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Characteristics of drought indices andrainfall in Lake Chad BasinChurchill Okonkwoa, Belay Demoza & Kyrian Onyeukwub

a Beltsville Center for Climate System Observation, AtmosphericScience Program, Howard University, Washington, DC, 20059, USAb FPM Geophysical & UXO Services, San Antonio, TX, USAPublished online: 02 Sep 2013.

To cite this article: Churchill Okonkwo, Belay Demoz & Kyrian Onyeukwu (2013) Characteristics ofdrought indices and rainfall in Lake Chad Basin, International Journal of Remote Sensing, 34:22,7945-7961, DOI: 10.1080/01431161.2013.827813

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International Journal of Remote Sensing, 2013Vol. 34, No. 22, 7945–7961, http://dx.doi.org/10.1080/01431161.2013.827813

Characteristics of drought indices and rainfall in Lake Chad Basin

Churchill Okonkwoa*, Belay Demoza , and Kyrian Onyeukwub

aBeltsville Center for Climate System Observation, Atmospheric Science Program, HowardUniversity, Washington, DC 20059, USA; bFPM Geophysical & UXO Services, San Antonio, TX,

USA

(Received 9 October 2012; accepted 16 April 2013)

A basin-scale analysis of the spatial and temporal distribution of drought indicesand rainfall characteristics was performed in Lake Chad Basin (LCB), located at theSahelo–Sudanian transition zone of West Africa. The research aims to improve ourunderstanding of distribution, scenarios, and location-specific probability distributionof rainfall in the basin. Dekadal variability and trends were constructed and analysedusing a geographic information system geoprocessing tool. There is a good correlationbetween the Tropical Rainfall Measuring Mission (TRMM 3B43) monthly rainfall andGlobal Precipitation Climatology Centre (GPCC) gauge with a correlation coefficientof 0.98. Climate Prediction Centre (CPC) Rainfall Estimate (RFE), Earth ObservingSystem (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) (eMODIS)normalized difference vegetation index images, global net primary production (NPP)anomaly, and standard precipitation index (SPI) were characterized. Results indicate anincrease in NPP and SPI values from 2002 to 2011, which supports the theory of recentgreening of the Sahel. Autocorrelation analysis identified a very high drought index atthe northernmost part of LCB (proximal to the Sahara Desert) with the northern part ofLCB characterized as low–low, suggesting more likelihood of low rainfall, and south-east and southwest portions as high–low, suggesting a decrease in likelihood of highprecipitation northward. This provides vital information to farmers and relevant author-ities for making educated decisions in poor rainy seasons. The statistical coefficient ofvariance and rainfall average also provide crucial information on region-specific rainfallneeds for crop production.

1. Introduction

Semi-arid regions that cover less than 41% of the Earth’s surface face enormous pressureand degradation (Safriel and Adeel 2005). According to Gleick (2000), the availability offresh water has a direct bearing on a variety of issues ranging from politics to human healthand well-being. The Sahel region (12◦ N–20◦ N; 8◦ W–52◦ E), which stretches acrossseveral African countries from Senegal eastward to Ethiopia in the west (Figure 1(a)), isone of the highly studied semi-arid regions of the world (Caminade and Terray 2010; Heldet al. 2005; Giannini, Saravanan, and Chang 2003; Charney 1975). Lake Chad Basin (LCB)(6◦ N–24◦ N; 7◦ E–24◦ E) is located at the southwestern intersection of the Sahel andforest Savanna (Figure 1(b)). In a semi-arid region like the Sahel, a substantial reduction inannual rainfall could lead to some serious repercussions on the local economy, farming, and

*Corresponding author. Email: churchill.okonkwo@bison.howard.edu

© 2013 Taylor & Francis

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River regime

Intermitient

Countries inSahelregion

Open grasslandwith sparse tress

Studystations

0134567

−Non-land Area−0 to 100−100 to 200−200 to 400−400 to 600−600 to 1000−1000 to 1500

Precipitation

(mm)

(a) (b)

(d)(c)

Cropped withopen woody vegetation

Sandy desert

Irrigated croplands

Perennial

Figure 1. (a) Sahel region showing perennial (dark blue) and intermittent (sky blue) rivers; purplelines demarcate the sub-basins. (b) Study stations in LCB. (c) Land use/cover in LCB. (d) Spatialdistribution of rainfall climatology for LCB (purple = Lake Chad).

livestock production. The spatial and temporal variability of rainfall is thus crucial to waterresources management in the Sahel region of sub-Saharan Africa, of which LCB is part.

