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JOURNAL OF HYDROMETEOROLOGY
VOLUME 13
Understanding the Changing Characteristics of Droughts in Sudan
and 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 natural disasters, occurring frequently in many countries.
A drought is an extended period of months or years
Corresponding author address: Zengxin Zhang, Ph.D., Associate
Professor, Jiangsu Key Laboratory of Forestry Ecological Engineering, Nanjing Forestry University, Nanjing 210037, China.
E-mail: zhangzengxin77@yahoo.com.cn
DOI: 10.1175/JHM-D-11-0109.1
Ó 2012 American Meteorological Society
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
OCTOBER 2012
ZHANG ET AL.
not sufficient ingredients, for the generation of convective 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 monitor 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 indices 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 hydrological 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 rainfall, water vapor must be supplied through the divergence 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 variables (e.g., water vapor, precipitation, and actual evapotranspiration), the major objectives of numerous previous
studies were mostly aimed at documenting the timemean atmospheric hydrological cycle and its seasonal
variation (Peixoto and Oort 1983, 1992; Chen et al. 1995).
For example, Zangvil and Karas (2001) investigated the
time-scale relationships among the large-scale atmospheric moisture budget components over the Midwestern
United States [35% of the Global Energy and Water
Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) domain] in relation to summer
precipitation. Both the measurements and numerical
experiments in hydroclimatology have confirmed positive and negative land surface–climate feedbacks, of
1521
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 southern 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 security 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 becoming 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 intensified in the 1980s. Hulme (1990) pointed out that rainfall 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 migrated southward (Zhang et al. 2011). The temperature
is rising and rainfall is declining for the past several decades, 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.
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JOURNAL OF HYDROMETEOROLOGY
2003; Osman and Hastenrath 1969). They pointed out
that at more local scales moisture advections and convergences are also significantly associated with the observed 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 precipitation 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 reflects 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 hydrological process and the precipitation recycling ratio is a
diagnostic measure for interactions between land surface 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) or European Centre for Medium-Range Weather
Forecasts (ECMWF) reanalysis data; however, the precipitation, 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 parameterizations. 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 shortrange model forecast, and this nudging is an important
component of analysis budgets to assess global and regional 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 System 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 reanalysis precipitation and actual evapotranspiration exhibit some deficiencies and that this field does not
compare as well with observations as other reanalysis
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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 hydroclimate research (i.e., North American Regional Reanalysis) 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 moisture content and precipitation recycling ratio, while
other variables—such as actual evapotranspiration, soil
moisture, and temperature data—are taken from the Climate 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 atmospheric branch of the water cycle in Sudan, 2) to
improve our understanding on the hydrologic processes
in the land surface branch of the water 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 188
of latitude and 168 of longitude, the climate ranges from
arid in the north to tropical wet and dry in the far southwest. About two-thirds of Sudan lies in dry and semidry
regions. The most significant climatic variables are rainfall and the length of the rainy season (Xu et al. 2010).
Monthly precipitation data have been selected from
the global precipitation reconstruction data (PREC) estimates 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 stations 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
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ZHANG ET AL.
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.
are then compared with the interpolated observed data
of 39 stations for the period 1961–90 and the comparison shows the spatial patterns of PREC data are similar 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 2.58
latitude–longitude grid. The soil moisture is selected
from a CPC global monthly soil moisture dataset at 0.58
resolution produced by a one-layer ‘‘bucket’’ waterbalance model (Fan and van den Dool 2004). The driving
input fields are global monthly precipitation (PREC) and
global monthly temperature. The potential evapotranspiration and actual evapotranspiration data are computed 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 examination 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 relation between the atmospheric hydrological components
and the droughts may 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 p 5 300 hPa will be used in
the calculation. The moisture content (Q) was calculated
based on the following equations (Zhou et al. 1998):
Q52
1
g
ðp
q(P) dP,
(1)
ps
where q is the specific humidity, ps is surface pressure,
p is atmospheric pressure at 300 hPa, and g is acceleration of the gravity.
The precipitation recycling ratio was computed approximately following the approach of Eltahir and Bras
(1994, 1996). The recycling formula is based on the
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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 atmosphere 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 species requires the following.
According to the principle of water balance, water
vapor content changing temporally is expressed by the
following equations:
›Ww
5 Iw 1 E 2 Ow 2 Pw
›t
and
›Wo
5 Io 2 Oo 2 Po ,
›t
(2a)
(2b)
where P, W, and E are the regional average precipitation, water vapor content, and actual evapotranspiration, respectively; Iw and Ow are water vapor inflow and
outflow fluxes supplied by evapotranspiration within the
region; and Io and Oo 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 PBL mixes with the
upper air. Based on the above-mentioned assumption,
the precipitation recycling ratio, r, can be defined as
r5
Pw
Ww
Ow
5
5
.
Pw 1 Po Ww 1 Wo Ow 1 Oo
(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 compared with
water vapor fluxes Ow 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 rearranged as follows:
Iw 1 E 5 Ow 1 Pw
Io 5 Oo 1 Po .
and
(4a)
(4b)
Substituting for Ow, Pw, Oo, and Pw from (3) into (4)
results in
VOLUME 13
Iw 1 E 5 r(Ow 1 Oo ) 1 r(Pw 1 Po ) and
Io 5 (1 2 r)(Ow 1 Oo ) 1 (1 2 r)(Pw 1 Po ) .
(5a)
(5b)
Combining Eqs. (5a) and (5b), the average precipitation recycling ratio is deduced as follows:
r5
Iw 1 E
.
