major climate indicators of ongoing drought in sudan

Journal of Hydrology 409 (2011) 612–625

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Journal of Hydrology

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Major climate indicators of ongoing drought in Sudan

Nadir Ahmed Elagib a,⇑, Muna M. Elhag b

a Department of Civil Engineering and Architecture, University of Bahrain, P.O. Box 32038, Isa Town, Bahrainb Faculty of Agricultural Sciences, University of Gezira, P.O. Box 20, Wad Medani, Sudan

a r t i c l e i n f o

Article history:Received 15 May 2011Received in revised form 17 August 2011Accepted 23 August 2011Available online 30 August 2011

This manuscript was handled by AndrasBardossy, Editor-in-Chief, with theassistance of Martin Beniston, AssociateEditor

Keywords:Rainfall anomalyTemperature anomalyDroughtENSOSahelSudan

0022-1694/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.jhydrol.2011.08.047

⇑ Corresponding author. Tel.: +973 1787 6096; fax:E-mail addresses: [email protected] (N.A. Ela

(M.M. Elhag).

s u m m a r y

There has been a rigorous debate during the 2000s about the recovery of the African Sahel from thelong-lasting drought. To examine the situation in Sudan, this paper develops the standardized anomalyindices (SAIs) of mean annual temperature and annual rainfall, and accordingly, establishes the Pedjdrought index (PDI) since the 1940s to 2008 for 14 stations spread over the country. There has been adrastic increase in temperatures (maximum, minimum, and mean) over the whole country in line witha significant decline of rainfall over the northern half of the country. Evidence of a correlation betweentemperature and rainfall anomalies has been reported, emphasizing the concurrence of dry and hotperiods. These results suggest less effective rainfall. Contrary to the postulation of recovery from thelong-lasting drought in the African Sahel, the results for Sudan indicate intensifying drought evidencedby significant rising trends in PDI. The 2000s depict a widespread and prolonged drought (mild to severe),except for the extreme southern and south-western parts of the country which displayed excess ofhumidity. The frequency of occurrence of drought classes during 1975–2008 ranged from 44.1% to70.6% compared to a frequency extending from 8.8% to 40.0% for 1941–1974. The PDI succeeded to rep-resent satisfactorily drought episodes captured by other drought indices recommended worldwide. ElNiño-Southern Oscillation (ENSO) index has a significant footprint on the PDI, strongly affecting the coun-try south of latitude 15�N.

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1. Introduction

Anomalous and extreme weather and climate phenomena, suchas droughts, floods, and heat and cold waves, can have serious anddamaging effects on human society and infrastructures, ecosys-tems and wildlife (Kunkel et al., 1999; Meehl et al., 2000). A gen-eral definition of drought is a decrease of water availability andcharacterizes two crucial aspects of water deficiency, namely dura-tion of the dry period and region or location (Mawdsley et al.,1994). Drought events involve detrimental consequences for soilerosion, crop production, water supplies and hydroelectric powergeneration (Landsberg, 1982). Drought phenomena are producedby ‘‘admixtures of climatic, hydrological, environmental, socio-economic, and cultural forces’’ (Kallis, 2000). The above definitionof drought (Mawdsley et al., 1994) uses two types of drought indi-cators: (1) environmental indicators, which measure the direct ef-fect on the hydrological cycle, including, among others, rainfall,temperature, evapotranspiration, river flow, etc., and (2) water re-sources indicators, which measure the severity in terms of the im-pact on the use of water, e.g. water supply for domestic or

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+973 1768 0843.gib), [email protected]

agricultural use, fisheries or recreation. Although ‘‘drought indicescan only reflect drought conditions based on hydro-meteorologicalvariables [and are] unable to quantify the economic losses’’ (Mish-ra and Singh, 2010), indices for monitoring climate variability anddrought impacts are useful tools for designing drought responseplans, assessing the need for domestic and international aid deci-sion to affected populations and declaring drought emergency(Quiring, 2009; Vasiliades et al., 2011).

Atmospheric drought is identified by Saarinen (1966) by strongdust storms, low precipitation, high temperature and low relativehumidity. An important triggering factor, among others, of desert-ification as identified by the UN Conference on Environment andDesertification (UNCED) is climate variations (Hare, 1993). The in-creases in surface air temperature and radiative heating can lead tohigher atmospheric demand for moisture, i.e. increases ocean andland evaporation, but these processes can lead to drier soils evenif the precipitation amount increases resulting from the enhancingevaporation (Kallis, 2000; Dai, 2011). Hence, mainly due to a wide-spread drying since the 1970s, global aridity and drought areashave increased substantially, arguably contributing to the recentdrying over the land of for example Africa (Dai, 2011).

The frequent occurrence of drought events have stimulated sub-sequent studies related to the topic. During the last decade, therehas been an intensive debate about the recovery of the African

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N.A. Elagib, M.M. Elhag / Journal of Hydrology 409 (2011) 612–625 613

Sahel from the long-lasting drought (L’Hôte et al., 2002, 2003; Ozeret al., 2003; Dai et al., 2004; Nicholson, 2005; Lebel and Ali, 2009;Ali and lebel, 2009). This debate forms the impetus for the presentinvestigation, which aims at updating and reanalyzing the timeseries of temperature and rainfall for Sudan, using the standardizedanomaly indices. Furthermore, a drought index dependent on thecombination of these anomaly indices has been used to establishthe drought conditions.