Figure 1(c) shows a map of land use/land cover for LCB. There is a northward regres-sion from open grassland with sparse tress in the south to sandy desert in the north.The rainfall climatology of LCB is also shown in Figure 1(d). The mean annual rain-fall varies from 1500 mm in the southeastern parts of LCB to less than 100 mm in thenorth. According to the Food and Agricultural Organization (FAO), there are three climaticzones: hyper-arid in the north, through semi-arid in the central parts, to subtropical in thesouth (FOA 2009). Most of the studies in this region have focused on the Sahel, but LCBis located at the transition Sahelo–Sudanian zone. As such, it responds to both dry cli-matic forcing (harmattan wind) (Knippertz and Fink 2008) from the north and wet forcingfrom the south (West African monsoon). Studies in the Sahel region include simulationsof hydrological impact of land use (e.g. Charney 1975; Taylor et al. 2002; Li et al. 2007),all of which attributed at least in part the degradation of land surface to reduced rainfall.There have also been several studies that examined the teleconnection between rainfallvariability in the Sahel and variation in sea-surface temperature (SST) over the tropicalPacific (e.g. Caminade and Terray 2010). Several studies have found that regional weatherpatterns forced by the SST of the North Atlantic have more influence (than hydrologicalimpact of land use) on the local climate in the Sahel region (e.g. Hoerling et al. 2006).An understanding of observed variability in precipitation therefore requires both SST andland–atmosphere interaction (Wang et al. 2004).

Studies on the use of the normalized difference vegetation index (NDVI) to mon-itor how vegetation responds to changes in climatic condition in Africa include thoseof Nicholson and Farrar (1994) and Anyamba and Eastman (1996). Some studies haveattributed the recent greening in the Sahel region in part to increasing rainfall in the pastdecade, global warming (Hagos and Cook 2008), and sea-surface temperature variability

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(Hoerling et al. 2006; Cook 2008). Nicholson (2005) used a Tropical Rainfall MeasuringMission (TRMM) data set in a study of the link between the recent greening of the Saheland increasing rainfall. According to Nicholson (2005), the Sahara region has remainedrelatively dry unlike the western Sahel, which has shown marked recovery.

A recent study by Li, Coe, and Ramankutty (2005) in LCB on hydrologic variabilityusing land-surface models found run-off to be extremely sensitive to rainfall fluctuation.Li, Coe, and Ramankutty (2005) also found that variability is higher in the dry periodthan in the wet period for rainfall, evapotranspiration, run-off, and river discharge. Thesefluctuations have direct impact on Lake Chad level and size. Because Lake Chad is a closedlake, decreased precipitation in the basin ultimately results in a net decrease in inflow fromthe tributaries (Figure 1(c)) and subsequent reduction in lake level and size. As a result ofthe decrease, the local population, which survives on cultivation from the lake, thus riskslosing wealth from this natural resource.

An understanding of the past and projected rainfall variability is the key to any planningat the local level. The mean June, July, and August (JJA) total precipitation for station loca-tions across Africa using daily data empirically downscaled from six global climate models(GCMs) projects a strong drying in the core of the Sahel (Hewitson and Crane 2006). Also,IPCC AR4 suggests that arid and semi-arid land will increase by 5–8% in Africa (IPCC2007; Boko et al. 2007). Drier climate and less rainfall will mean more draining from LakeChad through irrigation in a desperate search for water. What this entails is that decision-makers in the region need a better understanding of the rainfall total, distribution, scenarios,and location-specific probability distribution.

