Articles | Volume 18, issue 7
https://doi.org/10.5194/essd-18-5423-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-5423-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
27 Jul 2026
Data description article |
| 27 Jul 2026
| 27 Jul 2026
The rainfall and erosivity database for Mexico (1968–2017)
Viviana Marcela Varón-Ramírez
CORRESPONDING AUTHOR
Instituto de Geociencias, Universidad Nacional Autónoma de México, Campus UNAM Juriquilla, Querétaro, Mexico
Posgrado en Ciencias de la Tierra, Instituto de Geociencias, Universidad Nacional Autónoma de México, Campus UNAM Juriquilla, Querétaro, Mexico
Douglas Andrés Gómez-Latorre
Corporación Colombiana de Investigación Agropecuaria – AGROSAVIA, Centro de Investigación Tibaitatá, Mosquera-Cundinamarca, Colombia
Carlos Eduardo Arroyo-Cruz
Instituto de Geociencias, Universidad Nacional Autónoma de México, Campus UNAM Juriquilla, Querétaro, Mexico
Posgrado en Ciencias de la Tierra, Instituto de Geociencias, Universidad Nacional Autónoma de México, Campus UNAM Juriquilla, Querétaro, Mexico
Alberto Gómez-Tagle
Instituto de Investigaciones sobre los Recursos Naturales – INIRENA, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Mexico
Blanca Lucía Prado Pano
Instituto de Geología – Universidad Nacional Autónoma de México, Campus UNAM Ciudad Universitaria, Ciudad de México, Mexico
Ronald R. Gutierrez
Centro de Investigación y Tecnología del Agua (CITA), Universidad de Ingeniería y Tecnología (UTEC), Lima, Peru
Deyanira Lobo-Luján
Instituto de Edafología – Universidad Central de Venezuela, Maracay, Aragua, Venezuela
Mario Guevara
CORRESPONDING AUTHOR
Instituto de Geociencias, Universidad Nacional Autónoma de México, Campus UNAM Juriquilla, Querétaro, Mexico
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Viviana Marcela Varón-Ramírez, Douglas Andrés Gómez-Latorre, Carlos Eduardo Arroyo-Cruz, Alberto Gómez-Tagle, Blanca Lucía Prado Pano, Ronald Roger Gutierrez Llantoy, Deyanira Lobo-Luján, and Mario Antonio Guevara
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-530, https://doi.org/10.5194/essd-2024-530, 2025
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This research focuses on Mexico's soil water erosion (SWE) using rainfall data to estimate erosivity. A database of daily rainfall series was developed for three climate normals –CNs– (1968–1997, 1978–2007, 1988–2017) with over 5,000 series. We found mean erosivity values of 3600, 3296, and 3461 MJ mm ha-1 h-1 yr-1 for the three CNs. The resulting publicly available datasets of rainfall series and erosivity help better understand SWE and rainfall patterns across Mexico.
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Viviana Marcela Varón-Ramírez, Gustavo Alfonso Araujo-Carrillo, and Mario Antonio Guevara Santamaría
Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, https://doi.org/10.5194/essd-14-4719-2022, 2022
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This study introduces the first global, spatially explicit dataset of tree diameter structure, capturing key aspects of forest organization, including average tree size, large-tree dominance, and within-stand variability. Built from over one million ground-based field plots combined with more than 50 satellite and environmental layers using machine learning, it provides a consistent representation of forest structure and supports ecosystem research, climate modeling, and forest management.
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Viviana Marcela Varón-Ramírez, Douglas Andrés Gómez-Latorre, Carlos Eduardo Arroyo-Cruz, Alberto Gómez-Tagle, Blanca Lucía Prado Pano, Ronald Roger Gutierrez Llantoy, Deyanira Lobo-Luján, and Mario Antonio Guevara
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Manuscript not accepted for further review
Short summary
Short summary
This research focuses on Mexico's soil water erosion (SWE) using rainfall data to estimate erosivity. A database of daily rainfall series was developed for three climate normals –CNs– (1968–1997, 1978–2007, 1988–2017) with over 5,000 series. We found mean erosivity values of 3600, 3296, and 3461 MJ mm ha-1 h-1 yr-1 for the three CNs. The resulting publicly available datasets of rainfall series and erosivity help better understand SWE and rainfall patterns across Mexico.
