Drought spatiotemporal characterization using self-calibrating Palmer Drought Severity Index in the northern region of Nigeria

Journal Pre-proof Drought Spatiotemporal Characterization using self-calibrating Palmer Drought Severity Index in the Northern Region of Nigeria Akinwale T. Ogunrinde, Phillip G. Oguntunde, David A. Olasehinde, Johnson T. Fasinmirin, Akinola S. Akinwumiju PII:

S2590-1230(19)30088-X

DOI:

https://doi.org/10.1016/j.rineng.2019.100088

Reference:

RINENG 100088

To appear in:

Results in Engineering

Received Date: 31 October 2019 Revised Date:

11 December 2019

Accepted Date: 13 December 2019

Please cite this article as: A.T. Ogunrinde, P.G. Oguntunde, D.A. Olasehinde, J.T. Fasinmirin, A.S. Akinwumiju, Drought Spatiotemporal Characterization using self-calibrating Palmer Drought Severity Index in the Northern Region of Nigeria, Results in Engineering, https://doi.org/10.1016/ j.rineng.2019.100088. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

Drought Spatiotemporal Characterization using self-calibrating Palmer Drought Severity Index in the Northern Region of Nigeria Akinwale T. Ogunrindea,*, Phillip G. Oguntundea, David A. Olasehindeb, Johnson T. Fasinmirina, Akinola S. Akinwumijuc a

Department of Agricultural and Environmental Engineering, Federal University of Technology, Akure, P.M.B. 704, Akure, Ondo State. b Department of Agricultural and Bio‑systems Engineering, Landmark University, Omu‑Aran, Nigeria c Department of Remote Sensing and GIS, Federal University of Technology, Akure, P.M.B. 704, Akure, Ondo State.

Abstract:

Hazards from drought events has made it of utmost importance for scientist to

concentrate on the establishment of a dependable early warning systems that will enable stakeholders to effectively cope with the probable effect of this climatic phenomenon. Drought assessment in the northern region of Nigeria (NRN) was conducted majorly using a self-calibrated Palmer’s Drought Severity Index (sc-PDSI). Between 1981 and 2015, the number of drought episodes identified ranged from 5 – 18. The most severe drought episodes took place from 1998 to 2001, next to it was between 2006 and 2008. The power spectral analysis of sc-PDSI timeseries identified periodicities ranging between 3 and 18 years. The spatial extent of extreme drought episodes shows that, the peak coverage area (10 %) occurred at multiple times (August 1987, 2001, 2002 and 2009), most of which is in the 21st century. sc-PDSI reveals the long-term hydrological drought of River Niger indicated at Baro gauging station about 12 months before the low flow occurred. There is a need for policymakers to make adequate preparation by drawing out appropriate mitigation plans using established drought indices before future drought events occur.

Keywords

Drought; sc-PDSI; relative frequency; agroecological zones; spatial extent, Nigeria.

Introduction Drought is an intrinsic incident of climate that occurs in almost all part of the world. Generally, it has adverse effects on the economic, environment and the social status of humans than any other natural hazard – especially in West Africa where crude methods of farming are still predominant. There is an increasing concern on drought as the world is believed to have been experiencing a changing climate which has remarkably led to high temperature values, changes in rainfall patterns thereby leading to high variability and increase in extreme weather events in virtually all parts of the world (Vetter, 2009; IPCC, 2013). Drought occurrences leads to decrease in crop yields and livestock production due to water deficiency. For stake holders and government at all levels to properly mitigate the effects of drought, adequate knowledge derived from a comprehensive analysis of historic drought episodes will 1

offer huge assistance (Vicente-Serrano et al, 2012). Henceforth, there is the need for a sound spatial analysis of the past drought episodes and their causes. This will in turn improve the level of preparedness and adoption of viable mitigation plans without wastage as areas that are more prone to a category of drought can be made known. Spatial and temporal analysis can also help to assess the exposure of water resources, vegetation patterns and the entire environment to drought. Researches on drought all over the World have shown that drought analysis can give important information on water deficit and its impact on agriculture and the hydrology of an area, which is a prerequisite for mitigating drought and the planning of new water project. Therefore, a shift in focus to the provision of information in this direction is vital (Wagan et al, 2015; Masih et al, 2014; Vicente-Serrano et al, 2012). Despite the increase in interest by many researchers, institutions and government of countries or regions in the area of drought management, there is a limited research capacity in Nigeria to inform a dependable early warning system that can enhance the preparedness of the country to drought incidents. Adeogun (2014) suggested that the essentiality of a long-term drought analysis in Nigeria and underlined the importance of a suitable coordinated long-term field observations, assessment of drought using several indices and models to inform a reliable and efficient agricultural policy and conservation planning techniques. He finally submitted that, it is very important to adopt or develop a robust drought index for northern Nigeria. Some of the earlier studies (Ogunrinde et al, 2019; Shiru et al, 2018; Oguntunde et al, 2017; Omonigbo and Okogbue, 2014; Abdulkadir et al, 2013; Ifabiyi and Ojoye, 2013; Aremu and Olatunde, 2012) in this field in Nigeria have succeeded in using some of the common drought indices in classifying or monitoring drought, but no one has used self-calibrating Palmer Drought Severity Index (scPDSI), most especially in conjunction with Standardized Precipitation Evapotranspiration Index (SPEI) in monitoring hydrological drought. The calculating procedure for SPEI is very close to that of SPI, only that the subtraction between precipitation and ETo will be used as the input variable. PDSI have also been used for studying drought characteristics and variability across many regions of the world (Dai, 2011; Vasiliades et al, 2011; Mika et al, 2005). Despite the versatility of PDSI, the standardization values of the index were derived from limited information from a region in the USA, thereby making the PDSI values not comparable among different climatological zones. In order to solve this spatial comparability issues, Wells et al. (2004) proposed a sc-PDSI by calibrating the PDSI using some inherent climatic information of the region under study, in place of some empirical values utilized by Palmer. Though the new and the old versions of the PDSI (sc-PDSI and PDSI) are generally acceptable worldwide and have been used in many regions/countries, they have not been used to assess or monitor drought in West Africa and particularly in Nigeria. 2

