EVALUATION OF DROUGHT FROM RAINFALL DATA FOR LOKOJA. A CONFLUENCE OF TWO MAJOR RIVERS

Michael O. Alatise, Oladunni Bimpe Ikumawoyi
Department of Agricultural Engineering, Federal University of Technology, Akure, Ondo State, Nigeria

ABSTRACT

Drought, a natural component of the climatic system, is a complex phenomenon with its attendant consequences. This study was carried out to evaluate the occurrence of drought from rainfall data for Lokoja, a confluence of two major rivers in Nigeria. Rainfall data for upward of 73 years (1931-2003) was obtained for Lokoja and four different evaluation techniques; namely, Stochastic Component Time Series (SCTS), Rainfall Anomaly Index (RAI), Cumulative Rainfall Information (CRI) and Drought Severity Index (DSI) based on quartile range were used for the analysis. It was found from this study that the Stochastic Component Time Series gave the time of occurrence of drought, while the Rainfall Anomaly Index also gave the drought years, the years with highest and lowest drought as well as the impart of drought and its severity in the study area. The Cumulative Rainfall Information was only able to monitor and assess the drought while the Drought Severity Index was also able to monitor and assess the drought by classification into driest, dry, near normal, wet and wettest drought years. On the whole the Rainfall Anomaly Index proved to be the most appropriate techniques because of its ability to supply more information on drought occurrence in the study area more than the other three techniques.

Key words: drought, evaluation technics, Nigeria.

INTRODUCTION

Drought is a natural component of the climatic system. It can be regarded as lack of sufficient water to meet essential needs. It arises from climatic irregularities and its ultimate cause is climate fluctuations and variations. Drought does not occur through sudden events such as floods or storms. There are three major forms of drought namely meteorological, hydrological and agricultural [11, 12, 13, 14].

Meteorological drought can be said to be the shortage of rainfall in close to or above normal conditions. Agnew [4], Bruce and Clark [9], Dracup et. al. [10] defined meteorological drought as the deviation of precipitation from normal over an extended period of time. Hydrological drought is associated with a deficiency in bulk water supply. It includes water levels in streams, rivers, lakes, reservoirs and aquifers.

According to Linsley et. al. [18] and Yevjevich [35], hydrological drought may be defined as a period during which streamflows are inadequate to supply established quantities or amount under a given water management system. Agricultural drought is specifically concerned with cultivated plants, as opposed to natural vegetation [15]. Due to the continuous need of inadequate water by plants, agricultural drought may set in rapidly; and can similarly terminate suddenly. It is characterized by important short-term changes to the volumetric soil moisture in the root zone [24, 33]. Thus, it is often the distribution during the rainy season that is the most relevant factor in food production [1, 3, 31, 32].

Le Houerous [17] stated that drought is experienced in almost all types of agricultural lands in the world, but that the arid lands are the most susceptible. Agnew and Warren [5] described agricultural drought as a spatial phenomenon that causes significant reductions in agricultural productivity, mainly due to an inadequate supply of soil moisture. With reference to global climatic anomalies, drought is not a strange phenomenon to Africa especially the Sahel region in which the Northern part of Nigeria and the middle belt lie [20]. Adedokun [2] reported that the problem of drought and desertification has plagued the Sahelian region of West Africa in varying degrees since the early 70’s. This has subjected more than six million people to the danger of starvation, poverty and death [27]. One undisputable cause of famine in Northern Nigeria is the failure of crops resulting from insufficient or untimely rainfall. The region was severely hit by drought and famine during the Sahelian drought episode of late 1960’s to 1980’s [28].

The drought that occurred between 1972 and 1973 in Northern Nigeria resulted into death of over 3,000 animals while crop yield dropped by about 60% [36]. It as been noted also that the sub-saharan region of Nigeria is vulnerable to drought incidence and frequent occurrences of drought in the region have largely been responsible for social backwardness and general poor quality of life especially among the less privileged ones [34].

Since drought is a natural component of the climatic system, appropriate techniques should be applied to evaluate its occurrence based upon relevant data so that necessary measures can be taken to contain its serious effect on the people [6, 7, 8, 16].

There are many statistical techniques relevant to drought, but few are attached to the rainfall data while others are relevant to the stream flows, river basins, flood, dams etc [19, 21, 23, 25, 26, 30]. There are many techniques for the evaluation of drought, some of them include (i) Palmer Drought Severity Index (PDSI) used by Palmer [22] to evaluate meteorological drought in the United States of America, (ii) Stochastic Component Time Series (SCTS), (iii) Rainfall Anomaly Index (RAI), (iv) Percentiles, Dreciles and Quartiles (PDQ), (v) Drought Area Index (DAI), (vi) Drought Severity Index (DSI), (vii) Gumbel Recurrence Interval (GRI). Over the years, the incidence of drought has largely affected man, animals and even the economy one of the most prominent historical cities of Nigeria, Lokoja. The agricultural, (especially the livestock and fishery) and the hydro-electric power generation sectors have been seriously battered and the consequent effect on energy supply in Nigeria is enormous. Therefore, this study was carried out to (i) assess the drought potential of Lokoja, using long term rainfall data and (ii) determine the most appropriate technique for the evaluation of drought in the study area.

