Recibido: 07/03/2025
Aceptado: 23/04/2025
Disponible en línea: 30/06/2025
Publicado: 31/07/2025

REVISTA DE TELEDETECCIÓN
Asociación Española de Teledetección
(2025) 66, 22885
ISSN 1133-0953
EISSN 1988-8740
https://doi.org/10.4995/raet.2025.22885

Meteorological drought using the Precipitation Condition Index in dry northeast Mexico during the 2000-2023 period

Víctor H. Domínguez-Meza ¹, Ignacio González-Gutiérrez^*1, Wilberth A. Poot-Poot ¹, Xóchitl C. Ramírez-Campanur ²

¹ Research and Postgraduate Division Study. Sciences and Engineering Faculty. Autonomous University of Tamaulipas. University Centre Adolfo Lopez Mateos, Victoria City, Tamaulipas, Mexico.

² Postgraduate Division Study of the Architecture Faculty, Universidad Michoacana de San Nicolas de Hidalgo. Francisco J. Múgica S/N Av., Ciudad Universitaria, Morelia, Michoacán, México.

Abstract: Northeast Mexico is well known for having dry climate; nevertheless, agriculture and livestock stand among the top economic activities of the state. For the last 24 years, Tamaulipas has been struck by numerous meteorological droughts which have caused financial losses in the country. This work studied meteorological drought in Tamaulipas through remote sensing techniques and IMERG satellite imagery for accumulated precipitation for 2000-2023 period. Downloaded IMERG images were projected to WGS 84/ UTM14N, cropped in the shape of Tamaulipas and processed to compute the Precipitation Condition Index (PCI). As a method of validating, the Pearson Lineal Correlation was calculated for PCI results and Standardized Precipitation Index for one (SPI-1) and three (SPI-3) months. Six years are clearly identified as meteorological drought years: 2000, 2006, 2009, 2011, 2022 and 2023. Results obtained by PCI show drought events that match the dry years identified by the SPI. Furthermore, dry years are closely related to the low-activity annual tropical cyclone season in the Gulf of Mexico. PCI and SPI-1 in the northern region showed the best correlation between them (r=0.87), while the worst correlation was found between PCI and SPI-3 in the southern region (r=0.44), from which it is concluded that PCI is a meteorological drought index capable and adequate for monitoring meteorological drought in Tamaulipas and in similar weather conditions.

Key words: PCI, IMERG, Mexico, Drought, SPI.

Sequía meteorológica utilizando el Índice de Condición de la Precipitación en el noreste seco de México durante el período 2000-2023

Resumen: El noreste de México se caracteriza por tener un clima seco; a pesar de ello, la agricultura y la ganadería son las principales actividades económicas del Estado. En los últimos 24 años, Tamaulipas se ha visto afectado por numerosos eventos de sequías que han resultado en pérdidas económicas en el campo. El objetivo de esta investigación fue la identificación de sequías meteorológicas utilizando técnicas de teledetección con imágenes IMERG de precipitación acumulada, para el período 2000-2023. Las imágenes descargadas fueron reproyectadas a la proyección WGS 84 / UTM 14N y utilizadas para calcular el Índice de Condición de la Precipitación (ICP). El estado de Tamaulipas para fines prácticos se dividió en tres partes al norte, al centro y al sur. Como método de validación, se correlacionaron los resultados obtenidos por el ICP con los del Índice Estandarizado de Precipitación a uno (SPI-1) y a tres (SPI-3) meses usando la correlación de Pearson. Los resultados principales muestran claramente seis años de sequía: 2000, 2006, 2009, 2011, 2022 y 2023. Los eventos de sequía obtenidos por el ICP coinciden con los años secos identificados por el SPI y con los años de menor actividad ciclónica en el Golfo de México. El ICP y el SPI-1 en la región del norte mostraron la mejor correlación lineal entre ellos (r = 0.87), mientras que la peor correlación se encontró entre el ICP y el SPI-3 en la región del sur (r = 0.44), por lo que se concluye que el ICP es un índice de sequía adecuado para el monitoreo de la sequía meteorológica en Tamaulipas y en climas similares.

Palabras clave: ICP, IMERG, México, Sequía meteorológica, SPI.

1. Introduction

Drought is an unavoidable, world-wide natural climate phenomenon and it is considered as one of the most human life threatening climate-related events (Ortega-Gaucin et al., 2018). Spinoni et al. (2013) mentions that from the year 2000 onward, drought frequency, intensity and magnitude has been on the rise in some parts of the planet like the Mediterranean, Central Africa, East Asia and North America. Long, rainless periods lead to less water availability for human consumption and ecosystem functionality (Agustín-Canales et al., 2023), as well as the decrease in crop production and the increase in potential wildfire regions (Veneziano & García, 2022). Due to its position in-between the two tropics, Mexico is particularly exposed to drought events (Bocco et al., 2021).