The first step then is to characterize fairly accurately the distribution of rainfall in theregion on a seasonal and intra-seasonal level. The aim of this article is thus to provide abasin-scale analysis of the spatial and temporal distribution of hydro-meteorological vari-ables in LCB and delineate homogenous rainfall sub-regions. This understanding will act asa tool for policy-makers in designing a response to future climate change scenarios withinthe region.

2. Data sets and methodology

Good spatial estimates of rainfall are provided by remote-sensing methods in LCBdue to the limited network of rain gauge stations. The Earth Resources Observationand Science (EROS) Moderate Resolution Imaging Spectrometer (MODIS) (eMODIS)produced by the US Geological Survey’s (USGS’s) EROS Center (Swets et al. 1999)and the Rainfall Estimate (RFE) developed by the National Oceanic and AtmosphericAdministration’s (NOAA’s) Climate Prediction Center (Xie and Arkin 1997) were used.The RFE 2.0 used in this analysis gives an improved estimation of precipitation by com-bining World Meteorological Organization (WMO) station rainfall data and interpolationmethods that combine Meteosat and Global Telecommunication System (GTS) data. TheCRU CL 1.0 data set from the Climate Research Unit (CRU) of the University of EastAnglia, comprising monthly grids of observed mean climate from 1961 to 1990 (New,Hulme, and Jones 1999) and covering the global land surface at 0.5◦ resolution, was alsoused for precipitation spatial distribution analysis.

The rainfall characteristic of LCB was also analysed using TRMM and other satel-lites’ RFE (3B43 version7) and TRMM Precipitation Radar (PR) (3A25). The algorithmof TRMM 3B43 is a combination of three fields from the TRMM instrument pack-age: the pentad-average adjusted merged-infrared (IR) estimate, the monthly average

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TRMM Microwave Imager (TMI) estimate, and the monthly average Special SensorMicrowave/Imager (SSM/I) (Kummerow et al. 2000).

Because a geographical information system (GIS) is a good tool for analysing spatiallocation, interaction, structure, and processes, it is used to analyse hydro-meteorologicaldata that are spatially distributed. The variability and trends in annual rainfall data wereanalysed using clip and dissolve analysis to aggregate areas based on specified attributes ofannual rainfall, over the period of 30 years from 1961 to 1990.

The NDVI image at 250 m spatial resolution is a 10 day maximum composite valuethat allows for correlation with dekadal (10 day) RFE. The physics of the differences inwavelength of visible light (0.4–0.7 µm) and near-infrared light (0.7–1.0 µm) reflected byplants and measured from satellites is used to calculate NDVI. The principle is based onthe absorption of visible light by chlorophyll, a pigment used in photosynthesis by plants.While the chlorophyll absorbs the visible light, the structure of the cell strongly reflects thenear-infrared light. The formula for NDVI that measures vegetation greenness and vigour(Tarpley, Schneider, and Money 1984) is as follows:

NDVI = NIR − VIS

NIR + VIS, (1)

where NIR and VIS are the reflectances in near-infrared and visible, respectively(Schneider, McGinnis, and Gatlin 1981).

At the pixel level, high values of NDVI indicate tropical forests while zero values indi-cate barren lands. Between these two extremes are the shrubs and grasslands. This measureof vegetation cover is important particularly in semi-arid regions to monitor and map desertencroachment.

In this research also, the standard precipitation index (SPI) was used as a referenceindex for the identification of drought events. The Climate Anomaly Monitoring Systems(CAMS) technique was used to generate yearly rain gauge totals (Ropelewski, Janowiak,and Halpert 1984) and estimates from outgoing longwave radiation (OLR) anomalies gen-erated by the OLR precipitation index (OPI) (Xie and Arkin 1997). Also, an estimate ofterrestrial net primary production (NPP; in g cm−2 year−1) from satellite data was obtainedfrom global terrestrial NPP data from the University Earth and Ecosystems ScienceUniversity of Montana. The technique used the global Moderate Resolution ImagingSpectroradiometer (MODIS) NPP algorithm to examine spatially explicit NPP changes.For details, see Zhao and Running (2010).