MATERIALS AND METHODS

Lokoja, a confluence on the two prominent rivers in Nigeria is located between Latitudes 8°N and 11°N and Longitudes 6°E and 7°E as shown in figure 1. It lies on the basement complex formation, which comprises mainly of quartz-feldspatic granite, gneisses and schists.

Figure 1. Map of Nigeria Showing Lokoja in Kogi State

Lokoja is of low to moderate relief with a few scattered laterites capped hills where elevations rarely exceed 400 m above sea level. There are two seasons, dry and wet; the dry season lasts between October and April in each year while the wet season lasts between May and September. The annual average rainfall ranges between 1000 mm and 1500 mm while the mean annual humidity is about 70%. The annual average temperature is about 27°C with an annual average sunshine hour of 6.7 per day. A high temperature of between 33°C and 36°C is experienced in the area during the dry months of November, December, January and February. Vegetation of the area is of the Guinea Savannah type.

Rainfall Data

Rainfall data for 73 years (1931-2003) obtained from the Meteorological Department of the Federal Ministry of Aviation, Lokoja, Nigeria was analysed using four different statistical techniques; namely:

(i) Stochastic Component Time Series (SCTS)

(ii) Rainfall Anomaly Index (RAI)

(iii) Cumulative Rainfall Information (CRI) and

(iv) Drought Severity Index (DSI) based on the Quartile range.

The Techniques. (i) The Stochastic Component Time Series (SCTS) is given by the equation.

(1)

where:
Z_t – Stochastic Component Time Series for each year,
– total annual rainfall (mm) for each year,
– mean annual rainfall (mm) for each year and,
– standard deviation of rainfall (mm) for each year.

All the parameters given in equation (1) were calculated for the 73-year rainfall period.

(ii) Rainfall Anomaly Index (RAI). In this technique, the precipitation values for the period of study were ranked in the descending order of magnitude with the highest precipitation being ranked first and the lowest precipitation being ranked last. The average of the ten highest precipitation values as well as that of the ten lowest precipitation values for the period of study was calculated. The first average is called the maximal average of 10 – extrema and the second average is called the minimal average of 10 – extrema. They are known as average precipitation of 10 – extrema for positive and negative anomalies respectively. This technique which was developed by Van Rooy [29] is given by the equation:

(2)

where:
RAI – the Rainfall Anomaly Index for each year.
– average of the annual rainfall (mm) for each year,
– average precipitation of 10 – extrema (mm) for both positive and negative anomalies.

The relationships between the RAI and the years (1931-2003) of record were drawn.

(iii) Cumulative Rainfall Information Technique. In this technique, the rainfall amounts for the 73-years were cumulated. The first and the third quartile members were calculated using the equation:

(3)

(4)

where:
Q₁ – first quartile range for the 73-year period,
Q₃ – third quartile range for the 73-year period,
n – number of years of study.

(iv) Drought Severity Index. This technique uses the same cumulative frequency distribution procedure as highlighted for the Cumulative Rainfall Information.

RESULTS AND DISCUSSION

Rainfall Distribution Pattern of Lokoja

Figure 2 shows the graph of total annual rainfall (mm) against the years of record (1931-2003) for the study area. The lowest annual rainfall of about 579 mm was experienced in 1969 while the highest annual rainfall of about 1600 mm was experienced in 1934. 59 out of the 73 years record, that is, about 81 percent; had low rainfall values while the remaining 19 percent had heavy rainfall values. The years that experienced heavy rainfall had values below mean rainfall value of 1500 mm. From figure 2, it can be seen that the rainfall of Lokoja is one of much low than heavy rainfall, which implies that Lokoja is much prone to drought. Also from figure 2, it can be seen that the year 1934 had rainfall value above 1500 mm while 1978, and 1985 had rainfall value of approximately 1500 mm. 21 out of 73-years record had rainfall values between 1200 and 1500 mm, 38 years had rainfall values between 900 and 1200 mm while 10 years had between 700 and 900 mm. It is shown in figure 2 that the trend of the rainfall pattern is Stochastic in nature.

Figure 2. Graph of Total Annual Rainfall against years of record (1931-2003)

Assessment of Drought Potential using Long – Term Rainfall Data. The Stochastic Component Series Technique. Figure 3 shows the graph of Stochastic Component Series against the years of record. 4 years, that is, 1964, 1965, 1979 and 1984 had the Stochastic Component Times Series (SCTS) of between 300 and 500, 7 years which include 1932, 1933, 1974, 1975, 1995, 1996 and 1998 had SCTS of between 100 and 300. 61, out of the 73 years record, that is, 83% had the SCTS of between 10 and 100 while the lowest SCTS of about 15 was obtained for the year 1942.

It must be noted that any year whose SCTS value is les than 20 would experience severe drought, while less than 100 and 300 would experience less severe drought. And any year whose SCTS value is less than or equal to 500 would experience least incidence of drought.