In accordance with the type of impact it has in a certain region, droughts can be classified in meteorological, agricultural, hydrological, and socioeconomic (Wilhite & Glantz, 1985). Meteorological drought is defined as the climate condition where accumulated precipitation levels in a given year are significantly lower than the usual. Agricultural drought happens when crop production is reduced due to shortage of rain during a certain time period and over a specific region. Hydrological drought is related to negative anomalies between surface and subterranean water levels. Lastly, socioeconomic drought occurs when water demand surpasses the offer, affecting directly the society, economy and environment (Gallardo et al., 2018; Magaña et al., 2018; Mehran et al., 2015; Van Loon 2015).

In Mexico, northern states are well known for having dry weather, and frequently they suffer the consequences carried by meteorological droughts. Tamaulipas is one of those affected states, whose main economic activities are agriculture and livestock; according to data published by the Geography and Statistics Institute (INEGI, 2023), Tamaulipas ranked first national place during the final trimester of 2022 in primary activities. Agriculture-wise, corn figures as one of the most representative crops of the state, during the 2000-2023 period more than two million tonnes of corn seed were harvested with an approximate value of $5 555 753 840.00 mexican pesos (SIAP, 2024).

Plenty of methods have been developed to try to determine drought frequency, lifespan, magnitude and intensity from meteorological and hydrometric data (Ortega-Gaucin et al., 2018). The Standardized Precipitation Index (SPI), developed by McKee et al., (1993) quantifies precipitation regimes during different time scales and it is computed from monthly accumulated precipitation data, historical mean value and standard deviation of the time series; it is considered the most robust and effective index for meteorological drought determination (Macedo, 2022).

Given the frequency at which drought periods occur in Mexico, the National Weather Service (SMN) enact in 2003 the Drought Monitor (DM), tool used to monitor the evolution, magnitude, and extent of drought events throughout the country (Lobato-Sánchez, 2016). Computing of DM values involves diverse variables in addition to precipitation, such as: Vegetation Health Index (VHI), Normalized Difference Vegetation Index (NDVI) or Water Availability Portion in Dams, among others. This reason is why the DM results are not exclusively representative of the meteorological drought, but also agricultural and hydrological droughts to a limited extent (CONAGUA, 2024).

Furthermore, methodologies for computing meteorological drought indices from satellite data are available to the public. Reliable precipitation data collection on-site has proven to be a challenge (Dezfuli et al., 2017), especially when the weather stations are scattered or located in places hard to reach within the territory. In the state of Tamaulipas, only 10 % of the total operational weather stations have continuous data from 1980 to 2023 due to lack of maintenance, abandonment or simply blank spaces in the recorded data. In this sense, mathematical models based on remote sensing techniques for precipitation-estimates offer satisfactory results compared to those obtained from weather stations on-site data (Yu et al., 2022).

The Integrated Multi-Satellite Retrievals for GPM (IMERG) is an algorithm product of the joined efforts of the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA), designed to estimate precipitation values almost all over the planet at different time scales and at high spatial resolution (terrain covered by one lone pixel of the satellite image) (Jiang et al., 2021). Minimum spatial resolution for IMERG products is 10 km per pixel.

Authors like De Jesús et al., (2016) used the previous version of the IMERG satellite imagery, the TRMM (Tropical Rainfall Measuring Mission) for drought monitoring in Mexico from 1998 to 2013 and compared it to the SPI. The research concluded that TRMM mission can properly monitor meteorological droughts in the country.

The Precipitation Condition Index (PCI) is computed from accumulated precipitation satellite data, and it is capable of identify meteorological drought periods by comparing actual values with extreme historical values for a given region (Wei et al., 2021). PCI values range from 0 to 1, where values near 0 indicate extremely unfavorable precipitation conditions, while values closer to 1 represent optimum precipitation conditions (Du et al., 2013).

The objective of this investigation is to identify periods, intensity and magnitude of meteorological drought using the Precipitation Condition Index (PCI) and the Standardized Precipitation Index (SPI) during the 2000-2023 period in Tamaulipas, Mexico.

2. Materials and methods

The methodology followed in the present study can be synthesized in Figure 1.

