Understanding the Changing Characteristics of …folk.uio.no/chongyux/papers_SCI/JHM_01.pdf · 2012-11-01 · Understanding the Changing Characteristics of Droughts in Sudan and the

Understanding the Changing Characteristics of Droughts in Sudanand the Corresponding Components of the Hydrologic Cycle

ZENGXIN ZHANG

Jiangsu Key Laboratory of Forestry Ecological Engineering, Nanjing Forestry University, Nanjing, China,

and Department of Geosciences, University of Oslo, Oslo, Norway

CHONG-YU XU

Department of Geosciences, University of Oslo, Oslo, Norway

BIN YONG

State Key Laboratory of Hydrology-Water Resources and Hydraulics Engineering, Hohai University, Nanjing, China

JUNJUN HU

School of Computer Science, University of Oklahoma, Norman, Oklahoma

ZHONGHUA SUN

Network and Information Center, Changjiang Water Resources Commission, Wuhan, China

(Manuscript received 24 August 2011, in final form 27 April 2012)

ABSTRACT

Droughts are becoming the most expensive natural disasters in former Sudan and have exerted serious

impacts on local economic development and ecological environment. The purpose of this paper is to improve

understanding of the temporal and spatial variations of droughts by using the Standard Precipitation Index

(SPI) and to discuss their relevance to the changes of hydrological variables in Sudan. The analysis results

show that 1) droughts start in the late 1960s in Sudan and severe droughts occur during the 1980s in different

regions of Sudan—the annual precipitation and soil moisture also reveal the evidence that the droughts

prevail since the late 1960s; 2) the greater negative soil moistures anomalies are found in central and southern

Sudan during the rainy seasons while greater negative anomalies of precipitation occur only in central Sudan

compared between 1969–2009 and 1948–68; 3) the precipitation recycling ratio averaged over 1948–2009

decreases from south to north and the percentage of local actual evapotranspiration to local precipitation in

dry conditions is greater than that in wet conditions; and 4) the highest (second highest) correlations appear

between soil moisture and precipitation (actual evapotranspiration) and the significant decreases in annual

soil moisture are associated with the decrease of annual precipitation and the increase of annual temperature.

This suggests that continuous droughts in Sudan are caused jointly by the decrease of precipitation and the

increase of temperature in the region.

1. Introduction

Droughts may be one of the world’s most costly nat-

ural disasters, occurring frequently in many countries.

A drought is an extended period of months or years

when a region notes a deficiency in its water supply. The

severity of the drought depends upon the degree of

moisture deficiency, duration, and size of the affected

area. It can have a substantial impact on the ecosystem

and agriculture of the affected region, which can cause

significant damage and harm to the local economy.

During the drought, sparse vegetation and dry soil limit

evapotranspiration. Less than usual amounts of water

vapor in the atmospheric boundary layer reduce the

availability of water vapor and potential energy, though

Corresponding author address:Zengxin Zhang, Ph.D., Associate

Professor, Jiangsu Key Laboratory of Forestry Ecological Engi-

neering, Nanjing Forestry University, Nanjing 210037, China.

E-mail: [email protected]

1520 JOURNAL OF HYDROMETEOROLOGY VOLUME 13

DOI: 10.1175/JHM-D-11-0109.1

� 2012 American Meteorological Society

not sufficient ingredients, for the generation of convec-

tive rainfall (Shukla and Mintz 1982; Koster et al. 2004).

Being often cumulated slowly over a considerable

period of time, it is difficult to precisely determine the

onset and end of a drought event. To monitor droughts

and wet spells and study their variability, it is necessary

to devise numerous specialized indices that combine

available data such as precipitation and temperature

(Heim 2000; Trenberth et al. 2004; Su and Wang 2007;

Kalamaras et al. 2010; Yang et al. 2012). In recent years

various indices have been proposed to detect and mon-

itor droughts and have been used in modeling droughts

as well as stochastic and water-balance simulations

(Palmer 1965; Lana et al. 1998; Mishra et al. 2007). The

standardized precipitation index (SPI) is one of the in-

dices commonly used in recent decades. The SPI can

simulate climatic conditions over a wide spectrum of time

scales. Moreover, it is based on precipitation changes

alone. Further, Hayes et al. (1999) argued that the SPI

detects moisture deficits more rapidly than the Palmer

drought severity index (PDSI; Bonaccorso et al. 2003).

The SPI attempts to determine the rarity of a drought or

an anomalously wet event on a particular time scale for

any location that has a precipitation record. A drought

event can be decided at a time interval when the SPI

value is persistently negative, and vice versa.

An accurate quantitative knowledge of the hydro-

logical components of the earth–atmosphere system,

on a regional and global basis, is of basic importance

in many branches of geophysics (Rasmusson 1968). The

locally supplied moisture or upward flux of water vapor

can be from evaporation of in situ open water or soil

moisture, or from plant transpiration. To maintain rain-

fall, water vapor must be supplied through the diver-

gence of water vapor from its source to sink regions.

