drought event in a given period.
3. Results
Figure 2 shows the mean annual precipitation evaluated with CRU data for the period 1985–2014. From this figure we can observe the differences in precipitation between the HRs. The highest amount of precipitation occurs in the southeast and the Gulf of Mexico, for instance, HR27 to HR30 have an average value of 1750 mm. On the other hand, the lowest amount of precipitation is observed in the HRs of the Baja California Peninsula, which have an average value of 180 mm. Similarly, the northern endorheic hydrological regions (HR35 to HR37) also have low precipitation, with an average value of 375 mm. The hydrological regions that discharge into the Pacific Ocean show greater variability, since their precipitation ranges from 262 mm (HR8) to 1648 mm (HR23). This variability in precipitation is a key factor in the diversity of the country’s climates, along with altitude and the occurrence of meteorological events such as tropical cyclones.
Figure 3 shows the comparison between the selected climate models and the observations (CRU data). The models exhibit considerable variability, with most showing an overestimation of mean monthly precipitation. However, some models—such as M15, M16, and M19—have values close to the observed mean (77 mm), with differences ranging from −3% to 10%. Despite this, these models display less variability than the observations, as indicated by the narrower range in their boxplots. Other models are more similar to the observations; for example, models M02, M03, M10, and M22 show median values that differ from the CRU data by −8% to 9%, and their boxplot spread is comparable to that of the observations. The worst result is obtained with model M17, which has a median value 83% greater than the observations. Variability among the models is also evident in the comparison of the standard deviation of the climate simulations (Figure 3b). Half of the models (13) have median values within ±10% of the observed value (76.9 mm). In this regard, models M02 and M10 also stand out.
The results of the Pearson correlation coefficient (Figure 3c) show a mean value of 0.6. Furthermore, models M03, M09, M13, M23, and M26 present the best performance. However, results also vary among hydrological regions. For instance, the regions located along the central and southern Pacific coast (HR12 to HR26) show correlation values around 0.77. In contrast, hydrological regions in the Yucatán Peninsula (HR31 to HR33) present lower correlation values, around 0.35, while the arid northern regions (HR34 to HR37) show values around 0.44. This comparison indicates that model performance differs across hydrological regions. Nevertheless, all models are included in the following analysis to obtain a general perspective on drought risk in the context of climate change in Mexico.
Figure 4 shows the comparison between the CRU data and the median of 26 climate models (SIM) in the historical period. From the boxplots, we can observe that the median of the annual precipitation is higher in the climate simulation data than in the CRU data, but the latter exhibit greater variability. The indicators of drought frequency, mean drought duration, and maximum drought duration show similar median values between CRU data and SIM data (six events, 15 months, and 27 months, respectively). However, the CRU data display greater variability; for instance, the maximum drought duration indicator ranges from 15 to 48 months. The most significant underestimation in this indicator is observed in the humid HR28 (34 months), whereas overestimations are also observed, for instance, in HR31 (19 months). Despite these discrepancies, the median difference in maximum drought duration between datasets is only 3.5 months, suggesting that while climate models capture the overall trend, they may smooth out extreme events.
The spatial differences between the CRU and SIM datasets in the drought indicators for the historical period are presented in Figure 5, which categorizes HRs by percentile rankings (>50th percentile, between the 50th and 75th percentiles, and above the 75th percentile, as indicated in the boxplots in Figure 4). Both datasets mostly coincide in identifying the hydrological regions most affected by drought. Regarding the mean drought duration indicator, the HRs with the highest values are located in the Baja California Peninsula, the northwest, and the southern Pacific. However, SIM data tend to underestimate this indicator in the northeastern hydrological regions (HR24) and in the Gulf of Mexico hydrological regions (HR26 to HR28). Similarly, the maximum drought duration indicator shows that these three hydrological regions are considered the most affected according to CRU data. On the contrary, the SIM data identify the southern hydrological regions as those with the highest values for this indicator. In addition, both datasets identify the Baja California HRs and the northwest HRs as the hydrological regions with high values for the maximum drought duration indicator.
Figure 6 shows the change in precipitation between the historical period and the future periods (F1 and F2). This figure highlights the variability among climate models when a given scenario is considered. For instance, in the near future (F1) under the S45 scenario, the projected median change values range from 3.5% (M07) to −7.1% (M21); however, most models (19) project negative median changes (Figure 6a). Considering the same period but under the S85 scenario (Figure 6b), the projected median change in precipitation ranges from 4.3% (M24) to −10.8% (M13), with 24 climate models estimating negative changes. In the far future (F2), the models under the S45 scenario project changes between 4.4% (M16) and −12.4% (M05), with 21 climate models projecting negative changes (Figure 6c). The most significant changes are projected in the far future (F2) under the S85 scenario (Figure 6d), with values ranging from −0.5% (M16) to −23.1% (M05). The climate models show great variability, revealing differences among the hydrological regions. For example, in the F2 S85 scenario, model M02 has a wide range of projections, from 27% (HR04) to −42% (HR21), while model M03 shows a smaller range, from −0.3% (HR17) to −16.7% (HR03). However, both models present similar median values of change (about −9.5%).