The objectives of this study are therefore to characterize droughts in the northern region of Nigeria both spatially and temporally using sc-PDSI and to monitor the correlation between drought events in NRN using two meteorological drought indices. The next section gives an extensive description of the study area and the methodology adopted for the analysis. The third sub-section discusses the output of the findings based on the following: The temporal analysis of droughts; spectral analysis of scPDSI; spatial analysis of droughts in the Northern Region of Nigeria; correlation between SPEI and scPDSI and the comparison between meteorological and hydrological drought. The last section summarizes the key results of the study and gives logical recommendations to stakeholders and the government. Methodology The study area Northern Region of Nigeria (NRN) lies between latitude 6o N and 14o N and between longitude 2o E and 15o E within the Savanna region of Nigeria covering an area of 729, 815 square kilometers (Ogunrinde et al, 2019) (figure 1). NRN can be divided into three agroecological zones based on rainfall distribution. Towards the Sahara Desert is the Sahel agroecological zone, immediate below is the Sudan savannah agroecological zone and next is the Guinea savannah agroecological zone. From the top, the rainfall distribution increases downwards i.e. decrease as the Latitude increases. Despite this classification, rainfall anomaly over the years have shown that some areas may show the characteristic of another zone. The wet season in the northern areas of Nigeria last for only three to six months (May – October) depending on the agroecological zone. The temperature can range between 10 o

C in Jos Plateau or during the harmattan period to as high as over 40 °C during the hot/dry season. The

dominant soil classification in the study area are sandy loam and sandy clay. The two major jobs of the people in NRN are farming and Cattle rearing, especially among the Fulani tribe which happened to be major and influential in the region. Therefore, agriculture plays a major role in their livelihood and sustainability. Agricultural sustainability in NRN requires a good understanding of both ecological management and economic activities, since rainfall occurs only seasonally. The major crops grown in the region are during the rainy period which usually starts in May or June. Their over dependence on rainfall makes any little variation in this climatic variable disastrous to their livelihood and development. Most of the states in NRN are prone to drought. This hazard has affected the farmers in this region because they are mostly peasants and depend majorly on rainfall. In terms of the economy, this region contributes over 70 % of the Gross Domestic Product (GDP) raised from Agriculture to sustain economic activities in Nigeria. Since Agriculture remains the mainstay of NRN’s economy, poor crop yields as a result of drought may lead to mass poverty and food insecurity. Uppermost among the agroecological zones that is more susceptible to dryness and desertification is the Sahelian zone where Sokoto state is located. Sokoto state has a population of over 3 million people according to 2006 3

population census and a land area of over 25,000 square kilometers. The major crop grown in the area is cowpea (beans). The nutritive quality of the cowpea produced in this region has a reasonable protein content. This can replace the needed protein from other sources, since cowpea is an indigenous crop that requires little farm inputs for its growth and development. It is also a drought resistant crop. This crop is consumed in almost all households in Nigeria, neighbouring West Africa countries and beyond, thereby making a good income for the government, farmers and traders in the state.

