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Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

dc.contributor.authorAtiah, W.A.
dc.contributor.authorJohnson, R.
dc.contributor.authorMuthoni, F.K.
dc.contributor.authorMengistu, D.K.
dc.contributor.authorAmekudzi, L.K.
dc.contributor.authorKwabena, O.
dc.contributor.authorKizito, F.
dc.date.accessioned2023-08-14T10:01:14Z
dc.date.available2023-08-14T10:01:14Z
dc.date.issued2023-06-22
dc.identifier.citationAtiah, W.A., Johnson, R., Muthoni, F.K., Mengistu, D.K., Amekudzi, L.K., Kwabena, O. & Kizito, F. (2023). Bias correction and spatial disagregation of satellite-based data for the detection of rainfall seasonality indices. Heliyon, 9(7): e17604, 1-13.
dc.identifier.issn2405-8440
dc.identifier.urihttps://hdl.handle.net/20.500.12478/8238
dc.description.abstractLike many other African countries, Ghana’s rain gauge networks are rapidly deteriorating, making it challenging to obtain real-time rainfall estimates. In recent years, real-time satellite precipitation products (SPPs) development and accessibility have advanced significantly. SPPs may compliment or substitute gauge data, enabling better real-time forecasting of streamflows among other things. SPPs, on the other hand, contain considerable biases that must be addressed before the rainfall predictions can be applied to any hydrologic purpose, including seasonal or real-time forecasts. The Bias Correction and Spatial Disaggregation (BSCD) method was used in this study to bias correct daily satellite-based rainfall estimate (CHIRPS-v2) data. The researchers also looked at how the bias adjustment of daily satellite-based rainfall estimates influences the identification of seasonality and extreme rainfall indices in Ghana. The results revealed that the seasonal and annual rainfall patterns in the region were better represented after the bias correction of the CHIRPS-v2 data. We observed that, before bias correction, the cessation dates in the country’s southwest and upper middle regions were slightly different. However, they matched those of the gauge well after bias correction. The study, therefore, recommends the BCSD method for adjusting rainfall estimates from other techniques with extensive historical data that are indicative of the variability in rainfall for the specified location.
dc.description.sponsorshipUnited States Agency for International Development
dc.format.extent1-13
dc.language.isoen
dc.subjectCorrections
dc.subjectSatellites
dc.subjectRainfall Pattern
dc.subjectForecasting
dc.subjectGhana
dc.titleBias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices
dc.typeJournal Article
cg.contributor.crpMaize
cg.contributor.affiliationKwame Nkrumah University of Science and Technology
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationBotswana International University of Science and Technology
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryGhana
cg.coverage.hubEastern Africa Hub
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiometrics
cg.researchthemeNatural Resource Management
cg.identifier.bibtexciteidATIAH:2023
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectClimate Change
cg.iitasubjectFood Security
cg.iitasubjectMaize
cg.iitasubjectMeteorology and Climatology
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.journalHeliyon
cg.notesOpen Access Journal; Published online: 28 Jun 2023
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0)
cg.targetaudienceScientists
cg.identifier.doihttps://doi.org/10.1016/j.heliyon.2023.e17604
cg.iitaauthor.identifierFred Kizito: 0000-0002-7488-2582
cg.futureupdate.requiredNo
cg.identifier.issue7: e17604
cg.identifier.volume9


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