Figure 7 shows the variability in projected precipitation changes among the hydrological regions. In the near future (F1), under scenario S45 (Figure 7A), the Baja California hydrological regions exhibit the largest median changes compared to the rest of the country. On the other hand, the results obtained for scenario S85 (Figure 7C) show a greater projected median change in the hydrological regions located in the central and southern Pacific Ocean, as well as in the southeast, including the HRs of the Gulf of Mexico and the Yucatan Peninsula, with median change values around −10% and −15%. Regarding the far future F2 considering scenario S45 (Figure 7E), the most significant changes in precipitation are projected in hydrological regions of the North Pacific and the Baja California Peninsula (−10% to −15%), while scenario S85 (Figure 7G) presents the greatest changes with important geographical differences: the northern and central HRs projected median changes of −5% to −10%, the hydrological regions located in the North Pacific and the Gulf of Mexico have values of −10% to −15%, and the Yucatan Peninsula and Grijalva-Usumacinta (HR30) have median changes greater than −15%. The Baja California Peninsula and the central and southern Pacific hydrological regions show mixed projected changes.
As previously mentioned, climate models exhibit significant variability in the projected changes in precipitation. The right panel of Figure 7 depicts the standard deviation (STD) of the projected changes in precipitation. From this figure, we can note that the variability is greater in the F2 period than in the F1 period. The hydrological regions with the highest standard deviation values are those of the Baja California Peninsula, the southern Pacific, and the Yucatan Peninsula. Considering the F2 S85 scenario, the endorheic hydrological regions have relatively small median changes but high variability, especially Mapimi (HR35).
Figure 8 shows the change in the drought frequency indicator between the reference and the future periods. For the near future (F1), the scenario S45 (Figure 8A) shows small changes in this indicator, with values of one to two events in the Baja California Peninsula, the central hydrological regions, and the northwest hydrological regions. On the other hand, the scenario S85 (Figure 8B) shows a clearer difference between the north and the south, with more significant changes in the latter, especially in the southeast, with values of two to three more events in the Grijalva-Usumacinta (HR30) and in the south of the Yucatan Peninsula. Regarding the far future (F2), the scenario S45 (Figure 8C) shows a mixed geographical distribution in the change of this indicator. On the contrary, the scenario S85 (Figure 8D) shows that northern and central Mexico project an increase of one to two events, while the southeast, the Yucatan Peninsula, and central Pacific hydrological regions (HR11 and HR13) show an increase of two to three events. The most important change is projected in the central Mexico hydrological regions, from the Pacific Ocean to the Gulf of Mexico, with an increase of three to four events.
The change in the mean drought duration indicator is shown in Figure 9. The near future F1 S45 projects low changes and even a decrease in this indicator (Figure 9A) except for the Bravo-Conchos hydrological region (HR24) and some hydrological regions in the Central Pacific (HR13 to HR16), where values range from 3 to 6 months. The F1 S85 scenario (Figure 9B) presents a greater increase in this indicator: the northern HRs have values of 3 to 6 months; the Gulf of Mexico HRs present values of 6 to 9 months; and the central Pacific and the southeast regions have values of 9 to 12 months. The F2 S45 scenario (Figure 9C) shows a similar pattern to F1 S85, and the most important changes are projected with the F2 S85 scenario (Figure 9D), which presents mixed results ranging from no change to 6 months in the northern HRs, 9 to 12 months in most of the southern HRs, and 15 to 18 months in the southeast HRs. The most significant change is projected again in the central Pacific HRs, with an increase of 18–20 months.
Figure 10 shows the number of drought events in future periods that are more severe than the most severe event that occurred in the reference period. The F1 S45 scenario (Figure 10A) presents a value of one event in most of the territory, while F1 S85 (Figure 10B) shows a value of two events in the central and southern hydrological regions. The F2 S85 scenario (Figure 10C) also projects values of 1 to 1.5 events in most of HRs except for the northwest, which presents no event, and a value of about 2 events in the central HRs from the Pacific Ocean to the Gulf of Mexico. Finally, the F2 S85 scenario (Figure 10D) shows a marked geographical difference, as the northern HRs range from 1 to 2.5 events and the southern regions from 3 to 4 events, with a value of 4.5 events in HR 28.
4. Discussion
Droughts occur due to a combination of thermodynamic and dynamic processes. The former relates to changes in air temperature, radiation, wind speed, and relative humidity, while the latter involves changes in the occurrence, duration, and intensity of weather anomalies associated with atmospheric and oceanic motions [3]. In Mexico, the pattern of rainfall experiences recurrent alterations that lead to drought. During a warm ENSO phase, a weaker thermal gradient prevents the intertropical convergence zone from shifting north, reducing convection that would normally bring rain and leading to drought in central and southern Mexico [13]. Also, the negative phase of the Pacific Decadal Oscillation leads to prolonged meteorological drought in northern Mexico [35]. Nevertheless, thermodynamic processes are considered the main driver of drought changes in a warm climate, as they influence precipitation and evapotranspiration [3].