(c)

Figure 1:

Map of Northern Region of Nigeria (NRN) showing the study Meteorological

stations 250

Rainfall (mm)

200 150 100 50 0 Jan

Feb

Mar

Apr

May

Jun Jul Months 4

Aug

Sep

Oct

Nov

Dec

Figure 2:

Average monthly rainfall distribution of Nigeria

800

Discharge (m³/s)

700 600 500 400 300 200 100 0 Jan. Feb. Mar

Evapotranspiration (ETo)

Figure 3:

Apr May June July Aug. Sept. Oct. Nov. Dec. Months

Average monthly discharge distribution at Baro discharge station on River Niger

18 16 14 12 10 8 6 4 2 0 Jan

Figure 4:

Feb

Mar

Apr

May

Jun Jul Months

Aug

Sep

Oct

Nov

Dec

Average month ETo at Baro discharge station on River Niger

Dataset Precipitation and temperature data for twenty-one (21) northern Nigeria meteorological stations were obtained from the National Meteorological Agency (NIMET), Nigeria. The data covered a period of 35 years from 1981 to 2015. Before the datasets were used for the analysis, they were subjected to quality control and homogeneity test. Some of which are days with missing values and possible outliers, which might have occurred due to human or measuring equipment errors. The missing values are generally few; for instance, more than 90 % of the stations had 35 years of data, while the remaining 10 % stations had between 32 and 34 years of data. The missing values were estimated by considering the three neighboring stations. The available water holding capacity (AWC) for each station was determined using the soil profile of the from the Digital Soil Map for Nigeria by Nkwunonwo and 5

Okeke (2013). The programme used for the estimation of the monthly sc-PDSI values was downloaded from http://greenleaf.unl.edu/. This programme requires four input files: namely, monthly temperature and precipitation, normal temperature and parameters (AWC and latitude of the station). The monthly temperature file holds the temperature data for each meteorological station. Each line starts with the year and the average temperature of each of the 12 months of the year. The monthly precipitation file contains the rainfall data for each of the meteorological. It follows the same pattern like that of the monthly temperature file, but instead of average, the daily rainfall values will be summed for each month of the year. The normal temperature file has the normal temperature data for each of the meteorological station. It has only 12 entries, all of which must be in one line. These values are the average monthly temperature values over all the years in consideration. The parameter file contains two numbers. The first must be AWC while the second is the latitude of the meteorological station, which must be in decimal degrees. All the four files must be properly arranged following a template available on http://greenleaf.unl.edu/ for the programme to generate sc-PDSI monthly values. Self-Calibrating Palmer Drought Severity Index (scPDSI) The estimation of scPDSI is based on water demand and supply instead of changes in rainfall (Zargar et al, 2011). Unlike SPI and some other indices, there is focus on the irregularities in moisture deficiency instead of climate or weather anomalies (Guttman, 1999). Following the method explained by Palmer (1965), the following briefly explains the procedure for estimating sc-PDSI. For all the months in the study period, four variables connected to soil moisture are calculated along their associated potential variables. The variables are evapotranspiration (ET), recharge (R), runoff (RO), potential recharge (PR), potential evapotranspiration (PET), loss (L), potential runoff (PR), and potential loss (PL) (Wells, 2004). Thornthwaite PET method (Thornthwaite, 1948) was adopted for calculating PET. The estimation of these variables solely relies on the available water holding capacity (AWC) of the soil. There are four potential values that are determined based on the climate of the area using α, β, γ and δ (known as the weighting factors) to give the existing climatic conditions (ECC). The weighting factors are also called the water-balance coefficients and can be estimated as follows:  =

  

 = =  = (1)    

The existing climatic conditions (ECC) potential values are combined to form the ECC precipitation, p, which represents the amount of precipitation needed to maintain a normal soil moisture level for a period, e.g. a month.

 =  +  +  −  (2)

where p stands for the amount of precipitation needed to maintain a normal soil moisture level for a particular month under consideration.

 =  −  =  − ( +  +  −  ) (3) 6

where d is moisture departure, p represents the value of rainfall required to stabilize the soil moisture in a normal condition for one-month period (EEC) and P is the actual rainfall recorded for the month under consideration. Palmer (1965) equation was used to complement d value in accordance to the climate of the area so as to allow a good correlation among the sc-PDSI values from the spatiotemporal perspective. =

17.67

 (4) ∑   

=  (5) where K is the moisture anomaly index,

The Z index is used to indicate the degree of dryness or wetness during a month without considering recent precipitation trends. It is also used to estimate the PDSI value in a given month by using equation 6:

1 "# = 0.897"#' + ( ) 3

# (6)

The only difference between the PDSI and sc-PDSI is the replacement of the empirical constants (K) and the duration factors (0.897 and 1/3) with values that are generated automatically based on the historical climatic information of the study site. Monthly sc-PDSI values was calculated for all the meteorological stations in NRN using the homogenized monthly precipitation and temperature data between January 1981 and December 2015. Droughts temporal analysis Drought episodes are usually indicated by a negative drought index value. A drought event starts when the value indicates negative and ceases when the value change to positive (Table 1). The number of drought episodes for each station was identified during the study period. A drought episode can be described on the basis of some drought characteristics (severity, duration and intensity). Drought duration can be defined as start and end of a drought episode, while the sum of the negative index value for the months within a drought episode is the severity and its intensity can be defined as the division of severity by duration (Oguntunde et al, 2017). Power spectral analysis was adopted in order to determine the periodicity of drought episode in the NRN using the generated sc-PDSI timeseries. Table 1:

