An Assessment of the Changing Climate in Northern Nigeria Using Cokriging

U. Usman¹, S. A. Yelwa^2, , S.U. Gulumbe¹ and A. Danbaba¹

¹Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria

²Department of Environmental Sciences, Federal University Dutse, Jigawa State, Nigeria

Cite this paper:
U. Usman, S. A. Yelwa, S.U. Gulumbe and A. Danbaba. An Assessment of the Changing Climate in Northern Nigeria Using Cokriging. American Journal of Applied Mathematics and Statistics. 2013; 1(5):90-98. doi: 10.12691/ajams-1-5-3

Abstract

The aim of this paper is to test the applicability of Co-Kriging (CK) on the study of the changing climate in Northern Nigeria. Indices were derived from climatic variables (Rainfall and Temperature) obtained from Nigerian Meteorological Agency (NIMET) and remotely sensed data covering the period from 1981 to 2010 in the form of Normalised Difference Vegetation Index (NDVI) data derived from National Oceanic Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR). Because of the strong relationship between NDVI and Rainfall, CK method of data interpolation was tested with R-Statistical software. A digital elevation model (DEM) of the study area at 90-meter spatial resolution was used as a supplement in an overlay procedure using the IDRISI Remote sensing and GIS software so as to derive the correct altitude values of the Met stations for comparison with the coefficient of variation of the rainfall dataset. Results from the derived CK prediction maps showed that there are high variability in NDVI and rainfall across the time-series. Furthermore, spatial average variability in the growing season rainfall was 60% with a mean temperature of 4% although coefficient of variation in rainfall for the individual climatic station's ranged from 18.15 to 60.98 per cent. While the highest coefficient of variation in temperature for the entire time series (1981-2010) was located around Katsina area, the lowest was located around Minna. From the results of this analysis it is evident that the higher prediction variance values particularly for vegetation NDVI and rainfall are located in the southern part of the study area particularly around Kaduna, Minna, and Jos as compared to the northern part of the study area falling around Maiduguri, Sokoto and Katsina which indicated relatively lower prediction values. However, further studies should also be undertaken using the raster NDVI dataset in a GIS environment to buttress our view that there were changes in the general ecosystems within the study area as result of climatic impact.

Keywords:
cokriging normalized difference vegetation index remotely sensed data rainfall GIS DEM

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