In this study, meteorological drought in historical and future periods was evaluated with indicators based on the SPI [22,36]. The WMO [1] highlights SPI as a starting point for meteorological drought monitoring; however, it does not consider the temperature component. To fill this gap, other studies complement the analysis with the Standardized Precipitation Evapotranspiration Index (SPEI) [10]. When comparing drought indicators using SPI and SPEI with climate projections, drought indicators based on SPI could be underestimated [23]. The use of the SPEI index relies on the selection of a PET formulation. Compared to the reference Penman-Monteith [37] formulation, other methods—such as Thornthwaite [38], Turc, and Hargreaves-Samani [39]—could lead to significant differences in PET values in arid climates in Mexico [40]. Future work will explore the variability in drought indicators due to PET formulation using climate model projections.
Precipitation has proven to be more difficult to model due to the limited capacity of global climate models to account for the effects of regional features on rainfall patterns (e.g., topography, land cover, etc.) and the difficulty of disentangling its response to global warming from multidecadal fluctuations [13]. The GCMs selected in this study exhibit varying performances when compared with observations over the historical period. In general, the models tend to overestimate observed precipitation, although the variability, in terms of standard deviation, is generally close to the CRU data. Several studies have focused on evaluating GCMs to select those that better capture the spatial distribution of precipitation [41,42,43]. In this study, we use all 26 selected models to account for the uncertainty associated with GCMs, as different models handle key processes (e.g., clouds, oceans, ice sheets) differently. However, results show that model performance varies depending on the hydrological region considered. For example, the highest correlations with observations were found along the southern Pacific coast HRs, while lower correlations were observed in the Yucatan Peninsula HRs. Future work will include a deeper analysis aimed at selecting the models that better represent precipitation patterns for the study of drought indicators.
Ref. [44] presents a report on the state of knowledge regarding climate change in Mexico. The authors claim that climate change has modified the seasonal distribution of precipitation in the country; however, these changes are not uniform, as precipitation has decreased in northern regions and increased in central and southern regions. In addition, the authors state that global climate model (GCM) projections under high greenhouse gas (GHG) emission scenarios indicate that annual average precipitation will decrease across the country, accompanied by warmer conditions. Under the high GHG scenario (SSP5-8.5), the projected increase in annual average temperature in Mexico could range from 1.8 °C to 2.5 °C from 2050 to the end of the century, with the greatest rise occurring in the north of the country. On the other hand, the largest decrease in precipitation in the second half of the century is expected in the Yucatán Peninsula, the central and northern areas of the Pacific coast (in spring), and southeastern Mexico (in summer), while the smallest reduction is expected in the north. Overall, a similar pattern in future projected precipitation was found in this study for the high-emission scenario. Ref. [44] also highlights significant differences in projections due to variations among climate models. Similarly, our results show substantial variability between models and across geographical regions, with the highest variability in projected precipitation changes occurring in the northwest and southeast of the country.
From 2003 to 2020, observed droughts in Mexico have become more frequent and severe, and this trend is expected to become more critical under climate change conditions [45]. Regarding changes in drought indicators, the results of this study show that in the coming decades, the frequency and severity of droughts will increase across most of Mexico, particularly under the SSP5-8.5 scenario. These findings align with previous studies based on CMIP5 and CORDEX simulations [10,22,36].
Climate change will have negative impacts on several aspects of the hydrological cycle in Mexico, such as runoff [46,47] and aquifer recharge [48], which will affect water availability for human consumption, agriculture, and economic activities. Changes in the frequency and severity of meteorological droughts will pose a major challenge for agriculture, as significant reductions in crop yields are expected by the end of the century under a high-emission scenario. Under these conditions, subsistence farmers in vulnerable situations, primarily located in the south of the country, will face the greatest risks [44]. Our study agrees in identifying the southern hydrological regions as the most affected by the increase in drought frequency and severity, where agriculture is most dependent on rain and the population often lacks access to material and institutional resources to cope with drought [13].
Another important risk associated with drought in Mexico is related to water supply in the country’s most important urban areas, as many of them rely on transferring water from a basin with water availability to another basin lacking water. This is the case for Mexico City and other industrial cities, such as Querétaro and San Luis Potosí. Despite the environmental costs of this policy, plans for water transfers continue. For example, to guarantee the water supply to the city of Monterrey, located in an arid hydrological region, there are plans to transfer water from the HR Pánuco, considered a region with abundant water [49]. The results of this study show that the increase in the frequency and severity of drought by the end of the century could compromise this approach to water management, as hydrological regions currently considered abundant in water resources may experience changes in the coming decades. Agriculture in Mexico is expected to be significantly affected by changes in precipitation patterns and increasing drought severity, which threaten both water and food security [50]. For instance, ref. [51] highlights that the entire Mexican territory will be prone to droughts by the end of the century, ranging from moderate in central and southern regions to extreme in the north. These conditions, combined with rising temperatures, are projected to reduce soil moisture, leading to decreased agricultural and livestock yields. Furthermore, nationwide yield reductions could exceed 50% for soybean and rice, around 40% for maize and sorghum, and approximately 20% for wheat [44]. Therefore, it is essential to implement mitigation and adaptation strategies, including risk management policies such as technology transfer programs, the use of hybrid seeds, technical support, and accessible climate forecasts for farmers [50].