PDSI Classification for Moisture Anomaly PDSI

Classification

≥ 4.00

Extremely wet

3.00 to 3.99

Very wet

2.00 to 2.99

Moderately wet

1.00 to 1.99

Slightly wet

0.50 to 0.99

Incipient wet spell 7

0.49 to −0.49

Near normal

−0.50 to −0.99

Incipient drought

−1.00 to −1.99

Mild drought

−2.00 to −2.99

Moderate drought

−3.00 to −3.99

Severe drought

≤ - 4.00

Extreme drought

Droughts spatial analysis The spatial analysis was conducted in order to know the areas in NRN that are more prone to droughts and to show how the region was affected by drought of various severity categories using the Natural Neighbour (NN) interpolation algorithm in ArcGIS 10.3 Desktop edition. Principal component analysis (PCA) which is a multivariate analysis technique was applied on the sc-PDSI timeseries in order to show the spatial variability of drought episodes of NRN. PCA is a method for developing new uncorrelated variables that have linear relationships with the original variables. The principal components (PCs) are calculated and arranged in a downward pattern based on their eigenvalues. This means that the first PC accounts for the highest possible variance of the total variable and the last PC accounts for the lowest contributory impact on the variance of the original variable. The PCs preserved for the spatial analysis was determined using the spree plot of the loadings (Edossa et al, 2015) [21]. Several researchers have used PCA for Precipitation and drought spatial variability studies (Oguntunde et al, 2001; Raziei et al, 2008). Standardized Precipitation Evapotranspiration Index (SPEI) The computation of SPEI requires the potential evapotranspiration (PET) and rainfall data. The following steps were used to compute the SPEI values. The first step is to estimate potential evapotranspiration (PET) using Hargreaves model. The difference (D) between the Precipitation (P) and PET for the month i was computed as shown in equation 7: Di = Pi − PETi

(7)

The calculated Di can be done on many timesteps. The difference in a given month j and year i depends on the chosen timesteps, k. i.e. the accumulated difference for one month in a particular year. A 12month timestep is as follows: + "#, = + "#,





,

#/'+' 

= ,

#'+7

-#',. + , -#,. , #

01 2 < 4, 56

-#,. , 01 2 ≥ 4, (8)

8

Di is then fitted to a three-parameter log-logistic distribution. The probability density function (PDF) of a three parameter Log-logistic distributed variable is expressed as: '

 9 − <' 9− < 1(9) = : ; =1 + : ; >   

(9)

where α, β and γ are scale, shape and origin parameters, respectively, for D values in the range (γ > D > ∞). The parameters of the log-logistic distribution can be obtained using different procedures. Among them, the L-moment procedure is the most robust (Ahmad et al. 1988). '

 < ?(9) = @1 + ( ) A 9−

(10)

Having obtained F(x), the SPEI is obtained in a standardized form of F(x). Correlation of determination (R2) was done between the outcomes of SPEI and sc-PDSI in an attempt to examine the application of SPEI in a data limited condition and different timesteps. The time lag between drought events estimated by sc-PDSI and SPEI were determined, considering that both drought indices are facilitated majorly by deficit in rainfall. Identifying this time lag will be very useful in predicting hydrological droughts in an area/region. For this study, the approximate time lag between drought episodes estimated by sc-PDSI and stream flow data were evaluated for Niger state which is in the Guinea savannah agroecological zone of Nigeria. The stream flow data of River Niger measured at the Baro gauging station was adopted to quantify hydrological drought episodes whereas meteorological drought episodes in the area were quantified from the sc-PDSI values generated. Results and Discussion Temporal analysis of droughts The characterization of drought occurrence during the rainy season was given priority in this study. The timeseries of sc-PDSI estimates during four rainy months (May, June, July and August) in Sokoto which is a semi-arid area is presented in Figures 5. The four rainy months were utilized for this analysis because more than 80 % of the rains that fall in the NRN are within this period and most of the farmers in this region are peasants that rely mostly on rainfed agriculture. Also, most of the crops are grown within these months and that the rains from these months contribute majorly to stream flows within the region. Observations from Figures 2a-d show that the number of drought episodes/events indicated for May, June, July and August were 10, 9, 10 and 10 respectively. There is one case of extreme drought event during the months except July. However, on a general note, the peak drought event during the rainy period was indicated in the year 2011. This observation corresponds to a large extent to the work

9

of Hess et al. (1995) that reported a consistent reduction in August and September precipitation in the Sahel (Sokoto) agroecological zone of Nigeria in the 20th Century. The timeseries of sc-PDSI estimates and the characterization of each drought event recorded by scPDSI in Sokoto are presented in Figure 6 and Table 2, respectively. Most severe drought event was indicated between December 1982 and July 1987, followed by the event that occurred between October 2009 and June 2011, while the event of July 1993 and June 1997 (48 months) was found to be the longest. These events fell within the historical drought record of Nigeria according to Emergency Database record (EM-DAT) and Abubakar and Yamusa (2013). Some severe historical droughts that have occasionally distressed northern Nigeria are the 1972-1973, 1984-1985, 2007 and 2011 cases. These droughts were majorly hinged on the anomalies of large-scale atmospheric circulation, wind and moisture fluctuations and the dynamical teleconnections such as Sea Surface Temperature (SST) and El-Nino Southern Oscillation (ENSO) (Oguntunde et al, 2017; Aremu et al, 2012; Oladipo, 1993; Adefolalu, 1986). 8 May

6

June

July

August

4 2 0 -2

-6

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

-4

Figure 5.