2.1. Study area

The study area was the state of Tamaulipas as a whole, located in the northeastern part of Mexico, between coordinates 24 °17 ’14 ¨ N y 98 °33 ’48 ¨ W. Dry and warm sub-humid are the predominant climates in the region. Mean annual precipitation in Tamaulipas is 780 mm per year, most rainfall occurring mainly during summer (INEGI, 2020).

2.2. Data collection

2.2.1. Satellite precipitation data

Monthly accumulated precipitation data from the IMERG product (GPM_3IMERGM v07) was downloaded from Giovanni server [https://giovanni.gsfc.nasa.gov/giovanni/] at spatial resolution of 10 km during the 2000-2023 period in units of millimeters per month (mm/mon). The bulk download contained 283 satellite images in the “.nc” digital format. The downloaded images were then projected to the WGS 84 / UTM 14N datum, rescaled from pixel size of 10 km to pixel size of 1 km and cropped with a digital polygon in the shape of Tamaulipas. Finally, the images were stacked by month for the determination of the minimum and maximum values of the time series.

2.2.2. On-site precipitation data

In order to validate the PCI results obtained, monthly accumulated precipitation data in units of millimeters (mm) was downloaded from the period 1980 to 2023 for 15 weather stations scattered throughout the state of Tamaulipas pertaining to Mexico’s National Weather Service (SMN) [https://smn.conagua.gob.mx/es/] and 5 pertaining to USA National Weather Service (NWS) [https://www.weather.gov/] located in the border between Mexico and USA (Figure 2).

For validation purposes, the state was divided into three different regions: north, center and south. Table 1 identifies the weather stations used, coordinates in WGS 84 / UTM 14 N projection and each station corresponding region.

2.3. Precipitation Condition Index (PCI) spatio-temporal distribution

PCI was computed for each pixel monthly for all the downloaded satellite images using the following formula:

$$PCI=\frac{(IMER{G}_i-IMER{G}_{min})}{(IMER{G}_{max}-IMER{G}_{min})} (1)$$

Where IMERG_i stands for the monthly accumulated precipitation values of pixel i, IMERG_min stands for the minimum monthly accumulated precipitation value of pixel i during the study period and IMERG_max stands for the maximum monthly accumulated precipitation value of pixel i during the study period (Du et al., 2013). Table 2 shows values and possible classifications of meteorological drought for the PCI. Cartographic maps were constructed for each satellite image processed using RStudio v2022.12.0 installed packages “terra v1.7-78”, “gtools v3.9.5”, “ggplot2 v3.5.1” y “sf v1.0-17”. The generated maps correspond to the monthly spatio-temporal distribution of the PCI in Tamaulipas during the 2000-2023 period.

2.4. PCI Validation

2.4.1. Correlation between weather stations registered precipitation data and satellite registered precipitation data.

Due to the reduced amount of weather stations available in Tamaulipas that actually have enough data collection (1980 to 2023) for precipitation statistical studies, besides the poor spatial distribution and the lack of maintenance given to each station in order to achieve complete cover over the state, 20 weather stations were selected and divided into three regions based on their coordinates as shown in Figure 2. The goal of grouping the stations into regions allows for the results obtained to have a bigger cover instead of punctual information.

Weather stations on-site registered precipitation data was directly compared to the satellite registered precipitation data published by the IMERG product during the 2000-2023 period to obtain the lineal correlation between the two datasets. Monthly accumulated precipitation values for each station of each region were grouped, as stated in Table 1, and the mean accumulated precipitation value for each region was calculated. The Determination Coefficient (R²) between the on-site registered precipitation data and the satellite registered precipitation data was also obtained for each region.

2.4.2. Pearson correlation analysis between PCI, SPI-1 and SPI-3

2.4.2.1. SPI calculation

The downloaded on-site registered precipitation data was arranged in chronological order, starting in January 2000, and ending in December 2023, to compute the SPI for each of the 20 stations using the “SPEI v1.8.1” package installed in RStudio. Function “spi()” automatically makes and adjustment to the input (precipitation data) by converting it to a gamma probability function and, in later stages, adjusting it one more time to a normal distribution function with a standard deviation of one and a mean of zero (Vicente-Serrano et al., 2010).

The results obtained by the SPI are assigned to a certain meteorological drought class based on its value. Table 2 shows values and classes of meteorological drought used in SPI calculations. The SPI was computed for one (SPI-1) month and for a three (SPI-3) month period for the 20 weather stations from 1980 to 2023. Finally, mean SPI-1 and mean SPI-3 for each region were calculated as done before with the on-site registered precipitation data.

2.4.2.1. Pearson correlation

The results obtained by the PCI and by the SPI-1 for each region were correlated between them using a Pearson correlation. PCI and SPI-3 were also correlated using the same approach.