Thus, the atmospheric branch of the hydrological cycle

constitutes a vital component for understanding the

changing features of water resources. However, because

of the lack of homogeneous data for hydrological vari-

ables (e.g., water vapor, precipitation, and actual evapo-

transpiration), themajor objectives of numerous previous

studies were mostly aimed at documenting the time-

mean atmospheric hydrological cycle and its seasonal

variation (Peixoto andOort 1983, 1992; Chen et al. 1995).

For example, Zangvil and Karas (2001) investigated the

time-scale relationships among the large-scale atmo-

spheric moisture budget components over theMidwestern

United States [35% of the Global Energy and Water

Cycle Experiment (GEWEX) Continental-Scale In-

ternational Project (GCIP) domain] in relation to summer

precipitation. Both the measurements and numerical

experiments in hydroclimatology have confirmed pos-

itive and negative land surface–climate feedbacks, of

which moisture recycling is a prominent phenomenon

at continental scales. Raddatz (2005) investigated the

contribution of land surface evapotranspiration to the

atmospheric water balance for the agricultural region of

the Canadian prairies by estimating the recycling ratios,

including the moistening and precipitation efficiencies,

for drought areas for the summers of 1997–2003.

The former Republic of Sudan was Africa’s largest

country with over 90% of its people living below the

poverty line. Southern Sudan was split from the north

and created the world’s newest nation in July 2011.

This study was completed before the separation of the

Sudan and our study area covers the Sudan and south-

ern Sudan; in the rest of the paper we call the study area

Sudan for short. Frequent droughts and environmental

degradation are the major obstacles to livelihood se-

curity and food self-reliance in Sudan. Over 80% of

Sudan’s population lives in rural areas, depending on

agriculture and livestock to make a living. It is be-

coming a phenomenon in Sudan that 1 in every 5 years

is dry. When the droughts come, agriculture collapses,

people migrate, and those who stay face conflict over

food and water supplies. Since the infamous famine of

1984/85, Sudan has suffered severe droughts in 1989,

1990, 1997, and 2000. Each drought brought crop failure,

loss of livestock, and loss of pastureland. In 1984, a crop

failure and spread of waterborne diseases caused by

drought in Sudan took the lives of 55 000 people, which

weakened the socioeconomic capabilities of the nomadic

tribes (Osman and Shamseldin 2002). The droughts

and famine might be one of the most serious threats to

Sudan.

Comprehensive analysis and reviews of rainfall trends

and variability in Africa, including the Sahel region and

Sudan, had been carried out by many researchers and

most reputable works include those of Hulme and his

coauthors (e.g., Trilsbach and Hulme 1984; Hulme 1987;

Hulme and Tosdevin 1989; Hulme 1990; Walsh et al.

1988).Walsh et al. (1988) reported that declining rainfall

in semiarid Sudan since 1965 has continued and inten-

sified in the 1980s. Hulme (1990) pointed out that rain-

fall depletion has been most severe in semiarid central

Sudan between 1921–50 and 1956–85. The length of the

wet season has contracted, and rainfall zones have mi-

grated southward (Zhang et al. 2011). The temperature

is rising and rainfall is declining for the past several de-

cades, which might be the main cause of the drought in

Sudan (e.g., Alvi 1994; Janowiak 1988; Nicholson et al.

2000).

The decreasing precipitation might be related to the

atmospheric moisture transport. Much research work

has been performed regarding the moisture variabilities

over Africa (e.g., Cadet and Nnoli 1987; Fontaine et al.

OCTOBER 2012 ZHANG ET AL . 1521

2003; Osman and Hastenrath 1969). They pointed out

that at more local scales moisture advections and con-

vergences are also significantly associated with the ob-

served Sudan–Sahel rainfall and in wet (dry) situations,

with a clear dominance of westerly (easterly) anomalies

in the moisture flux south of 158N. Zhang et al. (2011)

revealed that the precipitation of the main rain season

(i.e., July, August, and September) and annual total

precipitation in the central part of Sudan decreased

significantly during 1948–2005 and the decreasing pre-

cipitation in Sudan was associated with the weakening

African summer monsoon. The summer moisture flux

over Sudan tended to be decreasing after the late 1960s,

which decreased the northward propagation of moisture

flux in North Africa.

The atmospheric branch of the hydrological cycle re-

flects the natural variability of weather and climate at

the regional and global scales. However, it is not obvious

how these changes will be reflected in terms of droughts.