Nevertheless, the study has some limitations. The resolution of the selected GCMs may not capture certain local processes; therefore, it is recommended to incorporate regional climate models to improve the spatial resolution of precipitation projections. Additionally, as mentioned, the use of drought indicators based on temperature should also be considered.
5. Conclusions
In this study, drought indicators were evaluated using multi-model GCM precipitation projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socio-Economic Pathway (SSP) scenarios for Mexican hydrological regions. Drought indicators, based on the Standard Precipitation Index, were computed for a reference period (1985–2014), the near future (2040–2069), and the far future (2070–2099) to evaluate changes in the frequency and severity of meteorological droughts in the context of climate change. The main findings are as follows: In the reference period, the drought indicators evaluated using simulated projections show a similar median value to those computed with the observational dataset (CRU data) but with lower variability. Regarding geographical distribution, both datasets mostly align in identifying the hydrological regions most affected by drought in terms of frequency; however, simulated projections show mixed results as they tend to either underestimate or overestimate the maximum drought duration indicator. GCMs exhibit high variability in projected precipitation changes between the reference and future periods, mostly showing a decrease in precipitation under SSP2-4.5. However, the SSP5-8.5 scenario indicates a clearer decrease in precipitation by the end of the century. In this case, the hydrological regions are projected to experience at least a 5 to 10% decrease, with significant reductions exceeding 10% in the Pacific Coast, the Gulf of Mexico coast, and the Yucatán Peninsula. Changes in drought indicators are more pronounced under the SSP5-8.5 scenario than under the SSP2-4.5 scenario, and in the far future period compared to the near future. The entire territory is projected to experience more frequent and severe droughts, with the central and southern hydrological regions facing the highest risk. For instance, in these regions, the frequency indicator is expected to increase by up to two events, and the drought duration indicator by up to 15 months in the far future period under the SSP5-8.5 scenario.
The results of this study highlight the uncertainty in evaluating changes in drought indicators, which is related to the selected GCM and the scenario considered. Overall, the findings of this investigation can provide water management planners with insights into the probable changes in meteorological drought that could affect agriculture, water supply, and economic activities in Mexico over the coming decades.
Conceptualization, J.A.V.-Z. and R.D.-O.; methodology, J.A.V.-Z. and R.D.-O.; software, J.A.V.-Z. and R.D.-O.; validation, J.A.V.-Z. and R.D.-O.; formal analysis, J.A.V.-Z. and R.D.-O.; investigation, J.A.V.-Z. and R.D.-O.; data curation, J.A.V.-Z. and R.D.-O.; writing—original draft preparation, J.A.V.-Z.; writing—review and editing, R.D.-O.; visualization, J.A.V.-Z. and R.D.-O.; supervision, J.A.V.-Z. All authors have read and agreed to the published version of the manuscript.
The GCM simulations datasets used in this study are openly available on the following website:
The authors thank the research groups that made available the climate projections. We also thank the anonymous reviewers for their valuable comments and suggestions, which helped improve this manuscript.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| HR | Hydrological region |
| GCM | Global climate model |
| S45 | Shared socio-economic pathway SSP2-4.5 |
| S85 | Shared socio-economic pathway SSP5-8.5 |
| CMIP6 | Sixth phase of the coupled model intercomparison project |
| F1 | 2040–2069 period (near future period) |
| F2 | 2070–2099 period (far future period) |
| CRU | Climatic research unit |
| SIM | Simulated data (climate models data) |
| SPI-12 | 12-month standardized precipitation index |
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Figure 1 Hydrological regions.
Figure 2 Mean annual precipitation in Mexican hydrological regions as computed with CRU data for the period 1985–2014.
Figure 3 Mean monthly precipitation (a), standard deviation (b), and Pearson correlation coefficient between CRU data and the climate models (c) for the historical period (1985–2014). Each boxplot was computed using results from the hydrological regions. The dashed line indicates the CRU median value.
Figure 4 Mean annual precipitation and drought indicators in the historical period (1985–2014) for CRU data and the median of the climate models data (SIM). Each boxplot was computed using the results from the hydrological regions.
Figure 5 Percentiles of mean drought duration for CRU data (a) and the median of climate model data (SIM data) (b), and maximum drought duration for CRU data (c) and SIM data (d) for the historical period (1985–2014).
