Rainfall Change Detection In Africa Using Remote Sensing And Gis Between 1999 – 2018

Abdullahi Muktar (), Wali Elekwachi and Nwankwoala Hycienth
Additional contact information
Abdullahi Muktar: Department of Geography, Usmanu Danfodiyo University, P.M.B 2346, Sokoto State, Nigeria
Wali Elekwachi: Department of Geography, University of Nigeria, University Road, 410001, Nsukka, Enugu State, Nigeria.
Nwankwoala Hycienth: Department of Geology, University of Port Harcourt, P.M.B 5323, Choba, Port Harcourt, Nigeria.Author-Name: Stephen Hemba

Big Data In Water Resources Engineering (BDWRE), 2020, vol. 1, issue 2, 52-54

Abstract: Many researchers used gauge data from weather stations for rainfall estimate across Africa. Since Africa lies within the tropics, there is possibility for variations in rain received from place to place. Therefore, there is need for excessive density of the gauges for accurate estimate of Africa’s rainfall. Due to numerous challenges, these cannot be achieved. This necessitates the application of remote sensing and GIS to detect changes in rainfall amount in Africa between 1999 and 2018. The data used was obtained from remote sensing satellite (TRMM) and analyzed using GIS application (IDRISI Taiga). The Simple Image Differencing was performed on the two annual mean images covering January to December, 1999 and January to December, 2018. This provides reliable information on rainfall estimate that can complement sparsely and unevenly distributed rain gauge network in Africa. The analysis shows that latitudinal locations, to some extent, determine spatial distribution of rainfall in Africa. It is also observed that significant changes in rainfall rate are mainly found around coastal regions. It was recommended that adequate ground data it needed to confirm these findings. African countries should provide adequate and justly distributed weather stations with on-net database for easy access to the data.

Keywords: Rainfall; Africa; Remote Sensing; GIS; Image Differencing. (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://bdwre.com.my//download/14127/ (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zib:zbdwre:v:1:y:2020:i:2:p:52-54

DOI: 10.26480/bdwre.02.2020.52.54

Access Statistics for this article

Big Data In Water Resources Engineering (BDWRE) is currently edited by Engr. Saddam Hussain Wahla

More articles in Big Data In Water Resources Engineering (BDWRE) from Zibeline International Publishing
Bibliographic data for series maintained by Zibeline International Publishing ( this e-mail address is bad, please contact ).