Precipitation recycling plays a key role in the hydro-

logical process and the precipitation recycling ratio is a

diagnostic measure for interactions between land sur-

face hydrology and regional climate. The analysis of

atmospheric hydrology recycling usually relies heavily

on the National Centers for Environmental Prediction

(NCEP)–National Center for Atmospheric Research

(NCAR)orEuropeanCentre forMedium-RangeWeather

Forecasts (ECMWF) reanalysis data; however, the pre-

cipitation, actual evapotranspiration, and soil moisture

data are derived from unconstrained reanalysis systems.

In other words, observations of these quantities are not

assimilated into the reanalysis system, so the assimilating

model is free to produce them, typically through param-

eterizations. So the reanalysis precipitation and actual

evapotranspiration are highly model dependent. Roads

et al. (2002) pointed out that maintaining the NCEP/

Department of Energy Global Reanalysis 2 (NCEP-2)

close to observations requires some nudging to the short-

range model forecast, and this nudging is an important

component of analysis budgets to assess global and re-

gional water and energy budgets. Trenberth et al. (2011)

analyzed the water and energy cycles in the last version

of the NCAR climate model [Community Climate Sys-

tem Model, version 4 (CCSM4)] and found that the

moisture transport from ocean to land should all be

identical but are not close in most reanalyses, and they

thought that major improvements are needed in model

treatment and assimilation of moisture, and surface

fluxes from reanalyses should only be used with great

caution. Therefore, it is not unexpected that the rean-

alysis precipitation and actual evapotranspiration ex-

hibit some deficiencies and that this field does not

compare as well with observations as other reanalysis

fields such as heights, winds, and temperatures that are

assimilated directly into the model. This poses serious

risks to conclusions drawn from analyzing this type of

data (Trenberth and Guillemot 1998). Even in more

modern reanalysis systems designed specifically for hy-

droclimate research (i.e., North American Regional Re-

analysis) the terrestrial water budgets are problematic

(Nigam and Ruiz-Barradas 2006; Weaver et al. 2009). In

this research, we only chose the wind fields, height fields,

and humidity fields to compute the atmospheric mois-

ture content and precipitation recycling ratio, while

other variables—such as actual evapotranspiration, soil

moisture, and temperature data—are taken from the Cli-

mate Prediction Center (CPC) and the Climatic Research

Unit (CRU).

To understand, and hopefully to be able to predict, the

impact of these changes on the droughts in Sudan, we

need to understand the relationship between the droughts

and the hydrologic variables in Sudan. The specific

objectives of this study are 1) to analyze the changing

feature of the drought and its involvement in the at-

mospheric branch of the water cycle in Sudan, 2) to

improve our understanding on the hydrologic processes

in the land surface branch of thewater cycle in Sudan, and

3) to explore the relationship between the droughts and

their possible cause based on the atmospheric branch

of the hydrologic cycle.

2. Study area and data

Former Sudan is a vast country with an area of about

2.5 million km2 and hosts an estimated population of

about 41.1 million people. The location of Sudan and

South Sudan can be seen in Fig. 1a. Stretching over 188of latitude and 168 of longitude, the climate ranges from

arid in the north to tropical wet and dry in the far south-

west. About two-thirds of Sudan lies in dry and semidry

regions. The most significant climatic variables are rain-

fall and the length of the rainy season (Xu et al. 2010).

Monthly precipitation data have been selected from

the global precipitation reconstruction data (PREC) es-

timates on a 0.58 3 0.58 latitude–longitude grid over the

period 1948–2009 in Sudan. The PREC analyses are

derived from gauge observations from over 17 000 sta-

tions collected in the Global Historical Climatology

Network (GHCN), version 2, and the Climate Anomaly

Monitoring System (CAMS) datasets (Chen et al. 2002).

The areal mean PREC data and the Sahel precipitation

index during 1948–2009 are compared and the results

show that the PREC data has a good agreement with the

Sahel precipitation index in the long term in the Sahel

region (108–208N, 208W–108E). Spatial distributions of

mean annual precipitation revealed by the PREC data


are then compared with the interpolated observed data

of 39 stations for the period 1961–90 and the compar-

ison shows the spatial patterns of PREC data are sim-

ilar to that of the observed annual mean precipitation

(Zhang et al. 2011). Atmospheric data was provided

by the NCEP–NCAR reanalysis (R-1) over the period

1948–2009. Wind, temperature, atmospheric pressure,

and specific humidity are available on a 2.58 3 2.58latitude–longitude grid. The soil moisture is selected

from a CPC global monthly soil moisture dataset at 0.58resolution produced by a one-layer ‘‘bucket’’ water-

balance model (Fan and van denDool 2004). The driving

input fields are global monthly precipitation (PREC) and

global monthly temperature. The potential evapotrans-

piration and actual evapotranspiration data are com-

puted by the Thornthwaite monthly water-balance

model driven by global monthly precipitation (PREC)

and global temperature from the CRU TEM3v dataset

at 0.58 resolution. The CRU temperature data has been

proven by many researchers, which has a high credibility

in many areas (Simmons et al. 2004). Wind components,

specific humidity, and covariance, which are needed for

atmospheric content and precipitation recycling ratio

computations, are provided at eight standard pressure

levels (1000, 925, 850, 700, 600, 500, 400, and 300 hPa).