2. Physiography of the study area

The Republic of Sudan and the Republic of South Sudan com-bined cover an area of 2,505,813 km2, extending from latitude ofabout 4�N to 22�N and from longitude of about 22�E to 38�E.Broadly speaking, this area is a huge plain interrupted by the Nubaand Marra mountains in the west and by the Red Sea hills in thenortheast. This vast plain is also crossed by the River Nile, whichruns from south to north.

The climate varies from continental in the northern parts,through savannah in the center, to equatorial in the southern mostparts. It is characterized by three seasons: hot, wet and dry (Elagiband Mansell, 2000a). According to UNESCO (1977) classification,generally, the summer is very warm, with mean temperaturegreater than 30 �C and the winter is warm, with mean temperaturebetween 20 and 30 �C. In the elevated areas of the west, the wintermay be classified as mild, with mean temperature of slightly lessthan 20 �C. Excluding South Sudan, there is a wide range of differ-ence in temperature between the two regimes. During the hotsummer, the maximum temperature well exceeds 40 �C. Theottest month of the year is June in the north, April to June in thecenter, February to April in the south and July/August along thecoast. On the other hand, December and January are the coldestmonths in most parts of Sudan, whereas the coldest month isJuly/August in South Sudan.

Rain falls during the summer months, where the temperature istempered, except along the humid Red Sea coast, which experi-ences winter rainfall with quite erratic distribution and a peakamount in November. The rainiest month in the rest of the regionsis mostly August, but in certain areas it is July. A wide variation inthe amount of rainfall and in the length of rainy season can be no-ticed from one region to another. On average, the annual rainfallvaries from almost nil in the extreme north to around 1200 mmin the extreme south-west. The wet season spans for about7 months in South Sudan (April–October) and narrows northwardto concentrate in almost one month (August) in northern Sudan.

The climate of Sudan has been changing in many aspects,including reduction of rainfall (Walsh et al., 1988; Eldredge et al.,1988; Hulme, 1990), warming of air temperatures (Elagib andMansell, 2000a; Elagib, 2010a, 2011), solar dimming, increasingreference evapotranspiration and intensifying aridity (Elagib andMansell, 2000b). Moreover, Elagib and Mansell (2000a) found thatthe trends of warmth and dryness have a strong association.

As many other Sahelian African countries, Sudan is a drought-prone area. Findings indicate that drought has become more recur-rent in recent decades, of which those of the early to mid-1970s,mid-1980s, early 1990s and early 2000s can be noted as commondrought years and were among the driest 10 years in the centralregion of Sudan (Elagib, 2009). These droughts emphasized the vul-nerability of the country to desertification and were socially andeconomically damaging (Hulme, 1986; Abu Sin, 1986; Walshet al., 1988; Webb et al., 1991; Olsson, 1993; Larsson, 1996; Ayoub,1999). Due to the dependence on water resources and soil mois-ture reserves during various stages of crop growth, agriculture isoften the first sector to be affected by the onset of drought (Nara-simhan and Srinivasan, 2005).

3. Data and methods

3.1. Data

Fourteen stations across Sudan (Fig. 1) have been selected togive as extensive coverage as possible of the existing environ-ments. Monthly rainfall and mean maximum (daytime Tx) andminimum (nighttime Tn) temperature records extending from the1940s to 2008 were obtained from Sudan Meteorological Author-ity. The mean temperature (Tm) was then calculated as the averageof the maximum and minimum values. Instrumental meteorologi-cal records are sparse before the 1940s, thus the data period con-sidered in the present study ‘‘may provide insights on whetherdrought will become more frequent and widespread under globalwarming in the coming decades’’ (Dai, 2011). Similar to manyAfrican countries (Thomson et al., 2011), the justification to limitthe number of stations included in this study to be 14 only is therecent drop in the number of stations in Sudan and the costlymeteorological data for studies (Elagib, 2010c). However, these sta-tions arguably represent the climates of the large and contrastingarea of Sudan. The northwestern part of Sudan is a great area withextreme desert conditions that are not suitable for living; thus, it isan area where there is not any single meteorological station.

3.2. Choice of indices for monitoring climate variability

Owing to the abundance of temperature and rainfall measure-ments both temporally and spatially, these data are commonlycombined and used to characterize the climatic state of a territory(e.g. Kothyari and Singh, 1996; Elagib and Abdu, 1997; Lough,1997; Zhai et al., 1999; Plume et al., 1999; Boyles and Raman,2003; Conway et al., 2004; Freiwan and Kadioglu, 2008; Aguilaret al., 2009; Choi et al., 2009; Rehman, 2010). In addition, the cor-relation between temperature and precipitation has been com-puted by several authors to be usually negative (Madden andWilliams, 1978; Zhao and Khalil, 1993; Nicholls, 2004; Karolyand Braganza, 2005; Huang et al., 2009), i.e. wet periods are usuallycool periods and vice versa.Year-to-year variability in the annualrainfall (RF) and mean annual temperature series over time was as-sessed in the present study by developing the standardized anom-aly indices (SAIs) for assessing climatic departures from the meanðxÞ, expressed as number of standard deviations (s) as follows:

