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Abstract In recent years, open and data‐driven science has fostered very important scientific breakthroughs. This study describes the challenges and opportunities for the scientific community devoted to bed form dynamics research in adopting such scientific paradigms through, for example, engineered data sharing, formal recognition of scientists who collect the data, and collaborative development of free accessible software. It highlights that once these actions are completed, the potential application of deep learning techniques could substantially improve bed form models and the scientific understanding of bed form dynamics. Likewise, it discusses the potential of Bedforms‐ATM, a free available software, to standardize some bed form data analysis techniques. We propose that the technical challenges be tackled by following scholarly accepted/proposed standards (e.g. FAIR Guiding Principles, Geoscience Papers of the Future), using the body of knowledge being built on the matter by some institutions (e.g. Federation of Earth Science Information Partners), and through technical discussions at scientific meetings such as MARID. © 2020 John Wiley & Sons, Ltd.

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.

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