Although there are a large number of variables that

can be examined to understand the characteristics of

the droughts in Sudan, this study focuses on the exami-

nation of atmospheric hydrological components (e.g.,

precipitation, actual evapotranspiration, soil moisture,

and precipitation recycling ratio) and their relation with

the droughts in Sudan. Better understanding of the re-

lation between the atmospheric hydrological components

and the droughtsmay lead to additional confidence in our

ability to predict the droughts. For better understanding

the relationship between the features of droughts and

the associated hydrological variables in Sudan, we will

analyze the variations of precipitation, atmospheric

moisture content, potential evapotranspiration, actual

evapotranspiration, temperature, soil moisture, and the

precipitation recycling ratio.

3. Methods

In the actual atmosphere, the atmospheric moisture

is very low over 300 hPa, so p5 300 hPa will be used in

the calculation. The moisture content (Q) was calculated

based on the following equations (Zhou et al. 1998):

Q521

g

ðpps

q(P) dP , (1)

where q is the specific humidity, ps is surface pressure,

p is atmospheric pressure at 300 hPa, and g is accelera-

tion of the gravity.

The precipitation recycling ratio was computed ap-

proximately following the approach of Eltahir and Bras

(1994, 1996). The recycling formula is based on the

FIG. 1. (a) The location of Sudan and southern Sudan and (b) the distribution of the annual average precipitation

based on PREC data during 1948–2009 in Sudan.


principle of mass conservation. Two species of water

vapor molecules are considered: molecules that are in

the atmosphere because of evaporation from within the

region considered and molecules that are in the atmo-

sphere as a result of atmospheric transport across the

boundary of the region (outside the region). For a finite

control volume of the atmosphere located at any point

within the region, conservation of mass of the two spe-

cies requires the following.

According to the principle of water balance, water

vapor content changing temporally is expressed by the

following equations:

›Ww

›t5 Iw 1E2Ow 2Pw and (2a)

›Wo

›t5 Io 2Oo2Po , (2b)

where P, W, and E are the regional average precipita-

tion, water vapor content, and actual evapotranspira-

tion, respectively; Iw andOw are water vapor inflow and

outflow fluxes supplied by evapotranspiration within the

region; and Io andOo are water vapor inflow and outflow

fluxes supplied by evaporation from outside the region.

In deriving the general recycling formula, we make

two assumptions. The first assumption states that water

vapor is well mixed in the planetary boundary layer

(PBL) of the earth’s atmosphere. The PBL is of the

order of 1 km deep and contains most of the water

vapor in the atmosphere. Observations of the vertical

distribution of water vapor and other conserved tracers

show a practically uniform distribution through the PBL

up to the level where the air from the PBLmixes with the

upper air. Based on the above-mentioned assumption,

the precipitation recycling ratio, r, can be defined as

r5Pw

Pw 1Po

5Ww

Ww1Wo

5Ow

Ow 1Oo

. (3)

At any location within the region, r estimates the ratio

of recycled precipitation to the total precipitation falling

at that location.

For a large-scale region Iw is very small comparedwith

water vapor fluxesOw at a long time scale. That is to say

we can make the assumption that Iw is zero in large

spatial and temporal scale. Equation (2) can be re-

arranged as follows:

Iw 1E5Ow 1Pw and (4a)

Io 5Oo1Po . (4b)

Substituting for Ow, Pw, Oo, and Pw from (3) into (4)

results in

Iw1E5 r(Ow 1Oo)1 r(Pw1Po) and (5a)

Io 5 (12 r)(Ow 1Oo)1 (12 r)(Pw1Po) . (5b)

Combining Eqs. (5a) and (5b), the average precipi-

tation recycling ratio is deduced as follows:

r5Iw 1E

Iw1E1 Io. (6)

Here, r is the average recycling ratio over a region.

The U.S. Geological Survey (USGS) Thornthwaite

monthly water-balance model is used to compute the

potential evapotranspiration and actual evapotranspi-

ration (http://wwwbrr.cr.usgs.gov/projects/SW_MoWS/

software/thorn_s/thorn.shtml). The water-balance model

is based on the methodology originally developed by

Thornthwaite (Thornthwaite 1948; Mather 1978, 1979;

McCabe and Wolock 1999; Wolock and McCabe 1999)

and the basic procedure of the model used in this study

is similar to that used by Xu and Chen (2005). Inputs to

the model are monthly mean temperature, monthly total

precipitation, and the latitude of the location of interest.