SAI ¼ x� xs

ð1Þ

where x is a particular year record. In the present study, the datawere standardized with reference to the period 1971–2000. Usingstandardized anomalies from a long-term mean and calculating aspatial average based on these anomalies minimize the effect ofchanges in the number or location of stations (Nicholson, 1985,1986, 1993). Such indices are thus very useful in the regional anal-ysis (Suckling, 1987; Jones and Hulme, 1996), i.e. by developing aregional series of SAIs by averaging the SAI series for all the stationswith different means and variances, and in comparison analysis ofclimatic incidences between the stations. They have been appliedor evaluated by, among many others, Katz and Glantz (1986), Lough(1997), Agnew and Chappell (1999) and Wu et al. (2001).

3.3. Choice of indices for monitoring drought

There are many indices in the literature for assessing the differ-ent kinds of drought (meteorological, hydrological, agricultural, so-cio-economic and groundwater droughts), and the performance ofthem is region specific (Mishra and Singh, 2010). For East Africa’sdrought conditions, Ntale and Gan (2003) identified eight assess-ment criteria to determine the most appropriate index, among

Fig. 1. Stations considered in the study.

Table 1Ranges of Pedj drought index corresponding to different levels of drought andhumidity (Potop and Soukup, 2009).

PDI range Drought/humidity category Abbreviation

PDI P 3 Extreme drought ExD2 6 PDI < 3 Severe drought SeD1 6 PDI < 2 Moderate drought MoD0 < PDI < 1 Mild drought MiDPDI = 0 Normal Nor–1 6 PDI < 0 Excess of humidity ExH–2 6 PDI < –1 Mean humidity MeH–3 6 PDI < –2 Strong humidity StHPDI < –3 Very strong humidity VStH

614 N.A. Elagib, M.M. Elhag / Journal of Hydrology 409 (2011) 612–625

three indices, for detecting drought events. Among the three indi-ces, the Standardized Precipitation Index (SPI) was found to be‘‘more appropriate for monitoring East African droughts becauseit is more easily adapted to the local climate, has modest datarequirements, can be computed at almost any time scale, providesrelatively consistent power spectra spatially, has no theoreticalupper or lower bounds, and is easy to interpret’’. Literature pro-vides good reviews and classifications of the available droughtindices (e.g. Heim, 2002; Keyantash and Dracup, 2002; Kallis,2000; Quiring, 2009; Mishra and Singh, 2010; Vasiliades et al.,2011). The weighting system identified to judge the overall utilityof the drought indices and rank them in terms of usefulness for theassessment of drought severity consists of six criteria (Keyantashand Dracup, 2002) are robustness, tractability, transparency,sophistication, extendability, and dimensionality. The weights as-signed to the drought index evaluation criteria are respectively28%, 21%, 17%, 17%, 10% and 7%. The relative importance assignedby Quiring (2009) are as follows: robustness (30%), tractability(25%), transparency (15%), sophistication (10%), extendability(10%) and dimensionality (10%). All the abovementioned reviewshave exhibited the popularity of the SPI for monitoring meteoro-logical drought severity over other indices since: (1) it is basedon only once input variable (precipitation), (2) can readily be com-pared across time and space and (3) is easy to calculate, under-stand and interpret. However, this index is associated with somelimitations:

(1) Its interpretation may be problematic in arid regions whenprecipitation records contain a number of significant zerovalues.

(2) It is computationally complex and requires specialized code.(3) Long record length is required (80+ years) for accurate results.(4) It is influenced by normalization procedure.(5) It does not consider hydro-environmental factors and sea-

sonal differences in evapotranspiration.

Among drought indices, the Reconnaissance Drought Index(RDI) emerged as one of the most recent developments for theassessment of drought severity through drought indices (Vangelis


et al., 2011; Khalili et al., 2011). It has been tested and comparedcomprehensively with the SPI using data from different climatic

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Fig. 2. Time series of standardized mean an

conditions and regions over the world. While both indices displayan overall similar behavior, the results show that:

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nual temperature and annual rainfall.


(1) it provides a more sound representation of drought conditions,(2) utilizing evapotranspiration, it can be very sensitive to cli-

mate variability and change, thus receiving a more criticalvalue of drought severity level,

(3) it offers a rational comparison of drought conditionsbetween areas with different climatic characteristics.

Based on the precipitation-to-evapotranspiration ratio, themean annual RDI is equal to the well-known aridity index devisedby UNEP (1992). Predominantly, Elagib and Mansell (2000b) andElagib (2009) used the UNEP aridity index to study the droughtconditions in Sudan. Quiring (2009) states that ‘‘Overall, no singleindex can represent all aspects of meteorological drought’’. He fur-ther identified three main aspects for a drought to be tractable,namely easiness of calculation, availability of data required andusefulness. As per Vasiliades et al. (2011), the bases for choice ofindices for drought monitoring in a specific area should eventuallybe (1) the quantity of climate data available and (2) the ability ofthe index to consistently detect spatial and temporal variationsduring a drought event. Moreover, Vangelis et al. (2011)emphasized the availability of suitable data to construct a droughtindex as well as its reliability and robustness. Robustness (Quiring,2009) refers to the ability of an index to (1) measure drought over awide range of climatic conditions, (2) be spatially and temporallycomparable and (3) have values independent of the seasonal cycle,and (4) be correlated with (and sensitive to) drought impacts andbe able to discriminate among these impacts. Since the climatein Sudan dominantly ranges from warm winter to very warm sum-mer, as mentioned in Section 2 an aridity index which accounts fortemperature is important owing to the fact that higher tempera-ture leads to rapid evaporation and insufficient soil moisture to re-place evapotranspiration.