Figure 6 Change in precipitation (%) between the reference period (1985–2014) and the future periods F1 (2040–2069, left panels) and F2 (2070–2099, right panels) under two scenarios, S45 (blue boxplots) and S85 (red boxplots). Each boxplot was computed using the average changes calculated for the hydrological regions.
Figure 7 Median change in precipitation (%, left panel) and standard deviation of the precipitation change (STD, right panel) computed between the reference period (1985–2014) and the future periods F1 (2040–2069) and F2 (2070–2099) under scenarios S45 and S85.
Figure 8 Change in the drought frequency indicator (events) between the reference period (1985–2014) and the future periods F1 (2040–2069, upper panel) and F2 (2070–2099, lower panel) under two scenarios, S45 (left panel) and S85 (right panel). Each hydrological region shows the median change of the climate models.
Figure 9 Change in the mean drought duration indicator (months) between the reference period (1985–2014) and the future periods F1 (2040–2069, upper panel) and F2 (2070–2099, lower panel) under two scenarios, S45 (left panel) and S85 (right panel). Each hydrological region shows the median change of the climate models.
Figure 10 Number of drought events in future periods that are more severe than the most severe event that occurred in reference period F1 (2040–2069, upper panel) and F2 (2070–2099, lower panel) under two scenarios, S45 (left panel) and S85 (right panel). Each hydrological region shows the median of climate models.
Selected global climate models.
| I.D. | Model | Institute |
|---|---|---|
| M01 | AWI-CM-1-1-MR | Alfred Wegener Institute (AWI), Germany |
| M02 | BCC-CSM2-MR | Beijing Climate Center, China |
| M03 | CAMS-CSM1-0 | Chinese Academy of Meteorological Sciences, China |
| M04 | CanESM5 | Canadian Centre for Climate Modelling and Analysis, Canada |
| M05 | CESM2-WACCM | National Center for Atmospheric Research, USA |
| M06 | CESM2 | |
| M07 | CMCC-CM2-SR5 | Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Italy |
| M08 | EC-Earth3-Veg | EC-Earth-Consortium |
| M09 | EC-Earth3 | |
| M10 | FGOALS-f3-L | Chinese Academy of Sciences, China |
| M11 | FGOALS-g3 | |
| M12 | FIO-ESM-2-0 | First Institute of Oceanography, China |
| M13 | GFDL-CM4 | National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA |
| M14 | GFDL-ESM4 | |
| M15 | INM-CM4-8 | Institut Pierre Simon Laplace, France |
| M16 | INM-CM5-0 | |
| M17 | IPSL-CM6A-LR | Korea Institute of Ocean Science and Technology, Korea |
| M18 | KACE-1-0-G | Japan Agency for Marine-Earth Science and Technology, Japan |
| M19 | KIOST-ESM | Max Planck Institute for Meteorology (MPI-M), Deutscher Wetterdiens (DWD), Germany |
| M20 | MIROC6 | MPI-M, AWI, Deutsches Klimarechenzentrum, DWD, Germany |
| M21 | MPI-ESM1-2-HR | Meteorological Research Institute, Japan |
| M22 | MPI-ESM1-2-LR | Nanjing University of Information Science and Technology, China |
| M23 | MRI-ESM2-0 | NorESM Climate modeling Consortium consisting of CICERO, Norway |
| M24 | NESM3 | Institut Pierre Simon Laplace, France |
| M25 | NorESM2-LM | National Institute of Meteorological Sciences, Korea |
| M26 | NorESM2-MM | Korea Institute of Ocean Science and Technology, Korea |
| M26 | GFDL-CM4 |
SPI values.
| SPI Range | Drought/Wetness Category |
|---|---|
| 2.0+ | extremely wet |
| 1.5 to 1.99 | very wet |
| 1.0 to 1.49 | moderately wet |
| −0.99 to 0.99 | near normal |
| −1.0 to −1.49 | moderately dry |
| −1.5 to −1.99 | severely dry |
| −2 and less | extremely dry |
1. World Meteorological Organization (WMO). Handbook of Drought Indicators and Indices; Integrated Drought Management Programme (IDMP) WMO: Geneva, Switzerland, 2016.
2. World Meteorological Organization (WMO). Standardized Precipitation Index User Guide; WMO: Geneva, Switzerland, 2012; 24.
3. IPCC. Weather and Climate Extreme Events in a Changing Climate. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, New York, NY, USA, 2021; pp. 1513-1766.
