Reliability Assessment of 15 Gridded Rainfall Datasets for the Construction of a Daily High-Resolution Reanalysis across Senegal for Agroclimatic Applications.
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| Title: | Reliability Assessment of 15 Gridded Rainfall Datasets for the Construction of a Daily High-Resolution Reanalysis across Senegal for Agroclimatic Applications. |
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| Authors: | Mbengue, A.1,2 (AUTHOR) asse.mbengue@anacim.sn, Sultan, B.2 (AUTHOR), Faniriantsoa, R.3 (AUTHOR), Ndiaye, O.1 (AUTHOR), Diongue-Niang, A.1 (AUTHOR), Satgé, F.2 (AUTHOR), Diop, M. L.1 (AUTHOR), Ndiaye, D.1 (AUTHOR), Konte, O.1 (AUTHOR) |
| Source: | Journal of Applied Meteorology & Climatology. Nov2025, Vol. 64 Issue 11, p1561-1583. 23p. |
| Subjects: | Senegalese, Dry farming, Reliability in engineering, Research methodology, Hydrometeorology, Climate change |
| Geographic Terms: | Senegal |
| Abstract: | This study focuses on developing a new high-resolution gridded rainfall dataset for Senegal, essential for supporting rainfed agriculture, which is sensitive to climate variability. Given the limited number of rain gauges, the research evaluates 15 publicly available gridded rainfall datasets (P datasets) against data from 21 stations of the Senegalese National Meteorological Service (ANACIM) over a 17-yr period (2005–21). The evaluation employs several agroclimatic indices, including the onset and cessation of rain, duration of the rainy season, and extreme events. The findings reveal that the reliability of P datasets varies significantly based on the metrics used. For total rainfall, African Rainfall Climatology, version 2 (ARC2), Climate Hazards Infrared Precipitation with Station (CHIRPS), ERA5, and Rainfall Estimation Algorithm, version 2 (RFEv2) emerged as the most reliable datasets, with ERA5 achieving the highest Kling–Gupta efficiency (KGE) value of 0.81 at daily scale. In terms of agroclimatic parameters, ARC2, CHIRPS, and RFEv2 excelled in accurately representing the start (KGE ≥ 0.45) and end (KGE ≥ 0.39) dates of the rainy season. However, P datasets generally overestimate rainfall events and struggle with identifying dry spells. The newly constructed merged dataset (M dataset) demonstrated over 100% improvement in correlation for daily estimates and significant bias reductions: 99.19% for ARC2, 80% for CHIRPS, and 90.57% for RFEv2. This research provides critical insights for selecting appropriate datasets to enhance climate information for agricultural decision-making in Senegal. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Applied Meteorology & Climatology is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189362608 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Reliability Assessment of 15 Gridded Rainfall Datasets for the Construction of a Daily High-Resolution Reanalysis across Senegal for Agroclimatic Applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mbengue%2C+A%2E%22">Mbengue, A.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> asse.mbengue@anacim.sn</i><br /><searchLink fieldCode="AR" term="%22Sultan%2C+B%2E%22">Sultan, B.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Faniriantsoa%2C+R%2E%22">Faniriantsoa, R.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ndiaye%2C+O%2E%22">Ndiaye, O.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Diongue-Niang%2C+A%2E%22">Diongue-Niang, A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Satgé%2C+F%2E%22">Satgé, F.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Diop%2C+M%2E+L%2E%22">Diop, M. L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ndiaye%2C+D%2E%22">Ndiaye, D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Konte%2C+O%2E%22">Konte, O.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Meteorology+%26+Climatology%22">Journal of Applied Meteorology & Climatology</searchLink>. Nov2025, Vol. 64 Issue 11, p1561-1583. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Senegalese%22">Senegalese</searchLink><br /><searchLink fieldCode="DE" term="%22Dry+farming%22">Dry farming</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability+in+engineering%22">Reliability in engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrometeorology%22">Hydrometeorology</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Senegal%22">Senegal</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study focuses on developing a new high-resolution gridded rainfall dataset for Senegal, essential for supporting rainfed agriculture, which is sensitive to climate variability. Given the limited number of rain gauges, the research evaluates 15 publicly available gridded rainfall datasets (P datasets) against data from 21 stations of the Senegalese National Meteorological Service (ANACIM) over a 17-yr period (2005–21). The evaluation employs several agroclimatic indices, including the onset and cessation of rain, duration of the rainy season, and extreme events. The findings reveal that the reliability of P datasets varies significantly based on the metrics used. For total rainfall, African Rainfall Climatology, version 2 (ARC2), Climate Hazards Infrared Precipitation with Station (CHIRPS), ERA5, and Rainfall Estimation Algorithm, version 2 (RFEv2) emerged as the most reliable datasets, with ERA5 achieving the highest Kling–Gupta efficiency (KGE) value of 0.81 at daily scale. In terms of agroclimatic parameters, ARC2, CHIRPS, and RFEv2 excelled in accurately representing the start (KGE ≥ 0.45) and end (KGE ≥ 0.39) dates of the rainy season. However, P datasets generally overestimate rainfall events and struggle with identifying dry spells. The newly constructed merged dataset (M dataset) demonstrated over 100% improvement in correlation for daily estimates and significant bias reductions: 99.19% for ARC2, 80% for CHIRPS, and 90.57% for RFEv2. This research provides critical insights for selecting appropriate datasets to enhance climate information for agricultural decision-making in Senegal. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Applied Meteorology & Climatology is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189362608 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/JAMC-D-24-0238.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1561 Subjects: – SubjectFull: Senegalese Type: general – SubjectFull: Dry farming Type: general – SubjectFull: Reliability in engineering Type: general – SubjectFull: Research methodology Type: general – SubjectFull: Hydrometeorology Type: general – SubjectFull: Climate change Type: general – SubjectFull: Senegal Type: general Titles: – TitleFull: Reliability Assessment of 15 Gridded Rainfall Datasets for the Construction of a Daily High-Resolution Reanalysis across Senegal for Agroclimatic Applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mbengue, A. – PersonEntity: Name: NameFull: Sultan, B. – PersonEntity: Name: NameFull: Faniriantsoa, R. – PersonEntity: Name: NameFull: Ndiaye, O. – PersonEntity: Name: NameFull: Diongue-Niang, A. – PersonEntity: Name: NameFull: Satgé, F. – PersonEntity: Name: NameFull: Diop, M. L. – PersonEntity: Name: NameFull: Ndiaye, D. – PersonEntity: Name: NameFull: Konte, O. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 15588424 Numbering: – Type: volume Value: 64 – Type: issue Value: 11 Titles: – TitleFull: Journal of Applied Meteorology & Climatology Type: main |
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