Based on the above literature review and in order to account forthe basic effects of both temperature and rainfall on drought, adrought index devised by Pedj (1975), hereinafter referred to asPDI, is used in this study to assess the drought. The index is definedas the difference between the annual SAIs of surface air tempera-ture and rainfall as

PDI ¼ SAITm � SAIRF ð2Þ

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Ed DamazinTemperature Rainfall

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WauTemperature Rainfall

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Fig. 2 (cont

where SAITm and SAIRF are the standardized anomalies of mean sur-face air temperature and rainfall, respectively, of the area of inter-est. It indicates that higher temperature and less rainfall result ina drier climate (Qian and Zhu, 2001). Using this index, the objectivecomparison of the trends displayed by various stations is easily per-mitted by classifying the weather conditions as drought or damp asshown in Table 1. This index has been used by several researchersaround the world (e.g. Gruza et al., 1999; Qian and Zhu, 2001; Potopand Soukup, 2009; and Potop et al., 2010).

3.4. Statistical analysis

Using the Spearman rank correlation test (also called Spearmanrho test) (Kanji, 1997), the direction of trends in the time series ofanomalies and PDI as well as the association of temperature andrainfall over the country have also been explored. The latter reportsthe relationship between the SAIs of surface air temperature(mean, daytime and nighttime) and rainfall. Spearman’s rank cor-relation coefficient is used as a measure of linear relationship be-tween two sets of ranked data. Taking a value between �1 and+1, a positive value of the coefficient indicates increasing trendand vice versa. The test is non-parametric, i.e. it is not necessaryto assume that both variables are normally distributed.

3.5. Effect of El Niño-Southern Oscillation

Additionally, the significance of the effect of El Niño-SouthernOscillation (ENSO) index (JISAO, 2009) on PDI is investigated bythe Spearman rho test.

4. Results and discussion

Fig. 2 shows the time series of SAIs of both the mean annualtemperature and annual rainfall. Persistent warming is revealedfrom the mid-1990s onwards in the hyper-arid stations ofDongola, Atbara and Port Sudan, in the semi-arid stations of theeast (El Gedaref) and west (Nyala) and at the south-western drysub-humid station of Wau. The onset of successive warming yearsas of the 21st century is apparent in all arid areas, namely Kassala,

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JubaTemperature Rainfall

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inued)

Table 2Spearman rho statistic (two-tailed) test of the direction of trends in the standardizedclimatic anomalies SAITx, SAITn, SAITm (daytime, nighttime and mean temperatures,respectively), SAIRF (rainfall) and Pedj drought index (PDI).

Station SAITx SAITn SAITm SAIRF PDI

Southern regionJuba 0.452d 0.408d 0.480d 0.063 0.277a

Wau 0.687d 0.450d 0.678d �0.099 0.446d

Malakal 0.565d 0.196 0.432d �0.131 0.316b

Central regionEd Damazin �0.123 0.294a 0.192 �0.090 0.149Nyala 0.308a 0.824d 0.693d �0.230 0.599d

El Obeid 0.332b 0.420d 0.424d �0.136 0.317b

El Fasher 0.316b 0.609d 0.588d �0.318b 0.547d

El Gedaref 0.786d 0.682d 0.760d 0.193 0.452d

Wad Medani 0.454d 0.617d 0.582d �0.335c 0.553d

Kassala 0.155 0.833d 0.697d �0.402 d 0.644d

Shambat 0.397d 0.487d 0.431d �0.299a 0.480d

Northern regionAtbara 0.559d 0.256a 0.486d �0.237 0.453d

Dongola 0.069 0.443d 0.321a �0.329b 0.451d

Coastal regionPort Sudan 0.239a 0.353c 0.376c �0.255a 0.398d

a Significant at 0.01 6 a 6 0.05.b Significant at 0.005 < a 6 0.009.c Significant at 0.001 < a 6 0.005.d Significant at a 6 0.001.

Table 3Percentage frequency of occurrence of above-normal annual temperature and rainfallfor 1975–2008.