Finally, in order to identify the spatial association of the climatic rainfall distributionin LCB, Anselin Local Moran’s I spatial cluster (Moran 1950) and outlier analysis wasperformed. The local indicator of spatial autocorrelation (LISA), introduced by Anselin(1995), was applied to account for spatial ‘structural instability’ of global measures. LocalMoran’s I , z-score, and p-value evaluating the significance of each index were calculated.The z-scores and p-values determined the statistical significance of the computed indexvalues. Generally, a Moran’s Index (I)-value close to positive I indicated that a plot waspart of a cluster, while and a negative I-value indicate that it was an outlier (Ord and Getis1995).

The local Moran statistic for areal unit i is Ii = zi∑

jwijzj, where zi is the original vari-

able xi in ‘deviation from’ zi = xi−x̄SDx

, where x̄ is the mean, SDx is the standard deviation,and wij is the spatial weight xi − x̄. The summation

∑

jis across each row i of the spatial

weights matrix.

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The output from this cluster analysis distinguishes between a statistically significant(0.05 level) cluster of high values (high–high (HH)), cluster of low values (low–low (LL)),outlier in which a high value is surrounded primarily by low values (high–low (HL)),and outlier in which a low value is surrounded primarily by high values (low–high (LH))(Mitchell 1999).

3. Results and discussion

3.1. Precipitation, temperature, and NDVI

Figure 2 shows the mean monthly variation in rainfall for the 30 year period 1960–1990.A short rainy season (June, July, August, and September) and longer dry spell is a pro-nounced characteristic of spatial temporal distribution of rainfall in LCB (Coe and Foley2001; Nicholson, Yin, and Ba 2000). Because of the strategic location of LCB in a climatictransition zone, regions in the south of the lake tend to experience more rainfall than thosein the north of the lake. The mean monthly rainfall for eight stations within LCB stationsthat are located in the southern parts of the basin shows distribution that is common intropical rainfall regions. Here rainfall occurs from April to early November. The northernproportion, however, only experienced rainfall for a shorter period of July, August, andSeptember (JAS). What this means is that the level and size of Lake Chad depend on pre-cipitation on the southern parts and how much of the run-off is transferred through the floodplains of the basin to the lake.

The distribution of surface water bodies (not shown) has a high correlation with rainfalldistribution. The southern parts have a few perennial rivers that act as a lifeline to agricul-tural activities and human needs while the northeast and northwest regions are characterizedby intermittent water bodies that have low seasonal flow and dry up quickly at the onset ofdrought (Okonkwo 2011). Studies by Lienou et al. (2005) in northern Cameroon duringmild and prolonged drought years show that the riverbeds of these intermittent streams aredesiccated, primarily as a result of substantial water abstraction for irrigation and nomadicpasturing.

Sep Oct Nov Dec

AugJulJunMay

Jan Feb Mar Apr

Low

High

Figure 2. Spatial and temporal distribution of rainfall in LCB (data from Climate Research Unit;New, Hulme, and Jones 1999).

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May

Sep

Jan Feb

Jun

Oct Nov

July

Mar Apr

Aug

Dec High

Low

Figure 3. Spatial and temporal distribution of temperature in LCB (data from climate ResearchUnit; New, Hulme, and Jones 1999).

There are thus different climatic conditions for the southern, central, and northern partsof LCB (Figures 1, 2, and 3). Figure 3 shows the spatial distribution of temperature in LCB.The distribution of decadal monthly rainfall and temperature suggests that there is a rela-tionship between high temperature and low rainfall. This appears to support Dai, Trenberth,and Qian (2004), who found that increased temperature leading to increased evapotran-spiration is associated with increased drought. Peak rainfall in August corresponds to thenorthward movement of the inter-tropical convergence zone (ITCZ) (Lélé, and Lamb 2010).