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Manuscript not accepted for further review
Short summary
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Human activities have disrupted the global carbon cycle, increasing CO2 levels. Soils are the largest carbon stores on land, making it essential to understand how much carbon they hold to fight climate change. Our study improved estimates of soil carbon in peninsular Spain by integrating historical soil data and using machine-learning methods to create detailed maps of carbon content. These maps will help manage soil carbon better and support efforts to track carbon emissions globally.
Sergio Díaz-Guadarrama, Viviana M. Varón-Ramírez, Iván Lizarazo, Mario Guevara, Marcos Angelini, Gustavo A. Araujo-Carrillo, Jainer Argeñal, Daphne Armas, Rafael A. Balta, Adriana Bolivar, Nelson Bustamante, Ricardo O. Dart, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo, Fernando Fontes, Joan S. Gutiérrez-Díaz, Wilmer Jiménez, Raúl S. Lavado, Jesús F. Mansilla-Baca, Maria de Lourdes Mendonça-Santos, Lucas M. Moretti, Iván D. Muñoz, Carolina Olivera, Guillermo Olmedo, Christian Omuto, Sol Ortiz, Carla Pascale, Marco Pfeiffer, Iván A. Ramos, Danny Ríos, Rafael Rivera, Lady M. Rodriguez, Darío M. Rodríguez, Albán Rosales, Kenset Rosales, Guillermo Schulz, Víctor Sevilla, Leonardo M. Tenti, Ronald Vargas, Gustavo M. Vasques, Yusuf Yigini, and Yolanda Rubiano
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Short summary
Short summary
In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
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Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, https://doi.org/10.5194/essd-15-431-2023, 2023
Short summary
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The global need for updated soil datasets has increased. Our main objective was to synthesize and harmonize soil profile information collected by two different projects in Ecuador between 2009 and 2015.The main result was the development of the Harmonized Soil Database of Ecuador (HESD) that includes information from 13 542 soil profiles with over 51 713 measured soil horizons, including 92 different edaphic variables, and follows international standards for archiving and sharing soil data.
Viviana Marcela Varón-Ramírez, Gustavo Alfonso Araujo-Carrillo, and Mario Antonio Guevara Santamaría
Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, https://doi.org/10.5194/essd-14-4719-2022, 2022
Short summary
Short summary
These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
Katherine E. O. Todd-Brown, Rose Z. Abramoff, Jeffrey Beem-Miller, Hava K. Blair, Stevan Earl, Kristen J. Frederick, Daniel R. Fuka, Mario Guevara Santamaria, Jennifer W. Harden, Katherine Heckman, Lillian J. Heran, James R. Holmquist, Alison M. Hoyt, David H. Klinges, David S. LeBauer, Avni Malhotra, Shelby C. McClelland, Lucas E. Nave, Katherine S. Rocci, Sean M. Schaeffer, Shane Stoner, Natasja van Gestel, Sophie F. von Fromm, and Marisa L. Younger
Biogeosciences, 19, 3505–3522, https://doi.org/10.5194/bg-19-3505-2022, https://doi.org/10.5194/bg-19-3505-2022, 2022
Short summary
Short summary
Research data are becoming increasingly available online with tantalizing possibilities for reanalysis. However harmonizing data from different sources remains challenging. Using the soils community as an example, we walked through the various strategies that researchers currently use to integrate datasets for reanalysis. We find that manual data transcription is still extremely common and that there is a critical need for community-supported informatics tools like vocabularies and ontologies.
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Short summary
This contribution presents the first rainfall and rainfall erosivity (R-factor) database for continental Mexico across three climate normals spanning 1968–2017. A harmonized dataset of 5410 daily rainfall series was compiled, quality controlled, homogenized, and gap-filled to estimate R factors. Results reveal strong ecoregional variability, with the highest erosivity in tropical regions and the lowest in arid areas. The database supports national-scale soil erosion assessments.
This contribution presents the first rainfall and rainfall erosivity (R-factor) database for...
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