Station Temperature Rainfall

Juba 50.0 55.9Wau 35.3 58.8Malakal 52.9 32.4Ed Damazin 61.8 47.1Nyala 55.9 55.9El Obeid 67.6 50.0El Fasher 67.6 38.2El Gedaref 67.6 47.1Wad Medani 64.7 50.0Kassala 67.6 38.2Shambat 64.7 50.0Atbara 70.6 44.1Dongola 70.6 29.4Port Sudan 64.7 41.2


Shambat, Wad Medani, El Obeid and El Fasher, in addition to thesemi-arid stations (Ed Damazin and Malakal) located in thesouth-eastern part of the country. Table 2 shows that all the SAItime series of Tm are increasing significantly except in one case(Ed Damazin). Out of the 14 stations under consideration, 11 showsignificant increasing trends in their SAI time series of mean an-nual maximum temperatures. Ed Damazin also emerges as anexception. Elagib (2010a) attributes this exceptional temperaturebehavior to the location of the station in the vicinity of Er RoseiresDam, thus causing the so-called lake effect (Landsberg, 1970). All ofthe SAI time series of mean annual minimum temperatures, exceptthat for Malakal, have significant rising trends. Hence, it can be

Table 4Correlations between rainfall and temperature anomaly indices estimated by Spearman rh

Station SAIRF versus

SAITx SAITn SAIT

Entire period

Southern regionJuba �0.370d �0.130 �0.2Wau �0.291b 0.061 �0.1Malakal �0.411d �0.395d �0.4

Central regionEd Damazin �0.198 �0.250a �0.3Nyala �0.325c �0.289b �0.3El Obeid �0.391d �0.428d �0.4El Fasher �0.350c �0.379d �0.4El Gedaref �0.082 �0.138 �0.1Wad Medani �0.528d �0.390d �0.4Kassala �0.403d �0.511d �0.5Shambat �0.363d �0.307b �0.3

Northern regionAtbara �0.281a �0.269a �0.3Dongola �0.059 �0.114 �0.1

Coastal regionPort Sudan �0.159 �0.383d �0.3

a Significant at 0.01 6 a 6 0.05.b Significant at 0.005 < a 6 0.009.c Significant at 0.001 < a 6 0.005.d Significant at a 6 0.001.

concluded that the country has undergone conditions of warmingtemperatures on both daytime and nighttime scales.

On the other hand, the total rainfall was generally persistentlyabove-normal up to the early or mid-1960s except in three areas,namely El Gedaref, Malakal and Juba, where rainfall fluctuationsbetween above- and below-normal values were experienced.Thereafter, reduced rainfall amounts started to prevail nearlyeverywhere in the country. In the mid-1980s, the rainfall waswell-below normal leading to the abnormally catastrophicdrought. Drought conditions also characterized the northerntwo-third of the country during the early 1990s. Later, the annualrainfall anomalies suggest somewhat improvement in some years,but the high inter-annual variability seemed to be the norm. It isnoticeable from Table 2 that the SAI time series of annual rainfallare overwhelmingly decreasing, indicating depleting rainfall,though the significance of the trends is only a characteristic ofmost of the stations lying in the northern half of the country.The trends revealed by the time series of temperature and rainfallanomalies have lead to significant rising trends in PDI throughoutSudan, indicating a state of strengthening drought.

o test (one-tailed).

m SAITx SAITn SAITm

1971–2008

56a �0.439c �0.272a �0.377b

21 �0.079 0.362a 0.24889d �0.343a �0.332a �0.455c

08c �0.290a �0.227 �0.285a

19c �0.247 �0.005 �0.18448d �0.347a �0.257 �0.300a

24d �0.289a �0.017 �0.13730 �0.309a �0.287a �0.320a

77d �0.407c �0.277a �0.347a

58d �0.463c �0.438c �0.509d

41c �0.139 �0.006 �0.118

20c �0.185 �0.178 �0.26628 �0.100 �0.144 �0.114

10c �0.116 �0.423c �0.280a

1940s

1960s

Fig. 3. Decadal mean of standardized mean annual t

Table 5Correlations between PDI and both SAITm and SAIRF estimated by Spearman rho test(one-tailed). Significance level a 6 0.001.

Station Entire period 1975–2008

PDI vs SAITm PDI vs SAIRF PDI vs SAITm PDI vs SAIRF

Juba 0.824 �0.723 0.808 �0.863Wau 0.578 �0.793 0.473 �0.618Malakal 0.857 �0.855 0.857 �0.828Ed Damazin 0.800 �0.790 0.812 �0.809Nyala 0.849 �0.744 0.613 �0.829El Obeid 0.841 �0.834 0.787 �0.781El Fasher 0.671 �0.485 0.416 �0.451El Gedaref 0.812 �0.632 0.793 �0.818Wad Medani 0.669 �0.496 0.467 �0.362Kassala 0.897 �0.852 0.837 �0.876Shambat 0.876 �0.694 0.835 �0.571Atbara 0.854 �0.727 0.820 �0.737Dongola 0.738 �0.690 0.834 �0.593Port Sudan 0.811 �0.766 0.898 �0.656


During the second half of the data period (Table 3), more thantwo-third of the years were hotter than normal in the northern-most half of the country, while the other southernmost half hasregistered above-normal temperatures in �35–62% of the period.With respect to rainfall, the bulk of the areas under considerationhas undergone drier than normal conditions in �44–62% of theyears, though two additional stations scored up to �68 and �71%dry years. When the correlation between the rainfall and temper-ature anomalies was obtained (Table 4), it has been found thatthe correlation is negative and is generally less significant duringabout the last four decades as compared to the entire period. How-ever, the situation is opposite for one station (El Gedaref), wherethe correlation is stronger (slightly significant) during 1971–2008. The location of El Gedaref close to the Ethiopian highlandsand the extensive rain-fed agriculture practiced in this area prob-ably have localized influences on the climate here (Elagib,2010b). Dongola in the hyper-arid zone and with the least rainfall

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emperature (upper) and annual rainfall (lower).


amounts among the stations reveals no significant correlation be-tween the two parameters. At the coastal line (Port Sudan), the cor-relation is inexistent during the daytime. The correlation alsovanished during the second period in Shambat, Nyala and Atbara.Such local climatic behavior and controls with respect to rainfallwere discussed earlier by many (e.g. Oliver, 1969; Hammer,1972; Trilsbach and Hulme, 1984).