Most of the research on NDVI across the Sahel region has focused on NOAA/AdvancedVery High Resolution Radiometer (AVHRR)-NDVI (Herrmann, Anyamba, and Tucker2005; Jarlan et al. 2005). The eMODIS vegetation index used in this study is, however, animprovement on NOAA/AVHRR (Li, Wang, and Li 2000; Wang, Price, and Rich 2001).Figure 4 shows the trend of monthly rainfall and NDVI for six stations within LCB. The2001–2011 time series and JAS seasonal plots of precipitation and NDVI are shown inFigures 4(a) and (b), respectively. The results show a reduction in precipitation in 2004,which is not reflected in the corresponding values of NDVI (Figures 4(b) and (c)). Whatthis suggests is that the inter-annual variability in precipitation did not result in variabilityin NDVI for the short time scale under consideration.

The spatial distribution of NDVI and rainfall is shown in Figures 5 and 6, respectively.The seasonality in the temporal relationship between NDVI and precipitation is evident andthere is clearly a lag in the response of NDVI to precipitation. This is in agreement with thefindings of Malo and Nicholson (1990). The lag time is longer for October, November, andDecember and accounts for the water-retention capacity of soil that sustains plant growthat the onset of the dry season in November.

3.2. Rainfall

3.2.1. Rainfall characteristics and regions

Having analysed the spatial and temporal patterns of precipitation and NDVI, we will nextattempt to delineate the LCB into rainfall regimes. This is achieved by applying probabilitydistribution measures of cluster analysis, coefficient of variations, means, and standarddeviation. Three distinct climatic zones – savannah (8◦ N–12◦ N; 7◦ E–24◦ E), semi-arid

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Figure 4. Rainfall and NDVI trends for (a) inter-annual and (b) seasonal, 2001–2011 (JAS).

Jan 2011 Feb 2011 Mar 2011 Apr 2011

Jun 2011 Jul 2011 Aug 2010

Nov 2010 Dec 2010Oct 2010

May 2011

Sep 2010

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Figure 5. Spatial distribution of smoothed NDVI, August 2010 to July 2011.

(12◦ N–15◦ N; 8◦ E–24◦ E), and arid (15◦ N–24◦ N; 7◦ E–23◦ E) – for LCB were iden-tified following Philip (1999) based on their climatic features. The rainfall characteristicsusing TRMM 3B43 and Global Precipitation Climatology Centre (GPCC) gauge data setsat the spatial scale of 1◦ × 1◦ are based on climatic zone and monthly/annual variability.The time series of accumulated annual rainfall from TRMM 3B43 is shown in Figure 7(a).Maximum precipitation is observed in summer and minimum in winter.

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Sep 2010 Nov 2010 Dec 2010

0−1

(mm)

1−10

150−200

250−(units)

200−250

125−150

100−125

75−100

50−75

25−50

10−25

Aug 2010Jul 2011Jun 2011May 2011

Jan 2011 Feb 2011 Mar 2011

RFE (Rainfall Estimate)

Apr 2011

Oct 2010

Figure 6. Spatial distribution of rainfall, August 2010 to July 2011.

Figure 7. Time series of (a) monthly average TRMM 3B43, (b) spatially averaged monthly rain-fall from gauge, GPCC, and TRMM 3B43, and (c) diurnal variation of rainfall (mm year−1) fromTRMM PR.

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Figure 7(b) shows the mean monthly comparisons between the GPCC rain gauge pre-cipitation and that of TRMM 3B43. From the comparison, there is maximum difference inthe month of August (the wettest month). TRMM underestimates rainfall by around 2 mmmonth−1 between February and June and overestimates it by up to 6 mm month−1 betweenJuly and October. Figure 7(c) shows the diurnal variation of rainfall from TRMM PR. Mostof the rainfall contribution from PR occurs from midday to the early hours of the morningwith two peaks, one around 17.00 and the second around 03.00 local time. This suggeststhat the highest intensity of rainfall occurs after midday with a sharp decrease around dawn,and the minimum at 10.00 local time.

There is a good correlation between TRMM 3B43 monthly rainfall and GPCC gaugewith correlation coefficients of 0.98, 0.97, 0.77, and 0.60 for LCB, southern LCB, centralLCB, and northern LCB, respectively, all of which are statistically significant (Figure 8).This is in agreement with Nicholson et al. (2003). The arid northern parts show the lowestcorrelation. The overall result suggests that savannah regions have the best correlation withSahara Desert regions showing minimal correlation. Also, there is an increase in scatter forobserved values greater than 300 mm month−1 (entire basin and savannah zone), 35 mmmonth−1 (semi-arid zone), and 8 mm month−1 (Sahara zone). This can be attributed to theabsence of gauge stations in the desert region of LCB.