Outputs include monthly potential and actual evapo-

transpiration, soil moisture storage, snow storage, and

runoff. El Haj El Tahir et al. (2012) compared the actual

evapotranspiration in Sudan estimated using the re-

mote sensing method [Surface Energy Balance Algo-

rithm for Land (SEBAL)], the modified Thornthwaite

water-balance method (WB), and the complementary

relationship method [Granger and Gray model (GG);

Granger andGray (1989)] in the BlueNile, eastern Sudan.

The soil water holding capacities in the Thornthwaite

model are approximately 205, 108, and 154 mm in 1-m-

deep soil for stationsAbuNaama,Damazine, andGedarif,

respectively. The results show that the three methods give

comparable results, and the agreement between SEBAL

and WB is closer than the agreement between SEBAL

and GGmethod during the wet season (July–September).

The Mann–Kendall (MK) trend test (Mann 1945;

WMO 1966; Kendall 1975; Sneyers 1990) is widely used

in the literature to analyze trends in the climate data. In

contrast to the traditional MK test, which calculates the

statistic variables only once for the whole sample, the

MK method can also be used to test an assumption re-

garding the beginning of the development of a trend

within a sample—that is, a changing point in the time

series (Zhang et al. 2010). Following the procedure as

shown by Gerstengarbe and Werner (1999), who used

the method to test an assumption about the beginning of

the development of trend within a sample (x1, x2, . . . , xn)

of the random variableX, the corresponding rank series

for the so-called retrograde rows are similarly obtained


for the retrograde sample (xn, xn21, . . . , x1). Based on the

rank series r of the progressive and retrograde rows of

this sample, the statistic variables Z1 and Z2 are calcu-

lated for the progressive and retrograde samples, re-

spectively. The Z1 and Z2 values calculated with

progressive and retrograde series are named UF and

UB, respectively, in this paper. The intersection point of

the two lines, UF and UB give the point in time of the

beginning of a developing trend within the time series.

The SPI is defined to describe the periods of dryness

and wetness. It is based on the long-term precipitation

data for a desired period. This long-term record is fitted

to a probability distribution, which is then transformed

into a normal distribution so that the mean SPI for the

location and desired period is zero (Edwards andMcKee

1997). In this paper gamma probability distribution was

selected for SPI calculation at time scales of 3, 6, 12,

and 24 months (Bordi et al. 2003). The gamma proba-

bility density function is expressed as

g(x)51

baG(a)xa21ex/b for x. 0, (7)

where a . 0 is a shape parameter, b . 0 is a scale pa-

rameter, and x . 0 is the amount of precipitation; G(a)defines the gamma function (Thom 1958).

Then an equal probability transformation from a gamma

to a normal distribution is applied (Guttman 1999):

SPI5xi2 xi

s. (8)

The SPI is a dimensionless index where negative (posi-

tive) values indicate drought (wet) conditions. McKee

et al. (1993) defined the criteria for a ‘‘drought event’’

for any time steps and classified the SPI to define various

drought intensities.

Fitting the distribution function to data requires an

estimation of a and b values. Edwards andMcKee (1997)

suggested that these two parameters can be estimated

using the maximum likelihood approximation by Thom

(1958) for

a51

4A

11

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi11

4A

3

r !and (9)

b5 xa, (10)

where

A5 ln(x)2� ln(x)

n(11)

and N 5 number of precipitation observations. Inte-

grating the probability density function with respect to x

and the estimates of a and b yields an expression of the

cumulative probability G(x) of precipitation for a given

time step (here the time step is one month):

G(x)5

ðx0g(x) dx5

1

baG(a)0

ðx0xa21ex/b dx . (12)

Since the gamma distribution is undefined for x5 0 and

q 5 P(x 5 0) . 0, where q is the probability of zero

precipitation, an adapted statisticH(x) can be calculated

using the following formula:

H(x)5 q1 (12q)G(x) . (13)

The cumulative probability distribution is then trans-

formed into the standard normal distribution to yield

the SPI. Since the above approach is not practical for

computing the SPI for large numbers of data points,

such as in our case, we used the approximate conversion

suggested by Abramowitz and Stegun (1965). Detailed

procedures of the calculation of the SPI can be found in

Guttman (1999) and Lloyd-Hughes and Saunders (2002)

(cited in Kemal et al. 2005).

The aim here was to identify areas vulnerable to dry-

ness and wetness at comparable time steps based on their

occurrence frequencies (Livada and Assimakopoulos

2007). An SPI classification scale is used to iden-

tify drought conditions according to the SPI values

(Table 1).

TABLE 1. SPI categories based on the initial classification of SPI values.

