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

Autor: Subhankar Das, Manoj Kumar Jain, Vivek Gupta, Ryan P. McGehee, Shuiqing Yin, Carlos Rogerio de Mello, Mahmood Azari, Pasquale Borrelli, Panos Panagos
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Data, Vol 11, Iss 1, Pp 1-18 (2024)
Druh dokumentu: article
ISSN: 2052-4463
08130434
DOI: 10.1038/s41597-024-03756-5
Popis: 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.
Databáze: Directory of Open Access Journals
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