Evidence map›Paper›PMID 38589610›Full record

ArticleScientific reports2024

An integrated modeling approach for estimating monthly global rainfall erosivity.

Ayele A Fenta, Atsushi Tsunekawa, Nigussie Haregeweyn, Hiroshi Yasuda, Mitsuru Tsubo, Pasquale Borrelli, Takayuki Kawai, Ashebir S Belay, Kindiye Ebabu, Mulatu L Berihun and 5 more

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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Ayele A FentaInternational Platform for Dryland Research and Education, Tottori University, Tottori, 680-0001, Japan. ayelealmaw@tottori-u.ac.jp.
Atsushi TsunekawaArid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan.
Nigussie HaregeweynInternational Platform for Dryland Research and Education, Tottori University, Tottori, 680-0001, Japan.
Hiroshi YasudaOrganization for Educational Support and International Affairs, Tottori University, Koyama Minami 4-101, Tottori, 680-8550, Japan.
Mitsuru TsuboArid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan.
Pasquale BorrelliDepartment of Environmental Sciences, University of Basel, 4056, Basel, Switzerland.
Takayuki KawaiGraduate School of International Resource Sciences, Akita University, 1-1 Tegatagakuen-Machi, Akita, 010-8502, Japan.
Ashebir S BelayDepartment of Earth Science, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia.
Kindiye EbabuArid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan.
Mulatu L BerihunFaculty of Civil and Water Resource Engineering, Bahir Dar Institute of Technology, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia.
Dagnenet SultanFaculty of Civil and Water Resource Engineering, Bahir Dar Institute of Technology, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia.
Tadesual A SetargieArid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan.
Abdelrazek ElnasharDepartment of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza, 12613, Egypt.
Arfan ArshadDepartment of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK, 74075, USA.
Panos PanagosEuropean Commission, Joint Research Centre (JRC), 21027, Ispra, VA, Italy.

Funding

Grants-in-Aid for Scientific Research JP22K01031Japan International Cooperation Agency JPMJSA1601Japan Science and Technology Agency JPMJSA1601
6 · The paper itself

Abstract

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001-2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission's Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha

Identifiers

PMID38589610
PMCPMC11001900

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.