4. Cárdenas Belleza, G.A.; Bierkens, M.F.P.; van Vliet, M.T.H. Sectoral Water Use Responses to Droughts and Heatwaves: Analyses from Local to Global Scales for 1990–2019. Environ. Res. Lett.; 2023; 18, 104008. [DOI: https://dx.doi.org/10.1088/1748-9326/acf82e]
5. FAO. The Impact of Disasters and Crises on Agriculture and Food Security: 2021; FAO—Food and Agriculture Organization of the United Nations: Rome, Italy, 2021; ISBN 978-92-5-134071-4
6. UNCCD (United Nations Convention to Combat Desertification). World Drought Atlas; UNCCD: Luxembourg, 2024; ISBN 1831-9424
7. García-Acosta, V. Las sequías históricas de México. Desastres Y Soc.; 1993; 1, pp. 2-18.
8. Villanueva-Diaz, J.; Stahle, D.W.; Luckman, B.H.; Cerano-Paredes, J.; Therrell, M.D.; Cleaveland, M.K.; Cornejo-Oviedo, E. Winter-Spring Precipitation Reconstructions from Tree Rings for Northeast Mexico. Clim. Chang.; 2007; 83, pp. 117-131. [DOI: https://dx.doi.org/10.1007/s10584-006-9144-0]
9. Stahle, D.W.; Cook, E.R.; Burnette, D.J.; Villanueva, J.; Cerano, J.; Burns, J.N.; Griffin, D.; Cook, B.I.; Acuña, R.; Torbenson, M.C.A.
10. Andrade-Gómez, L.; Cavazos, T. Historical Meteorological Droughts over the CORDEX-CAM (Central America, Caribbe-an and Mexico) Domain: Evaluating the Simulation of Dry Hot Spots with RegCM4. Int. J. Climatol.; 2024; 44, pp. 1110-1134. [DOI: https://dx.doi.org/10.1002/joc.8374]
11. Stahle, D.W.; Cook, E.R.; Díaz, J.V.; Fye, F.K.; Burnette, D.J.; Griffin, D.; Soto, R.A.; Seager, R.; Heim, R.R., Jr. Early 21st-Century Drought in Mexico. Eos Trans. Am. Geophys. Union; 2009; 90, pp. 89-90. [DOI: https://dx.doi.org/10.1029/2009EO110001]
12. Domínguez, J. Revisión histórica de las sequías en México: De la explicación divina a la incorporación de la ciencia. Tecnol. Y Cienc. Del Agua; 2016; 7, pp. 77-93.
13. Dobler-Morales, C.; Bocco, G. Social and Environmental Dimensions of Drought in Mexico: An Integrative Review. Int. J. Disaster Risk Reduct.; 2021; 55, 102067. [DOI: https://dx.doi.org/10.1016/j.ijdrr.2021.102067]
14. Livneh, B.; Bohn, T.J.; Pierce, D.W.; Munoz-Arriola, F.; Nijssen, B.; Vose, R.; Cayan, D.R.; Brekke, L. A Spatially Comprehensive, Hydrometeorological Data Set for Mexico, the U.S., and Southern Canada 1950–2013. Sci. Data; 2015; 2, 150042. [DOI: https://dx.doi.org/10.1038/sdata.2015.42]
15. Miranda, J.L.G. Precipitation Estimation over Mexico Applying PERSIANN System and Gauge Data. Master’s Thesis; The Universit y of Arizona: Tucson, AZ, USA, 2002.
16. Mayor, Y.G.; Tereshchenko, I.; Fonseca-Hernández, M.; Pantoja, D.A.; Montes, J.M. Evaluation of Error in IMERG Precipitation Estimates under Different Topographic Conditions and Temporal Scales over Mexico. Remote Sens.; 2017; 9, 503. [DOI: https://dx.doi.org/10.3390/rs9050503]
17. De Jesús, A.; Breña-Naranjo, J.A.; Pedrozo-Acuña, A.; Alcocer Yamanaka, V.H. The Use of TRMM 3B42 Product for Drought Monitoring in Mexico. Water; 2016; 8, 325. [DOI: https://dx.doi.org/10.3390/w8080325]
18. Rincón-Avalos, P.; Khouakhi, A.; Mendoza-Cano, O.; Cruz, J.L.-D.l.; Paredes-Bonilla, K.M. Evaluation of Satellite Precipitation Products over Mexico Using Google Earth Engine. J. Hydroinformatics; 2022; 24, pp. 711-729. [DOI: https://dx.doi.org/10.2166/hydro.2022.122]
19. Velázquez-Zapata, J.A. Evaluation of the SPI with High-Resolution Datasets in Historical and Future Climate in Central Mexico. Ing. Agrícola Y Biosist.; 2023; 15, pp. 23-43. [DOI: https://dx.doi.org/10.5154/r.inagbi.2023.06.030]
20. Esquivel-Arriaga, G.; Huber-Sannwald, E.; Reyes-Gómez, V.M.; Bravo-Peña, L.C.; Dávila-Ortiz, R.; Martínez-Tagüeña, N.; Velázquez-Zapata, J.A. Performance Evaluation of Global Precipitation Datasets in Northern Mexico Drylands. J. Appl. Meteorol. Climatol.; 2024; 63, pp. 1545-1558. [DOI: https://dx.doi.org/10.1175/JAMC-D-23-0227.1]
21. Touma, D.; Ashfaq, M.; Nayak, M.A.; Kao, S.-C.; Diffenbaugh, N.S. A Multi-Model and Multi-Index Evaluation of Drought Characteristics in the 21st Century. J. Hydrol.; 2015; 526, pp. 196-207. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2014.12.011]
22. Coppola, E.; Raffaele, F.; Giorgi, F.; Giuliani, G.; Xuejie, G.; Ciarlo, J.M.; Sines, T.R.; Torres-Alavez, J.A.; Das, S.; di Sante, F.
