Abstract:
The present study analyzed the geospatial drought occurrences in the Sudan-Sahelian region of
Nigeria. This was done using the standard precipitation drought index application for the study
area and it was evaluated accordingly by historical climatic parameters for the study using Climate
Research Unit (CRU) data retrieved from the Climate Research Unit website from 1956-2015 and
Landsat NDVI biweekly data was downloaded from the USGS Earth Explorer for the years 1985
to 2014 using ArcGIS 10.4. Variation analysis was carried out over the study area for all
climatic parameters (rainfall, temperature, soil moisture and potential evapotranspiration)
from (1956-2015) using yearly timescale for graphs and decadal timescales for geospatial
views. The long term rainfall records were analyzed using Standard Precipitation Index (SPI).
Also, analysis of LANDSAT NDVI biweekly data for each month of the year in the region from
1985-2014, and subsequent NDVI calculation was done to know the response of vegetation to
rainfall within the study area. Correlation techniques were further used to verify if there is
relationship between NDVI and rainfall in the study area over the period and used to produce
drought risk classification graphs and spatial vulnerability maps. From the result, the worst drought
years of 1973, 1983, 1984, and 1987 in the Sudan Sahel region of Nigeria indicate severe dryness
and hence, the irrigation requirement can be evaluated on the rainfall deficits & its severity for the
given years. The drought episode of the 80’s was also identified in this research to be more
devastating, due to its prolonged severe and moderate drought conditions in 1985 and 1986 which
later end up as an extreme case in 1987. The six (6) months (May-October), three (3) months (July-
September) and August SPI analysis was also carried out to this effect to ascertain the varying
degrees of drought conditions. Meanwhile, Mean NDVI for the growing season ranges from 0.220
to 0.548 across to the study area throughout the time series and the NDVI values greater than 0.360
corresponded well with the rainy season from May to October. The obtained correlation between
NDVI and rainfall was 0.61 which showed a positive relationship between NDVI and rainfall for
the study period. This could be useful tool in mean monthly rainfall estimation over the region.
The NDVI anomaly is observed to be similar to that of rainfall, as both have shown positive
anomalies in most of the years and weak negative anomalies in few years.