Figure 9 shows the mean monthly precipitation for six stations in LCB while Table 1shows the result of the location specific probability distribution for the WMO GTS stationsthat fall within LCB shown in Figure 1(c). Table 2 shows the summary of monthly sta-tistical parameters for the TRMM 3B43 product. The zonal characteristics of the rainfall

Figure 8. Scattergrams of TRMM merged product versus gauge analysis climatology (mmmonth−1). (a) Entire LCB, (b) savannah zone, (c) semi-arid zone, and (d) Sahara zone.

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Figure 9. Mean monthly precipitation of all stations in LCB, 2001–2011.

Table 1. Average annual rainfall, number of dekada with rainfall greater than 100 mm, and coef-ficient of variation of seasonal rainfall based on 2001–2011 data (data source, National Center forEnvironmental Prediction (NCEP)-RFE 2.0).

Stationnumber Station name

Average rainfall(mm) (JAS)

Number of dekada(>100)

Coefficient ofvariance (%)

1 Baguirmi 61.2 4 28.292 Cameroun 72.5 13 24.713 Jos 57.2 5 26.954 Kano 58.2 8 27.995 Zinder 27.1 0 17.566 Barl 26.4 1 21.49

Table 2. Statistical parameters of spatial correlation coefficient of TRMM (3B43) versus GPCC(gauge).

Correlationcoefficient (r) Count RMS Bias Slope

Standarddeviation

LCB 0.98 58,704 14.50 0.001 0.95 0.147South LCB(savannah) 0.97 32,592 18.68 0.002 0.84 0.19Central LCB(semi-arid) 0.77 13,056 7.99 0.005 0.83 0.50North LCB(Sahara desert) 0.60 13,056 2.78 −0.002 0.55 0.66

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show significant increase in standard deviation as one moves from the savannah zone to theSahara Desert.

The measures in Table 1 provide some useful numbers on rainfall extremes, frequency,and periods in the rainy season with rainfall greater or less than the minimum number ofmillimetres needed for crop production. A combination of standard deviation and coeffi-cient of variance gives an indication of rainfall variability from the long-term mean andwas used as the basis for the rainfall regime classification.

Located southeast of LCB is region 1, characterized by an annual precipitation rangeof 1000 to 1500 mm, and the mountain range of the Cameroun (Figure 9). Here, the wetseason starts early in April and peaks in August/September, with annual JAS rainfall of72 mm. From the dekadal RFE, this region has the lowest dekeda (15) with rainfall lessthan 50 mm. The coefficient of variation, which is the standard deviation divided by averagerainfall, implies that rainfall will vary ±24% from its long-term average. The stations inKano and Jos in the southwest and Baguirimi in the southeast are in region 2. The Josstation is at the trans-boundary of LCB and Benue Basin and represents the peak of thesouthern highland of LCB. Rainfall in this region will vary from ±27–28% from its long-term average while dekeda with rainfall greater than 100 mm is between 4 and 8 (Table 1).There are two maxima for Jos in the southwest (see Figures 1 and 6), one in June and theother in September.

Zinder and Barl in the central part of the basin with the characteristic May to Octoberwet period are in region 3. Region 3 shows the typical wet period characteristic of the Sahelregion. The number of dekada with rainfall less than 50 mm and greater than 100 mm is90 and 1, respectively. The importance of these statistical analyses to a local farmer is inproviding crucial information on rainfall needs of his or her crop. These include averageand seasonal rainfall and frequency of extremes. In relation to dry spells, farmers in region3 with the lowest period of rainfall greater than 100 mm are at a higher risk of sufferingloses, especially with drops that are sensitive to drought. A farmer in region 3 should there-fore recognize the relatively short wet period (JAS) and high number of dekeda with rainfallless than 50 mm and plan accordingly.