23. Spinoni, J.; Barbosa, P.; Bucchignani, E.; Cassano, J.; Cavazos, T.; Christensen, J.H.; Christensen, O.B.; Coppola, E.; Evans, J.; Geyer, B.
24. Arreguín-Cortés, F.I.; López-Pérez, M.; Ortega-Gaucin, D.; Ibañez-Hernández, Ó.; Arreguín-Cortés, F.I.; López-Pérez, M.; Ortega-Gaucin, D.; Ibañez-Hernández, Ó. La política pública contra la sequía en México: Avances, necesidades y perspectivas. Tecnol. Y Cienc. Del Agua; 2016; 7, pp. 63-76.
25. CONAGUA. Política Pública Programa Nacional Contra La Sequía PRONACOSE. Available online: https://www.gob.mx/cms/uploads/attachment/file/774064/Actualizaci_n_Pol_tica_P_blica_Nacional_para_la_Sequ_a_SEP2022.pdf (accessed on 19 May 2025).
26. SEMARNAT. Estadísticas del Agua en México. Available online: https://files.conagua.gob.mx/conagua/publicaciones/Publicaciones/EAM-2017.pdf (accessed on 19 May 2019).
27. Mendoza, V.; Villanueva, E.; Adem, J. Vulnerability of Basins and Watersheds in Mexico to Global Climate Change. Clim. Res.; 1997; 9, pp. 139-145. [DOI: https://dx.doi.org/10.3354/cr009139]
28. Neri, C.; Magaña, V. Estimation of Vulnerability and Risk to Meteorological Drought in Mexico. Weather Clim. Soc.; 2016; 8, pp. 95-110. [DOI: https://dx.doi.org/10.1175/WCAS-D-15-0005.1]
29. García, E. Distribución de la precipitación en la República Mexicana. Investig. Geográficas; 2003; 50, pp. 67-76. [DOI: https://dx.doi.org/10.14350/rig.30432]
30. Cotler, H.; Garrido, A.; Bunge, B.; Cuevas, M.L. Las Cuencas Hidrográficas de México: Priorización y Toma de Decisiones. Las Cuencas Hidrográficas de México: Diagnóstico y Priorización; Instituto Nacional de Ecología—Fundación Gonzalo Rio Arrionte I.A.P: México D.F., México, 2010; 231. ISBN 978-607-7655-07-7
31. Harris, I.; Osborn, T.J.; Jones, P.; Lister, D. Version 4 of the CRU TS Monthly High-Resolution Gridded Multivariate Climate Dataset. Sci. Data; 2020; 7, 109. [DOI: https://dx.doi.org/10.1038/s41597-020-0453-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32246091]
32. Eyring, V.; Bony, S.; Meehl, G.A.; Senior, C.A.; Stevens, B.; Stouffer, R.J.; Taylor, K.E. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization. Geosci. Model Dev.; 2016; 9, pp. 1937-1958. [DOI: https://dx.doi.org/10.5194/gmd-9-1937-2016]
33. McKee, T.B.; Doesken, N.J.; Kleist, J. The Relationship of Drought Frequency and Duration to Time Scales. Proceedings of the 8th Conference on Applied Climatology; Anaheim, CA, USA, 17–22 January 1993.
34. Edwars, T.C.; McKee, D.C. Characteristics of 20th Century Drought in the United States at Multiple Time Scales. Master’s Thesis; Department of Atmospheric Science, Colorado State University, Air Force Institute of Technology: Fort Collins, CO, USA, 1997.
35. Méndez, M.; Magaña, V. Regional Aspects of Prolonged Meteorological Droughts over Mexico and Central America. J. Clim.; 2010; 23, pp. 1175-1188. [DOI: https://dx.doi.org/10.1175/2009JCLI3080.1]
36. Spinoni, J.; Naumann, G.; Carrao, H.; Barbosa, P.; Vogt, J. World Drought Frequency, Duration, and Severity for 1951–2010. Int. J. Climatol.; 2014; 34, pp. 2792-2804. [DOI: https://dx.doi.org/10.1002/joc.3875]
37. Allen, R.; Pereira, L.; Raes, D.; Smith, M. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56; FAO—Food and Agriculture Organization of the United Nations: Rome, Italy, 1998; Volume 56.