To show how the PDI direction is dictated by the direction of theclimatic anomalies, the correlations between PDI and both thetemperature and rainfall anomalies for the entire period and thesecond half of the study period is given in Table 5. As expected,the PDI is negatively correlated with rainfall anomalies and variesdirectly with temperature anomalies. However, more important isthat the PDI significantly captures the directions of both anomalies.Also important is that while the dependence of PDI on temperature

1980s

2000s

Fig. 3 (cont

is more with respect to the entire time series with the exception ofWau, the situation is reversed for six stations (Juba, Wau, Nyala, ElFasher, El Gedaref and Kassala) out of the 14 stations during thesecond half of the study period, indicating stronger influence ofrainfall at these stations.

Maps of the temperature and rainfall indices are portrayed atdecade intervals in Fig. 3 to study the corresponding spatial andtemporal changes. During the decades extending from the 1940still the 1960s, the annual anomalies of temperature and rainfallclearly show cool and wet conditions, respectively, with isolatedexceptions. Slight deficit of rainfall began to occur in the 1970sdecade particularly in the extreme north, coastal area, the centroidof the country and the areas of the extreme south and south east.This deficit well established in the 1980s. The exception was WadMedani and Juba. The former exception was due to the effect of

1990s

inued)


very high positive anomaly in 1988 (see e.g. Hulme and Trilsbach,1989). By this decade, the temperature began to exceed the meanover large parts of central and southern Sudan. In the 1990s, the

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PDI

Fig. 4. Time series of Pedj drought index: Annual

decadal average anomalies indicated a retreat of the peak droughtconditions of the 1980s to only the southern part of the hyper-aridarea in the north and the mid and eastern areas of the country. This

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NYALA

(bars) and 5-year moving average (solid line).


decade was characterized by warmer conditions prevailing innorthern, eastern, mid, western and south-western Sudan. The2000s were the worst years in terms of warmth, which dominatedthe whole country, with temperature tending to exhibit markedpositive anomalies of up to about 2 standard deviations. The aver-age rainfall situation in this decade worsened in terms of magni-tude and spatial extent. Negative rainfall anomalies prevailed inthe north and parts of eastern, mid and southern Sudan.

Presented in Fig. 4 are the time series of PDIs associated withtheir climate classifications. The whole country experienced ExHto VStH conditions during the first three decades of the study per-iod. Subsequently, positive PDI values became more frequent, anda magnitude of more than 3 (ExD) was reported. Northern Sudanshows lasting drought for about the last three decades. CentralSudan (the Sahelian part of the country) was dominated by thewell-known mid-1980s drought. Darfur region in the west revealsprolonged drought commencing in 1971 and ending in mid-1980s(El Fahser) or late 1980s (Nyala). An extended period of drought insouthern Sudan is evident from the mid-1970s to the end of the1980s in Malakal but to mid-1980s in Wau and Juba. Three stationsindicate increasing inter-annual variability of their PDI time seriestowards the end decades of the study period. These are El Gedarefin eastern Sudan (since the early 1990s) and Wau and Juba in thesouth (since the mid-1980s). This fluctuation between drought andhumid conditions means that the drought in these stations has be-come insidious. The time series of PDIs are predominantly risingsignificantly, except in the case of Ed Damazin, indicating intensi-fying drought conditions across the country (Table 2).

The decadal average climate classification based on PDI is givenin Fig. 5. During the 1940s, ExH conditions dominated the north-ernmost one-third of the country and the extreme south and southeast. The extreme eastern and western stations experienced StHconditions. The rest of the country fell in the MeH class. In the fol-lowing decade, the wet conditions enhanced in terms of magnitudeof PDI and extent. Conditions of StH and VStH characterized morestations in central Sudan. The exception to this was less intensity ofExH at the semi-arid stations of El Gedaref, Ed Damazin and Mala-kal as well as Port Sudan and Atbara in the hyper-arid zone. Three

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ED DAMAZIN

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WAU

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Fig. 4 (cont

humidity conditions, namely excess, mean and strong, classifiedthe stations during the 1960s. As of the 1970s, scattered MiD oc-curred in the country (El Obeid, Ed Damazin, and Juba), but ExH ex-isted throughout the rest of Sudan. In the 1980s, the PDI indicatesexceedingly MiD afflicting the coastal region, the larger part of theSahel region and the northern and south-western stations of south-ern Sudan. The decadal mean value of PDI for El Fasher indicatesExH; however, this was due to humid state during the second halfof the 1980s despite the four successive drought years in the begin-ning of the decade (1982–1985). Conditions of ExH also existedelsewhere in the country. By the last decade of the 20th century,MiD classified only pockets of the country, i.e. the extreme east,west, south-west and north of the country, while the rest of thestations lied in the ExH category, except El Fasher which presenteda normal condition. The 21st century hitherto shows evidence of areturn to drought conditions. Various intensities of drought (MiD,MoD and SeD) typified the largest part of the country, and onlyExH class was a feature of the extreme south-western part of Su-dan, namely Nyala, Wau and Juba.