Temporal variation of drought in Sokoto in the between May and August based on sc-PDSI

10

6

sc-PDSI

4 2 0

-2 -4 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

-6

Figure 6.

Timeseries of sc-PDSI values estimated at Sokoto

Table 2.

Characterization of major drought events detected in Sokoto

Onset

End

Duration (months)

Magnitude Intensity

May-1983

Jul-1983

3

-2.65

-0.88

Jun-1988

Jun-1989

13

-15.36

-1.18

Jul-1993

Jun-1997

48

-52.49

-1.09

Sep-1998

Jul-2000

23

-47.65

-2.07

Jul-2001

Jul-2001

1

-1.78

-1.78

Jul-2002

Oct-2002

4

-2.57

-0.64

Jul-2003

Aug-2003

2

-3.16

-1.58

May-2005

May-2005

1

-1.06

-1.06

Aug-2006

Dec-2006

5

-7.82

-1.56

Jan-2007

May-2008

12

-23.83

-1.99

Oct-2009

June-2011

21

-55.22

-2.63

Sept-2014

Dec-2015

16

-10.90

-0.68

Spectral analysis of sc-PDSI Table 3 shows the periodicities of sc-PDSI timeseries in the study area. Periodicities can be referred to as a frequent occurrence in trend. The two types of periodicities are short-term and long-term depending on trend patterns. The periodogram of sc-PDSI for Sokoto indicated the highest among all

the stations considered (0.031 cycles/month ≅ 3 years/cycle). However, on a general note, multiple peaks were revealed in all stations considered based on 5 % significance level. The prevalence of 11

several periodic components in the timeseries with different frequencies may be responsible for this situation. Prevailing frequencies representing the remaining regions considered were calculated in a similar manner as shown in Table 3. The prevailing periodicities of the sc-PDSI timeseries were generally between 3 years (Minna and Jos) and 18 years (Nguru and Lokoja). Though, Nguru is located in the Sahel savannah while Lokoja is in the Guinea savannah agroecological zones, they both had similar drought characteristics. This implies that the effect of drought is not limited to any agroecological zone but cut across the NRN. This also may have significant spread to the southern part of Nigeria and other part of West Africa if similar study is carried out in the region. Moreover, this study focuses on the assessment of monotonic trends and changes over time in a timeseries dataset of some drought characteristics. Issues on the cycles and periodicities of some climatic variables that influences flooding and drought have been previously indicated in Nigeria (Oguntunde et al, 2011; 2012). For example, Oguntunde et al, (2011) reported a short and mediumwave rainfall periodicity of between 2 – 3 years and 10 – 15 years respectively with dominant peaks over all agroecological zones in Nigeria except Rain forest zone. Also, a study by Olaniran and Summer (1989) analyzed periodicities on some characteristics of rainfall during the rainy season for different agroecological zones of Nigeria from 1919 to 1985. These studies showed that, rainfall which is a primary variable in drought analysis have varying periodicities that is similar to the ones observed in the current study. One of the major factors that have been reported to have strong influence on the short to medium periodicities of precipitation in regions below 11o N is Quasi-Biennial Oscillation (QBO). QBO is one of the most significant components of short to medium-term climate variations is related to ENSO, which is the energy of subtropical high belt in both northern and southern hemispheres. These cycles may also be associated with the stratospheric QBO, which is also known as a biennial oscillation of the temperature and wind in the tropical stratosphere (Oguntunde et al, 2014). Table 3. S/N

Periodicities of the sc-PDSI time series in NRN

Station Name

Peak Frequency

Peak Period

Peak Period

(cycles/month)

(months)

(Years)