3.2.2. Spatial autocorrelation

Getis and Ord spot analysis was used to identify precipitation (drought) hot spots for LCB.According to Jacquez (2008), cluster analysis such as the Getis and Ord hot spot can beused to quantify geographic variation patterns like rainfall. Figure 10 shows the rainfallhot-spot results for LCB. As expected, the northernmost part at the proximity of the SaharaDesert has the highest drought index. Getis and Ord cluster hot-spot analysis appears inad-equate for use as a drought index technique since the entire basin was delineated into eitherhot or cold regimes. To address this inadequacy, LISA was applied to identify the spatialassociation of the climatic rainfall distribution in LCB. Anselin (1995) introduced LISAto account for spatial ‘structural instability’ of global measures. Because this study is pri-marily a basin-scale precipitation analysis, it fits into Anselin’s (1995) LISA criteria forlocal statistics and indicates the extent of spatial clustering around observations. This hasbeen applied in multiple fields to analyse spatial patterns of variables (e.g. Overmars, DeKoning, and Veldkamp 2003).

Figure 10(b) shows the basin-scale extent of the spatial clustering around the observedprecipitation relations. When similar values appear together in space, one has a positiveautocorrelation, which means that events in one region will make it more likely for thesame event to occur in a neighbouring region (Ponce, Pandey, and Ercan 2000). In this

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Figure 10. (a) Getis and Ord rainfall hot-spot results LCB. (b) Local indicator of spatial autocorre-lation.

precipitation regime analysis, the neighbourhood in the northern parts of LCB can be char-acterized as LL, indicating clustering of low values of precipitation. This is the region withleast annual precipitation rate (<100 mm) and means more likelihood of low rainfall. Thecentral part of LCB and greater portions of the southwest are HH, indicating a clusteringof high precipitation values (positive spatial autocorrelation). A cluster of dissimilar val-ues implies negative autocorrelation, which means that an event in one region will likelydecrease the possibility of that same event in a neighbouring region (Anselin 1995). An HLsituation indicates that low values are adjacent to high values of precipitation (negativeassociation) at the southeast and southwest and decrease the likelihood of high precipitationnorthwards.

From the previous section, we delineated LCB into rainfall regions; the Getis and Ordspot analysis of precipitation data went a step further and identified a very high droughtindex at the northernmost part of LCB (proximal to Sahara Desert). Region 1 falls withinthe clustering zone of HL while regions 2 and 3 are in the HH zone. There is sufficientrainfall for farmers in southeastern LCB with the onset of the rainy season in April. Fora farmer in regions 2 or 3, however, there is lower amount and duration of rainfall thatcuts short the planting season and increases vulnerability to drought climate variability.In addition to knowing which neighbouring region would be affected by low rainfall inanother region, LISA also indicates the extent of spatial clustering around observations,enabling farmers and relevant authorities make educated decisions in poor rainy seasons.

3.3. NPP and SPI

The spatial distribution of NPP and SPI is shown in Figures 11(a) and (b), respectively.Drought, being cyclic and regional in nature, is controlled primarily by mean annualrainfall, which itself depends on atmospheric circulation dynamics, SST, latitude, andmesoscale ocean currents, among others (Ord and Getis 1995). CAMS–OPI–SPI analysisindicates an increase in SPI value from 0 (near normal) in 2009 to around 2.5 (extremely

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Figure 11. Spatial distribution of (a) NPP and (b) NDVI.

Table 3. SPI Classification (Hayes et al. 1999).

SPI value Classification

2.0 and above Extremely wet1.5 to 1.99 Very wet1 to 1.49 Moderately wet−0.99 to 0.99 Near normal−1.00 to −1.49 Moderately dry−1.50 to −1.99 Severely dry−2.00 and less Extremely dry

wet) in 2010 south of LCB. Because SPI is normalized, drier and wetter climate can berepresented in the same way. While zero SPI value is classified as mild drought, −2 SPIidentifies extreme drought (Table 3). Extreme drought with an SPI value of −2.00 or lesshappens twice per century while that of 0 to −1, which is close to normal, may occur up to34 times per century. See Mckee, Doesken, and Kleist (1993), Mckee, Doesken, and Kleist(1995), and Hayes et al. (1999) for details of the SPI algorithm.