Figure 3. Graph of Stochastic Component Time series against years of record (1931-2003) for Lokoja

(ii) The Rainfall Anomaly Index. Figure 4 shows the graph of Rainfall Anomaly Index against the years of record. It can be seen from this figure that the Rainfall Anomaly Index exhibits linear trend; the linearity tapers off at lower index values indicating slightly different responses to more severe drought. The year 1934 had the highest Rainfall Anomaly Index while 1942 had the least. Figure 4 shows that drought occurred in the study area throughout the 73 years of record. 30 years out of the 73 years had positive Rainfall Anomaly Index values while the remaining 43 years had negative Rainfall Anomaly Index values. Since the anomalies decrease down the graph as shown in figure 4, therefore the years with negative anomaly index values experienced more severe drought than those years with positive anomaly index. The significance of the two curves in figure 4 shows that the anomaly index either positive or negative has almost the same drought period. Also, the rainfall values decrease down the graph.

Figure 4. Graph of Rainfall Anomaly index against Years of record (1931-2003) for Lokoja

(iii) The Cumulative Rainfall Information. Figure 5 shows the Cumulative Rainfall Information for the 73 years of record. A cumulative departure of rainfall from mean conditions can show long-term tendencies in water availability. The cumulative frequency curve is particularly useful for the evaluation of drought as well as drought periods so as to prevent water shortage during the drought years. When comparing rainfall frequencies at different stations or different months or years at the same station, for example, the Cumulative Information can be used to monitor the station. The average rainfall for the study area was 1126 mm while the mean annual rainfall was 1500 mm; this shows that drought occurred in the study area because of the total annual rainfall was low.

Figure 5. Graph of Cumulative Rainfall information against years of record (1931-2003) for Lokoja

(iv) Drought Severity Index Based on Quartile Range. This was used to monitor the drought in the study area especially, meteorological drought which is obtained by the classification of the drought severity. The minimum amount of cumulative rainfall for the 73 years was 898 mm while the maximum cumulative rainfall was 82,189 mm. The first and third quartile ranges were 20,140 mm and 62,648 mm respectively. The ranges show the range of rainfall at which drought occurred in the area. Drought Severity Index based on quartile range for Lokoja between 1993 and 2003 is as given as follows:

< 898 mm – for the driest period on record,
898-20,140 mm – for dry period,
20,140-62,468 mm – for near normal period,
62,648-82,189 mm – for wet period,
> 82,189 mm – for wettest period record.

Determination of the Most Appropriate Technique for the Evaluation of Drought in the Study Area. All the four techniques used for the evaluation made it possible for drought to be monitored in the study area. Also, the rainfall pattern shows the irregularities in timing and magnitude. As shown in figure 3, the Stochastic Component Time Series gave a good prediction of drought period or years; though the years with low SCTS were noted for severe drought, which was only due to the accumulation of drought over the years. It should be known that this technique only accounts for time of occurrence of drought not its severity.

The Rainfall Anomaly Index incorporates a ranking procedure to assign magnitudes to positive and negative precipitation anomalies, the annual rainfall amounts for each year were arranged in descending order of magnitude. In figure 4, the linearity shows reduction of the index values, indicating that drought severity increases down the curve. In this study, the Rainfall Anomaly Index was used to monitor the drought in the area, predict the drought years and shows the years with the highest and least severity. Through this, the impart of drought and severity were known. The years of moderate, extreme and exceptional drought were also known. The Cumulative Rainfall Information technique was only used to monitor and assess the drought in the study area but could not give details about the years with drought severity and impact.

The Drought Severity Index based on quartile range was used to monitor and assess drought by classification into driest, dry, near normal, wet and wettest. Considering the four techniques used to evaluate the drought in Lokoja, it can be seen from above that the most appropriate technique is the Rainfall Anomaly Index.

CONCLUSION

In this study, a 73-year (1931-2003) rainfall data for Lokoja, a confluence on two prominent rivers in Nigeria was collected for the evaluation of drought in the area.

Four major techniques namely, the Stochastic Component Time Series (SCTS), the Rainfall Anomaly Index (RAI), the Cumulative Rainfall Information (CRI) and the Drought Severity Index (DSI) based on quartile range were used to evaluate the occurrence of drought in the study area.

The Stochastic Component Time Series gave the time of occurrence of drought; the Rainfall Anomaly Index also gave the drought years and years with moderate, extreme and exceptional drought. Through this, the impart of drought and its severity were known.

The Cumulative Rainfall Information only monitored and assessed drought while the Drought Severity Index based on quartile range was used to monitor and assess the drought by classification into driest, dry, near normal, wet and wettest. Of the four techniques, the Rainfall Anomaly Index proved to be most appropriate technique because of its ability to give more information on the occurrence, severity and impact of drought in the study area.

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Accepted for print: 16.01.2007

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Michael O. Alatise
Department of Agricultural Engineering,
Federal University of Technology, Akure, Ondo State, Nigeria
PMB 704, Akure, Ondo State, Nigeria
email: micalatise@yahoo.com

Oladunni Bimpe Ikumawoyi
Department of Agricultural Engineering,
Federal University of Technology, Akure, Ondo State, Nigeria
PMB 704, Akure, Ondo State, Nigeria

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