1

Bauchi

0.01203913

83

7

2

Kaduna

0.00637915

157

13

3

Kano

0.00695375

144

12

4

Sokoto

0.03113750

32

3

5

Nguru

0.00475311

210

18

6

Katsina

0.01357849

74

6

7

Yola

0.01688426

59

5

8

Makurdi

0.00650603

154

13

9

Minna

0.02853770

35

3

12

10

Gusau

0.01400706

71

6

11

Potiskum

0.00703484

142

12

12

Lokoja

0.00460263

217

18

13

Yelwa

0.01201806

83

7

14

Jos

0.02649776

38

3

15

Birni-Kebbi

0.00986788

101

8

16

Ibi

0.00772912

129

11

17

Ilorin

0.00731737

137

11

18

Bida

0.01179175

85

7

19

Zaria

0.01437555

70

6

20

Abuja

0.01892002

53

4

21

Maiduguri

0.01161231

86

7

Spatial analysis of droughts in NRN Spatial coverage of drought events in NRN The coverage area (in percentage of the study area) for sc-PDSI drought classes computed between 1981 – 2015 are displayed in Figures 7 a-d. For descriptive reason, the results presented are for four rainy months (May – August). Taking mild drought episodes into account, the maximum coverage area was indicated in June 2007, followed by July 1990 and May 1999. For example, in June 2007, about 43 % of the area witnessed mild drought event, while in July 1990 and May 1999, about 38 % of the total area recorded mild drought events respectively. Majority of the study area experienced mild drought events except in May 1997, June 1984 and August 1984. The maximum coverage area of moderate drought episodes is recorded in July 2000 and 2012 (38 % respectively), followed closely by May 2000 and July 2007 (33 % respectively). Considering severe drought events, the maximum areal coverage (29 %) occurred in July and August 2008 and August 2009. This was unlike mild and moderate drought events; the severe events were recorded only in few areas. Between June 1988 and July 1992 and some few other months like May to August 1981, May to August 1993, August 2004, 2005 and May to July 2006, there was not a record case of any severe drought event. The areal coverage of extreme drought events during the study period shows that, the maximum areal extent recorded was less than 10 %. Notable areal extents were recorded in August 1987, 2001, 2002, 2009, 2014, 2015 and June, 2011. The drought years and months increases with decreasing severity and intensity during the study period at all the stations. Shiru et al (2018) though used SPEI to evaluate drought during the crops growing season in Nigeria reported similar information about severity of drought the 21st century. However, there is more insight on the severity levels of this phenomenon over time in the region spatially. Implications from these studies are 13

that, mild and moderate drought intensities are possibly going to reoccur more in the recent future and have more coverage than it has at the moment. Therefore, short-term mitigation measures should be focused more on the mild and moderate drought intensities, while long-term measures can be directed to severe and extreme drought intensities because West Africa and many other regions are believed to experience more dry conditions as the twenty-first century progresses (IPCC, 2013). 50 Areal coverage (% of Study area)

a. Mild

May

June

July

August

40 30 20 10 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

0

May

June

July

August

b. Moderate 30 20 10

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

0

30

c. Severe

May

June

July

August

20 10 0

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Areal coverage (% of Study area)

Areal coverage (% of Study area)

40

14

May

June

July

August

d. Extreme 5

0 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Areal coverage (% of Study area)

10

Figure 7 a–d. Coverage area of droughts of various severity levels during four months (rainy period). Drought frequencies in the NRN Figure 8 shows the relative frequency of drought episodes in NRN. Drought occurrence in NRN was calculated using the relative frequency of drought episodes in a particular relative to the total number of episodes indicated during the period of study (1981 – 2015). The essence of this is to detect how the areas are susceptible to the drought of various severity levels between 1981 and 2015. Results show that extreme and mild drought events occurred most frequently in Ibi (10 and 25 % respectively), followed by Abuja (4.29 %). Howbeit, the severe drought category episode was highest in Ilorin (11.44 %), followed closely by Kano (10.48 %). Aremu and Olatunde (2013) in a study on drought trends in the Northern Region of Nigeria (NRN) between 1941 and 2010 showed that NRN witnessed an increased drought occurrence in trend on decadal basis. Even though the current study is not based on decadal trends of drought frequency, the similarity between the results of the studies are the characteristics of droughts recorded for some stations e.g. Kano and Sokoto. These studies therefore confirmed that there is no coherent time pattern for the frequency of drought in any region as stipulated in many other studies (Li et al, 2017; Kasei et al, 2010). It also reflected the high intensity of droughts recorded in the 1980’s by EMDAT and other notable studies (Oguntunde et al, 2017; Abaje et al, 2013). Considering the rapid changes in the climate of the West Africa region, understanding climatic/weather behaviour using a data driven approach, such as artificial intelligence can go a long way in helping to predict extreme events before their occurrence (Vinuesa et al, 2019). https://arxiv.org/abs/1905.00501 The spatial boundary of drought frequencies representing four drought classifications based on sc-PDSI is shown in Figures 9a-d. Mild drought episodes are predominant in the South eastern (Lokoja and Makurdi) section of the map, while the other areas experienced low to medium frequencies. Moderate drought episodes are predominant in the Central and South western part than the other areas of the map. However, the frequency of severe drought events was sparsely distributed all over the map and not concentrated in any particular part, but its influence is felt more in the North eastern, South western, 15

South eastern and the central northern parts, while the extreme drought events only concentrate in the south eastern area of the map. This result implies that historical drought events/episodes have varying frequencies in the northern region of Nigeria (NRN) and not limited only to the Sahel agroecological zone as indicated in many studies (Oguntunde et al, 2017; Oladipo, 1993).

