| 05 Aug 2025
The first rainfall erosivity database in Mexico: facing challenges of leveraging legacy climate data
Abstract. Soil water erosion (SWE) is the dominant soil degradation driver on a global scale. For quantifying SWE, erosivity is an index that reflects the potential (i.e., the energy) of rainfall to cause SWE. To support large-scale SWE studies and the assessment of the SWE process at the national scale in Mexico, the objectives of this research are a) to develop the first Mexican rainfall time series database for three climate normals CNs (1968–1997, 1978–2007, and 1988–2017) leveraging legacy climate data, and b) to estimate rainfall erosivity across continental Mexico by using daily rainfall time series. The workflow has three methodological moments: 1) development of the daily rainfall time series database, 2) identification of the best empirical relationship to estimate daily rainfall erosivity, and 3) estimation of the rainfall erosivity across Mexican territory. We compiled and harmonized 5410 rainfall time series (RTS) well distributed across the Mexican territory. We perform quality control and assurance, homogeneity analysis (using the normal homogeneity test), and the data gap-filling process (using the proportion method). Then, we tested three combinations of the α and β coefficients, proposed by three authors, in a power model to estimate rainfall erosivity; in this step, we used three validation databases (global, national, and local scales). Finally, we estimated the annual rainfall erosivity for all three CNs with multiple combinations of α and β coefficients. As principal results, the new database includes 1370, 1678, and 1676 RTS for each CN and its corresponding rainfall erosivity. The best parameter combination is the one proposed by Richardson et al. (1983) for all three validation databases. For the global and national databases, we observe a positive bias (Mean error of 956 and 324 MJ mm ha-1 h-1 yr-1, respectively); in contrast, for the local database, results show a negative and higher bias (Mean error of -3699 MJ mm ha-1 h-1 yr-1). About the erosivity estimation across the Mexican territory, the median values for rainfall erosivity for the three CNs were 3245, 3070, and 3327 MJ mm ha-1 h-1 yr-1, respectively. The statistical distribution of the erosivity values was right-skewed for the three CNs, with high erosivity values reaching >12000 MJ mm ha-1 h-1 yr-1 in all three CNs. The behavior throughout the year of the rainfall erosivity was similar for the three CNs. However, September had the highest contribution to the rainfall erosivity. The new database provides daily climatological data and analysis across Mexican territory through a multi-year period (1968 to 2017). Rainfall erosivity results support the study of SWE at the national scale by identifying areas with higher susceptibility to soil loss due to rainfall action and providing a more spatially dense and well-documented rainfall erosivity database. Following the FAIR principles (Findability, Availability, Interoperability, and Reproducibility) for scientific data, this database is available from a scholarly accepted repository https://doi.org/10.6073/pasta/e0dc8bd3501f8c19bb750e853c3289cb (Varón-Ramírez et al., 2025) for public consultation.
I would first like to thank the authors for the effort invested in compiling and ‘harmonizing’ multiple historical data sources, which are not always easily accessible, particularly for researchers who do not work in Mexico. I also appreciate that the manuscript focuses not so much on a purely historical climatic analysis, but rather on how the R factor (rainfall erosivity) is estimated, whose utility—and potential future users of this dataset—is a key aspect of the work.
The manuscript “The first rainfall erosivity database in Mexico: facing challenges of leveraging legacy climate data” by Viviana Marcela Varón-Ramírez and colleagues provides a detailed dataset of historical precipitation time series for Mexico, applied to the estimation of rainfall erosivity. The dataset itself, as well as the calibration using several available empirical models, is interesting and represents a solid starting point. However, the applicability of the dataset would benefit from being presented more clearly, particularly in terms of its potential users and intended applications.
I do not have major concerns regarding the core content of the manuscript, and I have provided specific comments throughout the text that I hope the authors will find helpful and intuitive to address. My main concern—which may require more substantial work—does not relate to the dataset itself or its calibration, but rather to the discussion section. In its current form, the discussion is unsatisfactory and does not allow the reader to properly assess the potential usefulness or relevance of the dataset.
The discussion needs to be completely restructured in a more organized and focused manner, selecting and developing the strongest points of the article (some suggestions are provided in the annotated manuscript). In this sense, I consider and expect that this manuscript will be accepted subject to the revisions (they fall between minor and majors) so if authors handle the chaotic way in which they currently present their results I believe this paper can make in through and be a valuable asset for people in need of R-factor data/maps, etc.
If possible, it would be highly valuable for the authors to incorporate the suggestions provided in the manuscript so that the significant effort invested in compiling historical data for Mexico can be communicated more clearly and effectively. Revising the discussion may also require supporting it with a broader range of references than are currently included, depending on the final focus the authors choose to adopt.
Specific comments:
Authors can follow up -in my opinion- my comments and suggestions in a better way when they check their original manuscrit with anotated comments. I hope the editor finds this suitable given this manuscript structure of a ‘dataset-paper-like’.
typing errors are shortlisted within the original manuscript