Category SPI

Probability of occurrence (%)

Region I Region II Region III Region IV Sudan

Extremely wet 2.00 and above 3.30 3.02 1.87 0.57 1.44

Very wet 1.50 to 1.99 2.16 2.30 3.45 3.02 3.88

Moderately wet 1.00 to 1.49 7.90 10.92 10.06 14.94 10.92

Near normal 20.99 to 0.99 68.68 71.84 71.12 67.53 70.11

Moderately dry 21.00 to 21.49 11.49 5.17 6.90 5.75 5.17

Severely dry 21.50 to 21.99 4.74 2.01 3.45 4.89 5.17

Extremely dry 22.00 and less 1.72 4.74 3.16 3.30 3.30


4. Results

a. Characteristics of droughts in Sudan

From the distribution of annual mean precipitation,

actual evapotranspiration, potential evapotranspiration,

and soil moisture (Fig. 2), it can be found that the average

annual actual evapotranspiration and soil moisture vary

greatly in Sudan and decrease from south to north,

which is very similar to the pattern of annual mean

precipitation. However, the potential evapotranspiration

is different; the higher value occurs in northern Sudan and

lower value appears in southern Sudan.

Four climate regions were divided based on the an-

nual mean precipitation features in Sudan (Fig. 1b).

Figure 3 shows the SPI series on the 24 months based on

monthly precipitation (PREC) data in the four regions

of Sudan for 1948–2009. The SPI at 24 months is con-

sidered as a hydrological drought index that can be

used to monitor surface water resources [e.g., river flows

(Hayes et al. 1999)]. At this time scale, droughts lasted

FIG. 2. (a)–(d) Distributions of annual mean precipitation, actual evapotranspiration, potential evapotranspiration,

and soil moisture averaged on 1948–2009 in Sudan.


longer, but were less frequent with few dryness or wet-

ness periods (Livada and Assimakopoulos 2007). As a

whole, the dryness and wetness variabilities show simi-

lar patterns in different regions in Sudan and wet con-

ditions prevailed in the whole of Sudan during 1948–68,

while drier conditions were experienced during 1969–

2009. The extremely wet and dry events were recorded

in 1945/55 and 1984/85, respectively. From this figure, it

is clear that the transition from wet period to dry period

occurs in the late 1960s and more dryness and less wet-

ness are found since then.

Although the dry and wet conditions look similar in

different regions in Sudan (Fig. 3), a close look reveals

different features of the dryness and wetness variations.

For example, the wet condition seems to be ending

earlier in north Sudan than in south Sudan during the

1960s (Figs. 3a,d), and the amplitude of drought in cen-

tral and south Sudan seems higher than in north Sudan.

From Table 1 we also find that fewer extremely wet and

very wet events are found in south Sudan (the proba-

bility of occurrence for regions III and IV are 1.87% and

0.57%, respectively) than in central and north Sudan

with the probability of occurrence over 3%. However,

more extreme dry events can be found in central and

south Sudan than in north Sudan. But for the whole

country, we can find that the frequency of extreme dry

events is more than that of extreme wet events. The an-

nual precipitation and soil moisture are also used to

monitor the droughts in Sudan. The areal average annual

precipitation over the whole country decreased during

1948–2009 and the abrupt change point can be found in

1968 by using the Mann–Kendall method (Fig. 4a), and

the soil moisture also shows a significantly decreasing

trend during 1948–2009 (Fig. 4b).

As stated previously, more droughts can be found

from the late 1960s and the droughts have lasted over

more than 40 years in Sudan. From the spatial aspects,

more droughts can be found in central Sudan. Similar

results were obtained by Hulme (1990) when he re-

ported that the depletion has been most severe in semi-

arid central Sudan. Other researchers (e.g., Osman and

Shamseldin 2002) also found that the areal annual

FIG. 3. The SPI series for 24 months in different regions of Sudan

and southern Sudan (the different regions are based on Fig. 1).

FIG. 4. The MK Z values of the hydrological variables in the

hydrological cycle. (a) UF and UB represent the Z values for pro-

gressive and retrograde precipitation series, respectively, and (b) Z

values for progressive series of six hydrological variables are shown.


averaged rainfall values decreased markedly since the

1960s, and the drought in the 1970s produces a large

number of impacts that affects Sudan’s social, environ-

mental, and economical standard of living with reduced

crop, reduced water levels, increased livestock, and

wildlife death rates and damage to wildlife and fish

habitat (Zhang et al. 2011).

b. The atmospheric hydrological variables

Owing to the influence by the tropical and continental

climate, the distribution of rainfall in Sudan is very

asymmetric. The average annual rainfall shows a descend-

ing trend from south to north. More recent analysis

indicates that the precipitation of Sudan has a close

relation to the amount of moisture transport during the

rainy season (Zhang et al. 2011), and the continuous

serious droughts of Sudan might be affected by the

atmospheric hydrological cycle. Zhang et al. (2011) found

that the whole-layer moisture flux in summer [June–

August (JJA)] during 1948–2005 decreased significantly

in Sudan, which is in good line with the changes of pre-

cipitation in Sudan. To better understand the changing

characteristics of droughts in Sudan and the corre-

sponding hydrological variables of the hydrologic cycle,

the trends of the areal annual mean hydrological vari-

ables were analyzed by using the MK method (Fig. 4b).