Drought Frequency (%)

30.00 25.00

Mild drought

Moderate drought

Severe drought

Extreme drought

25.71

20.00 15.00 10.00 5.00 0.00

Figure 8.

Relative frequency of drought events in NRN

a

b

c

d

16

Figure 9 a-d. Spatial variation of drought frequency in NRN based on sc-PDSI Spatial pattern of drought using principal component analysis Figures 10 a-d indicates the spatial patterns of Principal Components (PCs) 1 to 4 based on the output of sc-PDSI. After a careful and thorough screening of the scree plot of the eigen values of all the analyzed principal components, four PCs (PC1, PC2, PC3 and PC4) were recommended for mapping because they accounted for about 67% of the total variance. The first PC indicated 36.2 % of the total

variance, the second PC accounts for 15% while the third and fourth PCs reported ≅ 8 % respectively. The maximum loading for PC1 was found majorly in the north eastern (Maiduguri axis) and also in the north central part (Kano), while the medium loadings spreads covering a large portion of the map leaving behind small pockets (Abuja and Nguru) for low insignificant loadings. Maximum loadings from PC2 were concentrated majorly in the central part (Kaduna and Bauchi) of the map and with some little portions of the north eastern part. Though PC3 and PC4 accounts for almost same variance in the principal component analysis (PCA), there mode of influence based on where the maximum loadings occurred differs. In PC3 map, the maximum loading was indicated at the outskirt of Katsina, while in PC4, it was found around the south eastern (Yola) area of the map. The general observation shows that, drought variability does not have a particular or regular pattern considering any section of the study area under PC1, PC2, PC3 and PC4 loadings. Explicitly, the monsoon system is usually correlated with some atmospheric parameters which relate in a way to dictates some characteristics of precipitation e.g. intensity and duration, which are very significant to drought. Despite the fact that the data required to link climate indices with drought characteristics are not available, such analysis is beyond the scope of the current study. However, the contributions of climate indices (SOI and NAO) to the observed variability and trends on the frequency and intensity of drought cannot be overemphasized. A report by Odekunle et al (2008) revealed that, climatic phenomena may take place at regular or irregular time intervals and may be of short or long duration or of small or large scale. Also, their consistence within a specified period of time can be defined in terms of trends, while their regular reoccurrences can be defined in terms of periodicities. In another study on the significance of some climate indices on rainfall anomaly, Odekunle et al. (2008) discovered that, the spatial and temporal patterns of some climatic phenomena such as drought and flooding may occur at either short- or long-term interval because of the influence of Southern Oscillation Index (SOI) and Northern Atlantic Oscillation (NAO).

17

b a

c

Figure 10a-d.

d

Spatial patterns of drought in NRN based on PC1, PC2, PC3 and PC4 Loadings

Correlation between SPEI and sc-PDSI Figures 11 a-d shows the relationship between SPEI four timesteps (1,3,6 and 12 months) and sc-PDSI in Kaduna located in the Sudan agroecological zone of Nigeria. The coefficient of determination (R2) increases as SPEI timescale increases. The plots also indicated that the cluster pattern of the correlation between the two indices (sc-PDSI and SPEI) changes with SPEI timesteps. The clustered points are firmer around the line of best-fit as the SPEI timestep increases, thereby showing that the relationship between the two indices become stronger as the timesteps move higher. The probable reason for the increment in the correlation (R2) values as the SPEI timesteps increases is the inherency of 18

approximately 12-month timestep in sc-PDSI (Jiang et al, 2014). This also affirms the findings of Edossa et al. (2015); Vicente-Serrano (2014), whose reports claim that sc-PDSI is more effective in monitoring long-term impacts of meteorological droughts.

Figure 11.

Relationship between SPEI and sc-PDSI at (a) 3-month and (b) 6-month time scales in Sokoto

Comparison between some episodes of meteorological and hydrological drought Figure 12 reveals the plotting of the Niger River flow at the gauging station located at Baro (1980 – 1991) close to the boundary of Niger state where Minna synoptic station is located. This comparison was done so as to establish the relationship between metrological drought episodes as indicated by scPDSI and the flows recorded at the Niger River. Low flows in rivers is majorly as a result of low amount of rainfall considering a long-term scale (Oguntunde et al, 2017). Figure 13 indicates the plot of estimated sc-PDSI values for 1980 – 1991 time period for comparative study. It was discovered that the major drought episode identified by sc-PDSI as indicated by mark 1 – 5 on both Figures 9 and 10 in Minna are followed by corresponding low river flow events at Baro gauging station. Considering the 19

first drought episode (mark 1), inferences from the analysis reveal that the lag time between the onset indicated by sc-PDSI and low river flows showed a lag time of about 12 months before meteorological drought actually translated to a long-time hydrological drought event. This finding is very similar to the works of Edossa et al (2015; 2010) that also discovered a lag of 8 and 14-months range between a meteorological drought index and river flow at Bultfontein stream gauging station in South Africa. Oguntunde et al. (2017) also reported a lag time of 2 and 3-months range between SPEI (Meteorological drought index) and Standardized Runoff Index (SRI) (Hydrological drought index).