The frequency of drought and humidity conditions are com-pared between two sub-periods, early and recent in Table 6. Appar-ently, the recent period has undergone the major part ofoccurrences of drought, particularly in the areas designated as hy-per-arid to semi-arid (from the area of Malakal and northward).Throughout the country, the frequency of drought occurrencesescalated from 8.8–40.0% during 1941–1974 to 44.1–70.6% for1975–2008.

Continuous drought period is an important feature to be ana-lyzed. Table 7 depicts this feature for the stations under consider-ation. The drought periods exhibited by the table are in line withthose discussed for the Sahel region of Africa by, for example,L’Hôte et al. (2002), Dai et al. (2004), Nicholson (2005), Lebel andAli (2009), indicating that the PDI can catch the major historicaldroughts of the 1970s and 1980s. It can also be noticed that, sincethe onset of the dry era in the late 1960s or early 1970s, the contin-uous drought periods have been increasing from an average of3 years in the beginning and middle of the era to an average of5 years in the 2000s. More pronounced is the prolonged dry period

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JUBA

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inued)


of more than 10 years in the end of the time series in northern andcoastal areas of Sudan (Dongola and Port Sudan, respectively). Jubais the only station which has not witnessed drought conditions inthe 2000s. For the eight stations used by Elagib (2009) in central Su-dan, the PDI produced results that are highly correlated to those ofthe UNEP dryness ratio (UNEP, 1992), which uses both rainfall andevapotranspiration input variables, with Spearman rho statistic(one-tailed) of �0.687 to �0.850 (a 6 0.001). This suggests thatPDI succeeds to perform reasonably as other recommended indices.

An examination of the PDI patterns of variation has provided anevidence of a footprint of ENSO over about two-third of the area ofthe country, i.e. south of latitude 15�N, an area encompassing thearid, semi-arid and dry sub-humid zones (Table 8). The correlationbetween the ENSO anomalies and PDIs was found to be positive. Thisconfirms the results obtained by Osman et al. (2001) and Osman andShamseldin (2002) for Sudan on the correlation between ENSO and

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Fig. 5. Decadal mean Pedj drough

annual rainfall that there is a co-existence between the driest yearsand warm ENSO events. However, it is worth mentioning that thisfactor does not explain all the variation in the drought regime inSudan, and a considerable proportion of the variation still remainsto be determined by other underlying factors (see e.g. Gasm El-Seed,1987). In his review of drought of the last millennium, Dai (2011)states that the southward shift of the warmest SSTs in the Atlantic,warming in the Indian Ocean, reduced vegetation cover and surfaceevaporation are responsible for the recent Sahel droughts.

5. Conclusions

The speculation of a recovery from the Sahelian drought hasbeen investigated using data from Sudan. Drought assessmentbased on measurements of precipitation seems limited because it

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t index and its classification.

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Fig. 5 (continued)


reflects only one component of the triggering inputs. The Pedjdrought index has been used herein in an attempt to capture otherinputs, such as temperature since it is an important contributingfactor to the process of evaporation and soil moisture reserves.Similar to other indices (Heim, 2002; Raziei et al., 2009), it couldbe argued that computing long timescale of this index (e.g. 12-month) is appropriate and addresses the hydrological drought, al-lows the avoidance of seasonal cycle while still accounting for theinter-annual variability and is suitable for water resources man-agement purposes. The index seems to perform reasonably and isresponsive to both temperature and rainfall events and is able todetect the onset of drought on a long timescale (year). Its spatialand temporal variations are consistent over the country as it isresponsive to the emerging drought and is able to show howdrought in one region compares to drought in another region. Italso proved satisfactory in simulating the drought episodes bythe well-known index developed by UNEP.

A significant rainfall decline over the northern half of the coun-try has been found. There has been a drastic increase in the tem-perature variables over the whole country, thus indicating lesseffective rainfall even in situations of above-normal rainfall events.Accordingly, it is seen that the trends in Pedj drought index areoverall increasing significantly, thus evidencing a tendency forthe droughts to progressively spread and intensify over Sudan.Drought has manifested itself during the recent decades and is stillunderway as depicted by the results of the 2000s. The drought inSudan during the 2000s is seen to be even stronger than previouslynoted by others for the African Sahel. Mild to severe droughts onaverage prevailed on a widespread manner, except in the extremesouth and south-west localities. In terms of severity, the decade ofthe 2000s was drier than even the decade of the 1980s on the aver-age. In some individual years of the 2000s, the PDI value within thehyper-arid and arid regions indicated severe or extreme drought.The year 2008 serves as an example of this condition for Dongola,

Table 7Extended drought periods (longer than 1 year).