38. Thornthwaite, C.W. An Approach toward a Rational Classification of Climate. Geogr. Rev.; 1948; 38, 55. [DOI: https://dx.doi.org/10.2307/210739]
39. Hargreaves, G.H.; Samani, Z.A. Reference Crop Evapotranspiration from Temperature. Appl. Eng. Agric.; 1985; 1, pp. 96-99. [DOI: https://dx.doi.org/10.13031/2013.26773]
40. Campos-Aranda, D.F. Comparison of RDI Based on PET in Three Climatic Locations in San Luis Potosi, Mexico. Tecnol. Y Cienc. Del Agua; 2018; 9, pp. 147-183. [DOI: https://dx.doi.org/10.24850/j-tyca-2018-04-07]
41. Tiwari, R.; Mishra, A.K.; Rai, S.; Pandey, L.K. Evaluation and Projection of Northeast Monsoon Precipitation over India under Higher Warming Scenario: A Multimodel Assessment of CMIP6. Theor. Appl. Clim.; 2023; 151, pp. 859-870. [DOI: https://dx.doi.org/10.1007/s00704-022-04299-8]
42. Martinez-Villalobos, C.; Neelin, J.D.; Pendergrass, A.G. Metrics for Evaluating CMIP6 Representation of Daily Precipitation Probability Distributions. J. Clim.; 2022; 35, pp. 5719-5743. [DOI: https://dx.doi.org/10.1175/JCLI-D-21-0617.1]
43. Hemanandhini, S.; Vignesh Rajkumar, L. Performance Evaluation of CMIP6 Climate Models for Selecting a Suitable GCM for Future Precipitation at Different Places of Tamil Nadu. Environ. Monit Assess; 2023; 195, 928. [DOI: https://dx.doi.org/10.1007/s10661-023-11454-9]
44. Estrada Porrúa, F.; Zavala Hidalgo, J.; Martínez Arroyo, A.; Raga, G.; Carlos, G.G. Estado y Perspectivas Del Cambio Climático En México: Un Punto de Partida; 1st ed. Programa de Investigación en Cambio Climático Universidad Nacional Autónoma de México: Mexico City, Mexico, 2023; ISBN 978-607-30-8172-6
45. Ortiz-Haro, G.A.; Gress-Carrasco, F.; Mazarati-Hiriart, M. Recursos Hídricos y Cambio Climático: Una Visión Desde México. Estado y Perspectivas del Cambio Climático en México: Un Punto de Partida; Estrada Porrúa, F.; Zavala Hidalgo, J.; Martínez Arroyo, A.; Raga, G.; Carlos, G.G. Programa de Investigación en Cambio Climático Universidad Nacional Autónoma de México: Mexico City, Mexico, 2023; ISBN 978-607-30-8172-6
46. Dávila-Ortiz, R.; Algara-Siller, M.; Velázquez-Zapata, J.A. Variabilidad del impacto del cambio climático en el régimen hidrológico de dos cuencas de la región Huasteca. Ing. Investig. Y Tecnol.; 2020; 21, pp. 1-12. [DOI: https://dx.doi.org/10.22201/fi.25940732e.2020.21.3.021]
47. Velázquez, J.A.; Troin, M.; Caya, D. Hydrological Modeling of the Tampaon River in the Context of Climate Change. Technol. Y Cienc. Del Agua; 2015; 6, pp. 17-30.
48. Molina-Navarro, E.; Hallack-Alegría, M.; Martínez-Pérez, S.; Ramírez-Hernández, J.; Mungaray-Moctezuma, A.; Sastre-Merlín, A. Hydrological Modeling and Climate Change Impacts in an Agricultural Semiarid Region. Case Study: Guadalupe River Basin, Mexico. Agric. Water Manag.; 2016; 175, pp. 29-42. [DOI: https://dx.doi.org/10.1016/j.agwat.2015.10.029]
49. Magaña, V.; Herrera, E.; Ábrego-Góngora, C.J.; Ávalos, J.A. Socioeconomic Drought in a Mexican Semi-Arid City: Monterrey Metropolitan Area, a Case Study. Front. Water; 2021; 3, 579564. [DOI: https://dx.doi.org/10.3389/frwa.2021.579564]
50. Cruz-González, A.; Arteaga-Ramírez, R.; Sánchez-Cohen, I.; Monterroso-Rivas, A.I.; Soria-Ruiz, J. Indices to Identify Historical and Future Periods of Drought for the Maize Crop (Zea mays L.) in Central Mexico. Agronomy; 2025; 15, 460. [DOI: https://dx.doi.org/10.3390/agronomy15020460]
51. Ramírez Sánchez, H.U.; Fajardo Montiel, A.L.; Ortiz Bañuelos, A.D.; De la Torre Villaseñor, O. The Agricultural Sector and Climate Change in Mexico. J. Agric. Ecol. Res. Int.; 2022; 23, pp. 19-44. [DOI: https://dx.doi.org/10.9734/jaeri/2022/v23i330222]
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Dávila-Ortiz, Rodrigo 2
1 División de Geociencias Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica, A.C., Camino a la Presa San José 2055, San Luis Potosí 78216, Mexico; [email protected], Secretaría de Ciencia, Humanidades, Tecnología e Innovación, Av. Insurgentes Sur 1582, Ciudad de México 03940, Mexico
2 División de Geociencias Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica, A.C., Camino a la Presa San José 2055, San Luis Potosí 78216, Mexico; [email protected]