Variability and the Severity of the “Little Dry Season” in Southwestern Nigeria

Abstract

The Little Dry Season (LDS) of West Africa is manifested as a decline in both the frequency and amount of daily rainfall for a number of weeks halfway through the rainy season. The mean or climatological LDS is derived from the slope of the cumulative percentage graph of 5-day mean rainfall (daily rainfall data between 1961 and 2000). LDS variability analysis was carried out using the concept of relative variability. The results obtained showed that LDS is observed from mid-July to mid-September along the coast. Northward and eastward the period of occurrence decreases. In general, the phenomenon is not observed north of the eastward-flowing or east of the southward-flowing River Niger. The results also show considerable interannual variability. Variability was highest along the southwestern coast and declined inland northward and eastward. Variability was highest with respect to total rainfall, followed by length and number of rain days. There are indications that for most years the LDS was only relatively dry while in certain years it represented a period of drought. The occurrence of the LDS in space and time is explained by the movements of the intertropical discontinuity and its associated zone of rainfall. Interannual variability in occurrence and severity are determined by the Walker Circulation phenomenon. Variability in the severity of the LDS has mixed implications for agricultural practices.

Towards a wetter Sudano‐Sahelian ecological zone in twenty‐first century Nigeria

“…Of the known factors influencing the climate of West Africa in general, and Nigeria in particular, the northward and southward migration of the ITD and its associated air masses (mT and cT) appear to be the most popularly accepted (Lamb, 1983;Adejuwon et al, 1990;Odekunle, 2004;Adejuwon and Odekunle, 2006). It has been established that rain falls mostly when an area is overlain by the mT air mass and ceases when the area is overlain by the cT air mass, thereby making the latitudinal position of the ITD a strong determinant of most rainfall attributes.…”

Relating seasonal dynamics of enhanced vegetation index to the recycling of water in two endorheic river basins in north-west China

“…Most methods used in establishing the onset and cessation of the precipitation season usually aim to determine the effective planting date of crops (Adejuwon et al, 1990;Adejuwon and Odekunle, 2006;Benoit, 1977;Ilesanmi, 1972). In these methods, the onset and end of the precipitation season is equated to the onset and end of the growing season (Benoit, 1977;Odekunle et al, 2005).…”

“…These methods do not help clarify the relationship between the onset of the growing and precipitation seasons, when the seasons are not entirely synchronised. Cumulative % precipitation (Ilesanmi, 1972) is the most widely used indicator of the onset and cessation of the precipitation season independent of other climatic and vegetation factors (Adejuwon et al, 1990;Adejuwon and Odekunle, 2006;Odekunle, 2006). In this method, daily % precipitation data are processed to generate 5-day means.…”

Application of GIS to assess rainfall variability impacts on crop yield in Guinean Savanna part of Nigeria

“…Among these are the works by Tim (2000), FAO (2001), Chiew (2002), , Adejuwon and Odekunle (2006), and Haimson and Ennis (2004). For example, Tim (2000) observed that over the period 1961 to 1990 the north east arid zone of Nigeria experienced a decline in annual rainfall which led to decline in millet based farming systems.…”

“…For example, Tim (2000) observed that over the period 1961 to 1990 the north east arid zone of Nigeria experienced a decline in annual rainfall which led to decline in millet based farming systems. The zonal variability of rainfall, especially, is observed to bring about not only the differences in the types of crops cultivated but also the rate of yield of such crops (Osagie, 2002;Adejuwon and Odekunle, 2006). Moreover, a lot of studies on interannual rainfall variability impact on crop yield used model-base simulations in their analysis.…”