Station Drought period

Juba 1972–1974; 1977–1979; 1981–1984; 1997–1998Wau 1971–1972; 1980–1983; 1985–1986; 1990–1991; 1993–1995; 1997–1999; 2002–2005Malakal 1972–1973; 1976–1980; 1982–1988; 1997–1998; 2002–2008Ed Damazin 1972–1973; 1978–1980; 1983–1988; 2002–2007Nyala 1972–1973; 1981–1987; 2001–2002; 2004–2005El Obeid 1969–1970; 1972–1973; 1979–1982; 1984–1988; 1990–1991; 2000–2005El Fasher 1972–1973; 1975–1976; 1982–1985; 1995–1997; 2001–2004El Gedaref 1969–1970; 1983–1984; 1987–1988; 1990–1992; 1997–1998; 2000–2002; 2004–2008Wad Medani 1972–1973; 1979–1980; 1982–1984; 1986–1987; 1990–1991; 2000–2006Kassala 1969–1970; 1979–1981; 1984–1985; 1990–1991; 1996–1997; 1999–2004Shambat 1969–1970; 1979–1981; 1984–1985; 1990–1991; 1998–2000; 2004–2005Atbara 1980–1981; 1984–1985; 1990–1991; 1995–2002; 2004–2008Dongola 1984–1987; 1990–1991; 1995–2008Port Sudan 1969–1970; 1973–1974; 1979–1981; 1987–1989; 1998–2008

Table 8Relationship between PDI and ENSO index.

Station Spearman rho Significance level a

Juba 0.375 0.002Wau 0.295 0.012Malakal 0.493 0.001Ed Damazin 0.247 0.029Nyala 0.394 0.001El Obeid 0.394 0.001El Fasher 0.306 0.009El Gedaref 0.335 0.005Wad Medani 0.370 0.002Kassala 0.277 0.017Shambat 0.165 0.106Atbara 0.123 0.176Dongola 0.047 0.363Port Sudan �0.045 0.368

Table 6Percentage frequency of occurrence of drought and humidity classes for the first (left column) and second (right column) sub-periods.

Class Juba Wau Malakal Ed Damazin Nyala El Obeid El Fasher

ExD 0.0 5.9 0.0 2.9 0.0 8.8 0.0 5.9 0.0 5.9 2.9 2.9 0.0 0.0SeD 0.0 5.9 0.0 2.9 5.9 11.8 2.9 5.9 0.0 8.8 0.0 8.8 0.0 14.7MoD 8.8 14.7 2.9 11.8 8.8 20.6 11.8 17.6 2.9 11.8 8.8 20.6 5.9 26.5MiD 14.7 17.6 8.8 32.4 14.7 26.5 17.6 23.5 5.9 26.5 20.6 20.6 8.8 14.7Nor 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0ExH 11.8 23.5 23.5 26.5 23.5 8.8 26.5 17.6 11.8 11.8 20.6 29.4 20.6 26.5MeH 26.5 20.6 35.3 20.6 32.4 8.8 29.4 14.7 5.9 32.4 20.6 8.8 23.5 8.8StH 20.6 8.8 11.8 2.9 8.8 14.7 11.8 14.7 29.4 2.9 8.8 2.9 17.6 8.8VStH 17.6 2.9 17.6 0.0 5.9 0.0 0.0 0.0 44.1 0.0 17.6 5.9 23.5 0.0

Drought 23.5 44.1 11.8 50.0 29.4 67.6 32.4 52.9 8.8 52.9 32.4 52.9 14.7 55.9Humidity 58.8 52.9 70.6 50.0 64.7 32.4 67.6 47.1 47.1 47.1 50.0 41.2 61.8 44.1

El Gedaref Wad Medani Kassala Shambat Atbara Dongola Port Sudan

ExD 0.0 5.9 0.0 5.9 0.0 8.8 0.0 0.0 0.0 0.0 0.0 2.9 0.0 14.7SeD 2.9 11.8 0.0 17.6 0.0 8.8 2.9 5.9 0.0 2.9 6.7 17.6 0.0 11.8MoD 2.9 14.7 2.9 8.8 5.9 17.6 5.9 20.6 16.1 17.6 10.0 20.6 14.7 14.7MiD 14.7 20.6 14.7 26.5 8.8 23.5 11.8 20.6 16.1 50.0 23.3 26.5 23.5 17.6Nor 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0ExH 41.2 23.5 11.8 11.8 14.7 17.6 20.6 41.2 41.9 11.8 36.7 14.7 26.5 23.5MeH 17.6 17.6 29.4 20.6 5.9 17.6 23.5 5.9 25.8 14.7 6.7 11.8 20.6 8.8StH 14.7 5.9 20.6 8.8 38.2 2.9 11.8 2.9 0.0 2.9 10.0 2.9 11.8 5.9VStH 5.9 0.0 20.6 0.0 26.5 2.9 23.5 2.9 0.0 0.0 6.7 2.9 2.9 2.9

Drought 20.6 52.9 17.6 58.8 14.7 58.8 20.6 47.1 32.3 70.6 40.0 67.6 38.2 58.8Humidity 73.5 47.1 61.8 41.2 58.8 38.2 55.9 50.0 67.7 29.4 53.3 29.4 58.8 38.2


Atbara, Port Sudan, Wad Medani, Kassala and El Fasher. Moreover,the dry period in this decade is longer than those of the 1970s and1980s. There is a strong ENSO footprint on drought south of lati-tude 15�N though it does not fully explain the drought phenome-non as evaluated by the PDI.

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major climate indicators of ongoing drought in sudan

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