Title : ( GloRESatE: A dataset for global rainfall erosivity derived from multi-source data )
Authors: Subhankar Das , Manoj Kumar Jain , Vivek Gupta , Ryan P. McGehee , Shuiqing Yin , Carlos Rogerio de Mello , Mahmood Azari , Pasquale Borrelli , Panos Panagos ,
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.
Keywords
, Soil Erosion, Rainfall erosivity, Global Map, satellite rainfall datasets@article{paperid:1100793,
author = {سبحانکار داس and مانوج کومار جین and ویوک گوپتا and ریان پی مک گی and شوییکینگ این and کارلوس روجریو دو مللو and Azari, Mahmood and پاسکال بورللی and پانوس پاناگوس},
title = {GloRESatE: A dataset for global rainfall erosivity derived from multi-source data},
journal = {Scientific Data},
year = {2024},
volume = {11},
number = {1},
month = {August},
issn = {2052-4463},
keywords = {Soil Erosion;Rainfall erosivity; Global Map;satellite rainfall datasets},
}
%0 Journal Article
%T GloRESatE: A dataset for global rainfall erosivity derived from multi-source data
%A سبحانکار داس
%A مانوج کومار جین
%A ویوک گوپتا
%A ریان پی مک گی
%A شوییکینگ این
%A کارلوس روجریو دو مللو
%A Azari, Mahmood
%A پاسکال بورللی
%A پانوس پاناگوس
%J Scientific Data
%@ 2052-4463
%D 2024