GloRESatE: A dataset for global rainfall erosivity derived from multi-source data

Subhankar Das; Manoj Kumar Jain; Vivek Gupta; Ryan P McGehee; Shuiqing Yin; Carlos Rogerio de Mello; Mahmood Azari; Pasquale Borrelli; Panos Panagos

doi:10.1038/s41597-024-03756-5

GloRESatE: A dataset for global rainfall erosivity derived from multi-source data

Sci Data. 2024 Aug 27;11(1):926. doi: 10.1038/s41597-024-03756-5.

Authors

Subhankar Das¹, Manoj Kumar Jain², Vivek Gupta³, Ryan P McGehee⁴, Shuiqing Yin⁵, Carlos Rogerio de Mello⁶, Mahmood Azari⁷, Pasquale Borrelli^{8

9}, Panos Panagos¹⁰

Affiliations

¹ Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India.
² Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India. [email protected].
³ School of Civil and Environmental Engineering, Indian Institute of Technology Mandi, Mandi, India.
⁴ Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa, USA.
⁵ Faculty of Geographical Science, Beijing Normal University, Beijing, China.
⁶ Water Resources Department, Federal University of Lavras, Lavras, Brazil.
⁷ Department of Range and Watershed Management, Ferdowsi University of Mashhad, Mashhad, Iran.
⁸ Department of Science, Roma Tre University, Rome, Italy.
⁹ Department of Environmental Sciences, Environmental Geosciences, University of Basel, Basel, Switzerland.
¹⁰ European Commission, Joint Research Centre (JRC), Ispra, Italy.

Abstract

Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.1° × 0.1° spatial resolution) integrating satellite (CMORPH and IMERG) and reanalysis (ERA5-Land) derived rainfall erosivity estimates with gauge rainfall erosivity observations collected from approximately 6,200 locations across the globe. We used a machine learning-based Gaussian Process Regression (GPR) model to assimilate multi-source rainfall erosivity estimates alongside geoclimatic covariates to prepare a unified high-resolution mean annual rainfall erosivity product. It has been shown that the proposed rainfall erosivity product performs well during cross-validation with gauge records and inter-comparison with the existing global rainfall erosivity datasets. Furthermore, this dataset offers a new global rainfall erosivity perspective, addressing the limitations of existing datasets and facilitating large-scale hydrological modelling and soil erosion assessments.