干旱区生态环境知识资源中心(Arid): Improving satellite-based global rainfall erosivity estimates through merging with gauge data

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DOI	10.1016/j.jhydrol.2023.129555
	Improving satellite-based global rainfall erosivity estimates through merging with gauge data
	Fenta, Ayele Almaw; Tsunekawa, Atsushi; Haregeweyn, Nigussie; Yasuda, Hiroshi; Tsubo, Mitsuru; Borrelli, Pasquale; Kawai, Takayuki; Belay, Ashebir Sewale; Ebabu, Kindiye; Berihun, Mulatu Liyew; Sultan, Dagnenet; Setargie, Tadesaul Asamin; Elnashar, Abdelrazek; Panagos, Panos
通讯作者	Fenta, AA
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2023
卷号	620
英文摘要	Rainfall erosivity is a key factor that influences soil erosion by water. Rain-gauge measurements are commonly used to estimate rainfall erosivity. However, long-term gauge records with sub-hourly resolutions are lacking in large parts of the world. Satellite observations provide spatially continuous estimates of rainfall, but they are subject to biases that affect estimates of rainfall erosivity. We employed a novel approach to map global rainfall erosivity based on a high-temporal-resolution (30-min), long-term (2001-2020) satellite-based precipitation product-the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG)-and mean annual rainfall erosivity from the Global Rainfall Erosivity Database (GloREDa) stations (n = 3286). We used a residual-based merging scheme to integrate GPM-IMERG-based rainfall erosivity with GloREDa using Geographically Weighted Regression (GWR). The accuracy of the GWR-based merging scheme was evaluated with a 10-fold cross-validation against GloREDa stations. Based on GPM-IMERG-only, the global mean annual rainfall erosivity was estimated to be 1173 MJ mm ha-1 h-1 yr-1 with a standard deviation of 1736 MJ mm ha-1 h-1 yr -1. The mean value estimated via GPM-IMERG merged with GloREDa was 2020 MJ mm ha-1 h-1 yr-1 with a standard deviation of 3415 MJ mm ha-1 h-1 yr -1. Overall, GPM-IMERG-only estimates underestimated rainfall erosivity. The underestimations were greatest in areas of high rainfall erosivity. The accuracy of rainfall erosivity estimates from GPM-IMERG merged with GloREDa substantially improved (Nash-Sutcliffe efficiency = 0.83, percent bias =-2.4%, and root mean square error = 1122 MJ mm ha-1 h-1 yr-1) compared to estimates by GPM-IMERG-only (Nash-Sutcliffe efficiency = 0.51, percent bias = 27.8%, and root mean square error = 1730 MJ mm ha-1 h-1 yr -1). Improving satellite-based global rainfall erosivity estimates through integrating with gauge data is relevant as it can contribute to enhancing soil erosion modeling and, in turn, support land degradation neutrality programs.
英文关键词	GPM-IMERG GloREDa Geographically Weighted Regression Merged rainfall erosivity Erosivity density Desertification
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000990770800001
WOS关键词	GEOGRAPHICALLY WEIGHTED REGRESSION ; SOIL-EROSION ; LAND-USE ; PRECIPITATION PRODUCTS ; VARIABILITY ; REGION ; BASIN ; RESOLUTION ; MODEL ; SCALE
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397399
推荐引用方式 GB/T 7714	Fenta, Ayele Almaw,Tsunekawa, Atsushi,Haregeweyn, Nigussie,et al. Improving satellite-based global rainfall erosivity estimates through merging with gauge data[J],2023,620.
APA	Fenta, Ayele Almaw.,Tsunekawa, Atsushi.,Haregeweyn, Nigussie.,Yasuda, Hiroshi.,Tsubo, Mitsuru.,...&Panagos, Panos.(2023).Improving satellite-based global rainfall erosivity estimates through merging with gauge data.JOURNAL OF HYDROLOGY,620.
MLA	Fenta, Ayele Almaw,et al."Improving satellite-based global rainfall erosivity estimates through merging with gauge data".JOURNAL OF HYDROLOGY 620(2023).