3. Results and discussion

3.1. PCI spatio-temporal distribution

For each PCI monthly calculation, a corresponding map was generated, resulting in a total of 283 maps that represent the spatio-temporal distribution of the meteorological drought in Tamaulipas during the 2000-2023 period. Figures 3 and 4 contain the annual mean PCI values for Tamaulipas.

Driest years tend to be followed by at least one humid year, an example of this can be noted in the dry period identified from 2009 to 2012 (not counting the year 2010) which presented extreme, severe, and moderate droughts. Another dry period, of lesser duration, was identified from 2022 to 2023. Individual driest years were 2000, 2006, 2009, 2011, 2022 and 2023. The longest humid period lasts from 2014 to 2017.

3.2. Surface area affected by meteorological drought

Figure 5 values are a numeric and percentage-wise representation of the visually colored annual PCI maps presented in Figures 3 and 4, and they symbolize the surface area affected by each category of meteorological drought for a given year.

A year is considered as a drought year when 60 or more percent of the territory is affected by accumulated conditions of extreme, severe, and moderate drought. Under this assumption, six years are identified by the PCI as meteorological drought years: 2000, 2006, 2009, 2011, 2022 and 2023.

The year 2000 exhibits the greatest surface area affected by extreme drought among the 24 years studied (72.6 %), nevertheless, some sites in the southern region display mild or no drought at all. This can be explained by the arrival of hurricane Keith, category 1, on October 5^th, whose trajectory was halted by the Cold Front #4; this interaction caused all the rainfall to concentrate in the southern region (CONAGUA, 2001). As published by Breña-Naranjo et al. (2015), regions with dry climate conditions in Mexico are the most benefited by tropical cyclones driven rainfall.

The beginning of the year 2006 marked the end of a humid period dating back to 2001. According to Franklin and Brown (2007), the 2006 Atlantic tropical cyclones season was the least active since 2001, with only 9 tropical storms formed and no hurricane registered to made landfall in USA territory. In an analogous way, Atlantic tropical cyclones seasons 2007 and 2008 registered 16 and 18 cyclones, respectively, in contrast with the 2009 season that displayed only 11 storms; this is well represented by the PCI results. Besides the amount of tropical cyclones formed and the trajectory they take during a season, regional precipitations are more likely to happen over continental regions if the center of the storm is located less than 500 km from the coast (Dominguez and Magaña, 2018).

The year 2010 is considered atypical and does not figure among the driest of years even though 2009 and 2011 are between it; this is because on July 2010, Tamaulipas was hit by hurricane Alex, category 2, and registered accumulated rainfall on several parts of the state ranging from 97.2 to 315.5 mm in less than 60 hours (CONAGUA, 2010). Contrary to common belief, Breña-Naranjo et al. (2015) found that the highest volume of rainfall during a tropical cyclone does not correlate to wind speed; in other words, the most intense hurricanes does not guarantee the most amount of rainfall.

At last, the dry period from 2022 to 2023 presented 37 tropical cyclones in total, but no significant storm made landfall in Tamaulipas’ shoreline during that time; these dry conditions continued well into 2024, where the dam water levels reached near historically low levels that had not been seen since the year 2000. The contribution of rainfall created by tropical cyclones has been increasing 5 % to 10 % per decade in the Atlantic coastal states of the USA, but there is still uncertainty whether tropical cyclone activity is driven by natural decadal oscillations or by global warming (Knight and Davis, 2009).

3.3. Correlation between weather stations registered precipitation data and satellite registered precipitation data

On-site registered precipitation data was compared with the satellite-registered precipitation data published in the IMERG product as exhibited in Figure 6. The 20 used weather stations were divided into three regions as shown in Table 1.

The R² values ranged from 0.75 to 0.88, the north region being the closest to 1. These results could be explained by the quality of the meteorological data collection between the NWS and the SMN. NWS datasets contain more registers, more variables and more available stations scattered through the region than SMN.

About the Pearson correlation, results show values of (r) in all regions above 0.91, the north region being the highest (r=0.94) and the south the lowest (r=0.91); these values top those obtained by Vázquez-Rodríguez et al. (2024) (r=0.80) and are close to those reported by Wei et al. (2020) (r=0.93). The (r) values demonstrate the capability of the IMERG product to be used as a reliable accumulated monthly precipitation dataset when on-site data is not available. Nevertheless, Yu et al. (2022) mentions the constant underestimations of the IMERG product when it comes to the measurement of extreme precipitation events happening both in a short and in a long-time span. Another observation of the IMERG product was the better performance as the altitude levels increased.