Zhao and Running (2010) reported an extremely small reduction (0.55 petagramsof carbon (Pg C)) in global terrestrial NPP of 535.21 Pg C over a period of 10 years(2000–2009). From Figure 9(a), however, the NPP has been increasing gradually in LCBsince 2007: −30 g cm−2 year−1 in the southwest to about 10 g cm−2 year−1 in 2010.

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The increase was more remarkable in the southeast: 0–10 g cm−2 year−1 in 2007 to near100 g cm−2 year−1 in 2010, corresponding to a similar increment in SPI value.

Reduction in adverse climate control on plant growth between 1982 and 1989 led to anincrease in terrestrial NPP (Nemani et al. 2003), which denotes NPP as a drought indicator.The moderately dry drought in LCB (2007) reduced annual NPP of forested ecosystems,and thus the pronounced negative values.

Since the last mild drought of 1992 in LCB, there appears to be a continued recovery asshown from the temporal distribution of NPP in agreement with the findings of Sullivan andRohde (2002). As expected, the spatial distribution of NPP (Figure 11(a)) is highest in thesoutheastern part of LCB, corresponding to 30 year average rainfall distribution (Figure 1).While Lamprey (1988) found that the southern boundary of Sahara desert vegetation hadshifted southward by 90–100 km during the 17 year period from 1958 to 1975, a recentfield study in the same area found no evidence of such expansion (Hagos and Cook 2008).The spatial distribution of NPP from the present study appears to be in agreement with thereported greening of the Sahel (Hoerling et al. 2006; Cook 2008).

4. Conclusions and recommendations

In conclusion, we emphasize that because LCB is located at the transition Sahelo–Sudanianzone between the Sahara Desert and tropical savannah of West Africa, a basin-scale anal-ysis of the spatial and temporal distribution of hydro-meteorological variables is neededin order to better understand the rainfall total, distribution, scenarios, and location-specificprobability distribution.

We showed that SPI values have been increasing from 2002 to 2011 and there is asignificant relationship between rainfall, SPI, and NPP in LCB. Getis and Ord spot analysisidentified the northernmost part near the Sahara Desert as having the highest drought index.LISA indicates positive autocorrelation in the northern parts of LCB characterized as LL,meaning a higher likelihood of low rainfall. Negative autocorrelation (HL) in the southeastand southwest indicates that low values are adjacent to high values of precipitation anddecreases the likelihood of high precipitation northwards.

From the understanding of the locational attributes of temporal and spatial droughthydro-meteorological variables, and information on their location specific probability dis-tribution, local famers should benefit by knowing which region will be affected by lowrainfall in a neighbouring region. This LISA method indicates that the extent of spatial clus-tering around observations will enable relevant authorities and agencies to make educateddecisions on relocation of farmers in poor rainy seasons.

One of the limitations of this study is the absence of a prediction model for theLCB. This is the subject of ongoing research and will represent the first time that therelative importance of climate indices and complex mechanisms effecting the changingSahel precipitation will have been examined in the same modelling framework usingClimate Weather Research and Forecasting (CWRF). In CWRF, all the functionalities ofWeather Research and Forecasting (WRF) used in Numerical Weather Prediction (NWP)are included, with additional emphasis on enhancing its capabilities for climate applications(Liang et al. 2005). Some of the crucial characteristics of CWRF that will address the unre-solved issue of narrowing uncertainty in precipitation projection in the Sahel include itsnew physics modules for terrestrial hydrology, land surface, and planetary boundary layer(Liang et al. 2005). Finally, CWRF application in narrowing uncertainty in climate projec-tion (Liang and Wang 2011) will be utilized to help policy-makers in the Sahel region makeexpert value judgement about potential risks from drought.

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AcknowledgementWe would like to thank Dr Ricardo Sakai for his comments and suggestions, which helped us toconsiderably improve the manuscript.

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