The annual mean atmospheric moisture content, pre-

cipitation, actual evapotranspiration, and soil moisture

decrease significantly during 1948–2009, while the tem-

perature and potential evapotranspiration show signifi-

cant increasing trends over the whole country.

To further analyze the spatial and temporal variation

of hydrological variables, the time–latitude cross section

averaged over 22.58–37.58E is shown in Fig. 5. From this

figure, obvious positive atmospheric moisture content,

precipitation, actual evapotranspiration, and soil mois-

ture anomalies can be found in the 1950s and 1960s,

while negative anomalies occur in the 1970s, 1980s, and

1990s. However, the potential evapotranspiration and

temperature anomalies are opposite to that of soil mois-

ture and precipitation in which the negative anomalies

present in 1950s and 1960s and positive anomalies occur

since the late of 1970s. Then we can find that the pattern of

potential evapotranspiration and temperature anomalies

are opposite to the patterns of actual evapotranspiration

and precipitation, which are very similar to soil moisture.

c. The relationship between the droughts and thehydrological recycle in Sudan

As shown above, the changes of actual evapotranspira-

tion and soil moisture are in good line with that of atmo-

spheric moisture content and precipitation in a long time.

But what will happen if they are under dry conditions?

Themeridional cross section of themean hydrological

variables’ differences between the dry period (1970–

2005) and wet period (1948–69) averaged over 22.58–37.58E are shown in Fig. 6. For atmospheric moisture

content, precipitation, actual evapotranspiration, and

soil moisture, obvious negative anomalies can be found.

The negative anomalies values become greater from

January to August and decrease afterward, and the lo-

cation of maximum negative anomalies varies greatly in

different months. The negative precipitation anomalies

can be found in the central Sudan in the rainy season with

the precipitation anomalies larger than 300 mm yr21.

Similar results can be found with other hydrological

components, such as atmospheric moisture content, ac-

tual evapotranspiration, and soil moisture, for which

greater anomalies largely occurred in central Sudan and

in the rainy season. The maximum negative values are

located in southern Sudan in the dry season and in central

and north Sudan in the rainy season.A similar pattern can

be found in potential evapotranspiration and tempera-

ture except that the anomalies are positive.

Figure 7 shows the spatial distribution of the hydro-

logical variables’ anomalies between the dry and wet

periods. From this figure, obvious negative anomalies

for atmospheric moisture content, precipitation, actual

evapotranspiration, and soil moisture can be found in

the whole Sudan and the greater negative anomalies are

located in central Sudan, while positive anomalies can be

found in the whole country for potential evapotranspi-

ration and temperature.

To further investigate the differences of hydrological

variables between the dry and wet conditions, we ana-

lyzed the precipitation recycling ratio over Sudan (Fig. 8).

The precipitation recycling ratio, which is defined as the

contribution of local evapotranspiration to local pre-

cipitation, aims at understanding the hydrological process

in the atmospheric branch of the water cycle (Eltahir and

Bras 1996). From this figure, we can find that the pre-

cipitation recycling ratio averaged over 1948–2009 de-

creases from south to north and the large value is located

in south Sudan. It can be found that the contribution of

local evapotranspiration to local precipitation is more

than 30%–40% in south Sudan while the contribution is

only 10%–20% in central Sudan (Fig. 8a). The precipita-

tion recycling ratios for the African region are presented

by Brubaker et al. (1993); two peaks appear in March

(r 5 0.41) and in August (r 5 0.48). The February–

March peak corresponds to fairly high E and low P in

those months, while the July–August peak corresponds

to a season of high E and high P. The annual mean

precipitation recycling ratio is about 0.3 on the areal

average over Africa. The comparison of precipitation

recycling ratio in the dry conditions and wet conditions


is shown in Fig. 8b and Fig. 8c, from which we can find

that the precipitation recycling ratio in the dry conditions

(averaged over 1948–68) is greater than that of wet con-

ditions (averaged over 1969–2009), which indicates the

percentage of local evapotranspiration converting into

local precipitation in the dry conditions is higher than that

in wet conditions. Similar results can be found in the

central United States, as Bosilovich and Schubert (2001)

pointed out that the 1988 (drought year) summer re-

cycling ratio is larger than that of 1993 (flood year), and

that the 1988 recycling ratio is much larger than aver-

age. And the diagnosed recycling data show that the

recycled precipitation is large when moisture transport

is weak and convergence and evaporation are large.

FIG. 5. Time–latitude cross section (averaged over 22.58–37.58E) of hydrological variablesanomalies compared with the average on 1948–2009 in Sudan: (a) moisture content, (b) pre-

cipitation, (c) temperature, (d) potential evapotranspiration, (e) actual evapotranspiration, and

(f) soil moisture. Units are 8C for temperature, and mm for other variables.