3.4. Pearson correlation analysis between PCI, SPI-1 and SPI-3

Annually generated PCI maps from PCI monthly data were used for the comparison between SPI-1 and SPI-3.

3.4.1. PCI and SPI-1

Researchers like Wei et al. (2021) use the SPI-1 for monitoring meteorological drought and they declare that the correlation between PCI and SPI-1 values is the highest when compared to the other SPI time periods. This could indicate the reliable nature of the PCI for short time-span drought monitoring. Figure 7 displays SPI-1 values for the north, center, and south Tamaulipas’ regions.

Since the values of the SPI-1 ranged from –2.58 to +1.88, the SPI-1 results exhibit drought years that match those recognized by the PCI. All three regions presented dry periods, sometimes surpassing the three-month mark duration, and even reaching extreme drought intensity on several occasions. Dotted line represents the start of the moderate drought conditions, so all values below that line are considered to be meteorological drought conditions.

2011 and 2022 were the years with the most intense drought conditions, presenting SPI-1 values below –2.5, matching the results obtained by the PCI for the same years. The center region displays a significant lack of precipitation data from the weather stations from 2014 to 2018. The northern region is the most frequently affected by meteorological drought conditions. The southern region showed the most intense values of drought.

3.4.2. PCI and SPI-3

On the other hand, agencies like the European Drought Observatory (2020), mention that the SPI-3 is useful for the detection of short duration drought events that can negatively impact soil moisture or superficial water flow. In a similar way, Li et al. (2024) asseverate that the SPI-3 adequately reflects the stational precipitation conditions. Figure 8 shows SPI-3 values for north, center, and south Tamaulipas’ regions. SPI-3 results also evidence drought periods that match those identified by the PCI.

Resulting values of SPI-3 ranged from -3.67 to +2.80. All three regions presented dry periods extending over the span of months and reaching extreme drought intensity. The SPI-3, as it is computed by the accumulated precipitation of the last three months, is capable of identify dry periods more precisely. The year 2011 presented extreme drought values, the lowest value was found to be in the northern region. The center region lacks data from 2014 to 2017. All three regions’ dry periods behaved in a comparable way, but the northern region stood out among the other two because of the more intense drought conditions presented.

Pearson correlation (r) values for PCI-SPI-1 and PCI-SPI-3 are shown in Table 3. Authors like Jiao et al. (2019) obtained PCI-SPI-1 and PCI-SPI-3 values of 0.73 and 0.62, respectively, while Wei et al. (2021) reported values of 0.61 and 0.44 for the same variables. In the same way, Zhang et al. (2022) stated PCI-SPI-1 and PCI-SPI-3 values of 0.87 and 0.56, respectively. All PCI values obtained in this research were consistent with data published by other researchers, this way it is demonstrated that PCI can be used successfully for meteorological drought monitoring.

4. Conclusions

PCI is an adequate meteorological drought index for a one-month time scale for monitoring the spatio-temporal distribution of the precipitation in regions where on-site registered precipitation data is deficient. Lineal correlations between on-site registered precipitation data and satellite registered precipitation data range from 0.68 to 0.87, values considered to be acceptable. PCI showed better correlations with SPI-1 than with SPI-3 for drought periods identification.

Six years were clearly identified as meteorological drought years based on the results obtained by the PCI (60 % or more of the surface area affected by moderate, severe or extreme drought conditions): 2000, 2006, 2009, 2011, 2022 y 2023. These years match the years with reduced tropical cyclone activity in the Atlantic Ocean. This research strengthens the reliability of remote sensing databases and further promotes the continued use of satellite imagery for drought monitoring purposes. While the use of remote sensing data bases is becoming more and more valid, the importance of the on-site registers does not change, if anything a well-planned distribution and maintenance program to the available weather stations in Mexico is much needed because in any given time a satellite can go off, so relying in only one data base is not recommended.

5. Acknowledgments

V.H.D.M. thanks CONAHCYT for the economic support given for the duration of the Masters’ program and to the Autonomous University of Tamaulipas for the opportunities given to present this research in conferences and publication of the paper in a scientific magazine.

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To cite this article: Domínguez-Meza, V.H., González-Gutiérrez, I., Poot-Poot, W.A., Ramírez-Campanur, X.C. 2025. Meteorological drought using the Precipitation Condition Index in dry northeast Mexico during the 2000-2023 period. Revista de Teledetección, 66, e22885. https://doi.org/10.4995/raet.2025.22885

* Corresponding author: ignacio.gonzalez@uat.edu.mx