Figure 12.

Plot of River Niger flow at Baro gauging station in Niger state

Figure 13.

Plot of sc-PDSI at Minna synoptic Station in Niger state

Summary and Conclusion This study focused on drought monitoring and assessment in the NRN using scPDSI and SPEI under historical climate (1981 – 2015). The spectral analysis of scPDSI, spatial analysis of droughts, 20

frequencies of drought, spatial pattern of drought using principal component analysis and comparison between drought episodes as indicated by scPDSI and an indicator (river flow) for hydrological drought were all carried out. The core results and its implications are as follows: •

Drought episodes recorded varied between 5 and 18 at different severity levels. The least drought episodes (5) occurred in Ibi, Gusau and Bida, while the highest (18) occurred in Sokoto and Jos. Seasonal variation of drought characteristics in Sokoto (Sahel agroecological zone) during the rainy season shows that August had the highest frequency (not in magnitude and intensity of drought) of events, while June had the least during the study period.

•

The spatial perspective of drought for the four classification of drought severity showed that mild, severe and extreme droughts concentrates more around the south-eastern area (Ibi) of the map, while the moderate category occurs more frequently in the lower left (south western) part of the map. Indications from this observation is that all the agroecological zones in NRN are predispose to drought events, since most parts recorded all classes of drought at a certain time between 1981 and 2015 as against the general opinion that only the Sahel and Sudan savannah zones are susceptible to drought in NRN.

•

Other notable findings from the study are summarized as follows; one, the peak coverage area of an extreme drought episodes during the usual rainy months considered was detected in August 1987, 2001, 2002 and 2009 (10 %). Considering the coverage area in the rainy season of severe/extreme droughts, the calendar period between 1991 and 1994 was detected as the most captious. Two, the peak coverage area of severe drought episodes in NRN was detected in July and August 2008 (29 %), followed by July 2010 (24%). Three, the number of mild/moderate drought years during the period considered for this analysis is higher when compared with all other categories of drought using sc-PDSI.

•

All the four drought categories recorded higher frequency in the earlier part of the 21st century (2000 – 2015) than in the last two decades of the 20th century (1981 – 1999).

Since majority of the inhabitants in NRN and West Africa at large have challenges facing the climatic conditions of the present-day, the future climate change will have significant impact in this region if the government and other stake holders refuse to take pungent actions. The natural resources in most part of NRN are largely limited and the population is increasing very rapidly. This situation however calls for more effective actions at resolving the problems that may be posed by drought as a result of climate change in the near future. There is a need to look at how mitigation measures can be carried out by the end users instead of solely left for the government. Another conspicuous problem is lack of a coherent policy to deal with natural hazards e.g. drought and flood. Therefore, the organizational structure that can enhance drought mitigation systems should be a synergy between all the tiers of government and the end users. The role of the local people must be emphasized in structuring the mitigation model to be 21

developed because of the significant role they play in agricultural sector and in sustaining the economy of the country. An integrated and extensive approach that can lead to good mitigation plans against drought include; (i) the development and management of the resources in the drought prone areas of NRN should be based on strategies that takes into consideration the local residents and natural resources, their bio-diversity and their socio-economic needs, (ii) there must also be a strong link between various agencies of the government that cut across water and agricultural sectors. The approach suggested earlier is in contrast to the usually approach that is characterized by (i) the use of theoretical and difficult to implement techniques that cannot not be easily replicated by the local residents, (ii) total dependence on foreign experts and technicians that are not very knowledgeable about the tradition and inherent problems of the local people. Thus, if the suggested approach is properly implemented, the process should lead to (i) improvement in the coordination and cooperation within all tiers of government that is required to mitigate natural hazards, (ii) improvement in the ability of government to react very promptly when disaster is sure to take place, (iii) upgrade in the ability of the government to provide interventions to the core areas/region of need based on a reliable early warning system that will incorporate drought indices, artificial intelligence and geographical information system (GIS). In a nutshell, the developed model should be easily adaptable to many African situations where there is the need to integrate the local residents into drought preparedness plans. The sustainability of organizational structures and resource capacity for rapid response to natural hazards such as drought and land degradation must be seen as a national challenge that should be prioritized in Nigeria and West Africa at large.

Funding:

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Highlights • • •

For stake holders and government at all levels to properly mitigate the effects of drought, adequate knowledge derived from a comprehensive analysis of historic drought episodes will offer huge assistance. sc-PDSI reveals the long-term hydrological drought of River Niger indicated at Baro gauging station about 12 months before the low flow occurred. The role of the local people must be emphasized in structuring the mitigation model to be developed because of the significant role they play in agricultural sector and in sustaining the economy of the country.

Conflict of interest None.