To better quantitatively estimate the relationship be-

tween the hydrological variables, we calculate the corre-

lations between them (Table 2). From this table, we can

find good relationships between the hydrological vari-

ables. The table reveals that there are high correlations

between potential evapotranspiration and temperature

with the correlation coefficient of 0.87 and precipitation

and actual evapotranspiration with correlation coeffi-

cient of 0.86. Significant correlations can also be found

between soil moisture and atmospheric moisture con-

tent, precipitation, temperature, and actual evapotrans-

piration; the highest correlation coefficient appears

FIG. 6. Time–latitude cross section (averaged over 22.58–37.58E) for the hydrological variables anomalies between 1969–2005 and 1948–

68: (a) atmospheric moisture content, (b) precipitation, (c) temperature, (d) potential evapotranspiration, (e) actual evapotranspiration,

and (f) soil moisture. Units are the same as in Fig. 5.


FIG. 7. Spatial anomalies’ distribution of the hydrological variables’ anomalies between 1969–2005 and

1948–68: (a) atmospheric moisture content, (b) precipitation, (c) temperature, (d) potential evapotranspi-

ration, (e) actual evapotranspiration, and (f) soil moisture. Units are the same as in Fig. 5.


between soil moisture and precipitation, and the second

largest correlation coefficient is between soil moisture

and actual evapotranspiration, which indicates the soil

moisture might be more affected by them.

5. Conclusions

In this study, we analyzed the characteristics of

droughts in Sudan and the corresponding hydrological

components during 1948–2009 with the aim of exploring

the changing features of droughts and possible relation-

ship between the droughts and hydrologic variables in

Sudan. The following conclusions can be drawn from

the study.

1) From the estimation of the SPI on a 24-month time

scale, we can find wet conditions prevailed during

1948–68 over the whole Sudan while drier conditions

FIG. 8. Spatial distribution of annual mean precipitation recycling ratio over Sudan: (a) averaged for 1948–2009, (b)

averaged for 1948–68, and (c) averaged for 1969–2009.


experienced since the late 1960s. More dry events

and fewer wet events were found since the late 1960s

and the extreme dry and wet events are recorded in

1984/85 and 1954/55, respectively. The changes of

annual precipitation and soil moisture also reveal

this evidence.

2) Significant decreasing trends can be found in the

annual precipitation, atmospheric moisture content,

actual evapotranspiration, and soil moisture during

1948–2009, while significant increasing trends occur

in the annual temperature and potential evapotrans-

piration. Precipitation and actual evapotranspiration

are the leading terms in the atmosphericwater balance

over Sudan. The significant decreases in annual soil

moisture are associated with the decrease of annual

precipitation and the increase of annual temperature.

3) The patterns of soil moisture are most similar to that

of atmospheric moisture content and precipitation

and the patterns of potential evapotranspiration and

temperature anomalies are opposite to that of soil

moisture during 1948–2009. Negative anomalies be-

tween 1969–2009 and 1948–68 for atmosphericmoisture

content, precipitation, and actual evapotranspira-

tion can be found in Sudan and the greater negative

anomalies are located in central Sudan, while posi-

tive anomalies can be found in the whole country for

potential evapotranspiration and temperature. So the

changes of the hydrological components might lead

to more severe droughts in central Sudan.

4) Significant correlations are found between the soil

moisture and other hydrologic variables (such as pre-

cipitation, atmospheric moisture content, temperature,

and actual evapotranspiration); the highest correlation

appears between soil moisture and precipitation and

the second highest correlation is between the soil

moisture and actual evapotranspiration.

5) The precipitation recycling ratio averaged over 1948–

2009 decreases from south to north and the large

values are located in south Sudan. It can be found

that the percentage of local evapotranspiration to

local precipitation is more than 30%;40% in south

Sudan while the percentage is only 10%;20% in

central Sudan. The precipitation recycling ratio in

the dry condition (averaged over 1948–68) is greater

than the wet condition (averaged over 1969–2009),

which indicates the percentage of local evapotrans-

piration converting into local precipitation in the

dry conditions is greater than that of wet conditions.

Acknowledgments. This work is financially supported

by the Research Council of Norway with project num-

ber 171783 (FRIMUF), ‘‘985 Project’’ (Grant 37000-

3171315), and by the Open Fund of State Key Laboratory

of Satellite Ocean Environment Dynamics and the In-

stitute ofDesertMeteorology,CMA(Grant Sqj20080011).

The second author is also supported by the Program of

Introducing Talents of Discipline to Universities—The

111 Project of Hohai University. The authors wish to

thank the reviewers for their valuable comments and

suggestions, which greatly improved the quality of the

paper.

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