Iw 1 E 1 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 evapotranspiration (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 evapotranspiration, soil moisture storage, snow storage, and
runoff. El Haj El Tahir et al. (2012) compared the actual
evapotranspiration in Sudan estimated using the remote sensing method [Surface Energy Balance Algorithm for Land (SEBAL)], the modified Thornthwaite
water-balance method (WB), and the complementary
relationship method [Granger and Gray model (GG);
Granger and Gray (1989)] in the Blue Nile, eastern Sudan.
The soil water holding capacities in the Thornthwaite
model are approximately 205, 108, and 154 mm in 1-mdeep soil for stations Abu Naama, Damazine, and Gedarif,
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 GG method 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 regarding 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 variable X, the corresponding rank series
for the so-called retrograde rows are similarly obtained
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ZHANG ET AL.
TABLE 1. SPI categories based on the initial classification of SPI values.
Probability of occurrence (%)
Category
SPI
Region I
Region II
Region III
Region IV
Sudan
Extremely wet
Very wet
Moderately wet
Near normal
Moderately dry
Severely dry
Extremely dry
2.00 and above
1.50 to 1.99
1.00 to 1.49
20.99 to 0.99
21.00 to 21.49
21.50 to 21.99
22.00 and less
3.30
2.16
7.90
68.68
11.49
4.74
1.72
3.02
2.30
10.92
71.84
5.17
2.01
4.74
1.87
3.45
10.06
71.12
6.90
3.45
3.16
0.57
3.02
14.94
67.53
5.75
4.89
3.30
1.44
3.88
10.92
70.11
5.17
5.17
3.30
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 calculated for the progressive and retrograde samples, respectively. 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 and McKee
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 probability density function is expressed as
g(x) 5
1
x
ba G(a)
a21 x/b
e
for
x . 0,
(7)
where a . 0 is a shape parameter, b . 0 is a scale parameter, 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):
SPI 5
xi 2 xi
.
s
(8)
The SPI is a dimensionless index where negative (positive) 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 and McKee (1997)
suggested that these two parameters can be estimated
using the maximum likelihood approximation by Thom
(1958) for
1
11
a
^5
4A
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !
4A
and
11
3
^ 5 x,
b
a
^
(9)
(10)
where
A 5 ln(x) 2
å ln(x)
(11)
n
and N 5 number of precipitation observations. Integrating 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
ðx
0
g(x) dx 5
1
ba G(a)0
ðx
xa21 ex/b dx . (12)
0
Since the gamma distribution is undefined for x 5 0 and
q 5 P(x 5 0) . 0, where q is the probability of zero
precipitation, an adapted statistic H(x) can be calculated
using the following formula:
H(x) 5 q 1 (1 2 q)G(x) .
(13)
The cumulative probability distribution is then transformed 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 dryness and wetness at comparable time steps based on their
occurrence frequencies (Livada and Assimakopoulos
2007). An SPI classification scale is used to identify drought conditions according to the SPI values
(Table 1).
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FIG. 2. (a)–(d) Distributions of annual mean precipitation, actual evapotranspiration, potential evapotranspiration,
and soil moisture averaged on 1948–2009 in Sudan.
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 annual 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 considered 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
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ZHANG ET AL.
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FIG. 4. The MK Z values of the hydrological variables in the
hydrological cycle. (a) UF and UB represent the Z values for progressive and retrograde precipitation series, respectively, and (b) Z
values for progressive series of six hydrological variables are shown.
FIG. 3. The SPI series for 24 months in different regions of Sudan
and southern Sudan (the different regions are based on Fig. 1).
longer, but were less frequent with few dryness or wetness periods (Livada and Assimakopoulos 2007). As a
whole, the dryness and wetness variabilities show similar patterns in different regions in Sudan and wet conditions 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 wetness 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 central 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 probability 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 annual 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 reported that the depletion has been most severe in semiarid central Sudan. Other researchers (e.g., Osman and
Shamseldin 2002) also found that the areal annual
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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, environmental, 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 descending 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 precipitation in Sudan. To better understand the changing
characteristics of droughts in Sudan and the corresponding hydrological variables of the hydrologic cycle,
the trends of the areal annual mean hydrological variables were analyzed by using the MK method (Fig. 4b).
The annual mean atmospheric moisture content, precipitation, actual evapotranspiration, and soil moisture
decrease significantly during 1948–2009, while the temperature and potential evapotranspiration show significant 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 moisture 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 moisture 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 the
hydrological recycle in Sudan
As shown above, the changes of actual evapotranspiration and soil moisture are in good line with that of atmospheric moisture content and precipitation in a long time.
But what will happen if they are under dry conditions?
VOLUME 13
The meridional cross section of the mean 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 location 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, actual 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 temperature except that the anomalies are positive.
Figure 7 shows the spatial distribution of the hydrological 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 evapotranspiration and temperature.
To further investigate the differences of hydrological
variables between the dry and wet conditions, we analyzed the precipitation recycling ratio over Sudan (Fig. 8).
The precipitation recycling ratio, which is defined as the
contribution of local evapotranspiration to local precipitation, 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 precipitation recycling ratio averaged over 1948–2009 decreases 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 precipitation 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
OCTOBER 2012
ZHANG ET AL.
1529
FIG. 5. Time–latitude cross section (averaged over 22.58–37.58E) of hydrological variables
anomalies compared with the average on 1948–2009 in Sudan: (a) moisture content, (b) precipitation, (c) temperature, (d) potential evapotranspiration, (e) actual evapotranspiration, and
(f) soil moisture. Units are 8C for temperature, and mm for other variables.
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 conditions (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 recycling ratio is larger than that of 1993 (flood year), and
that the 1988 recycling ratio is much larger than average. And the diagnosed recycling data show that the
recycled precipitation is large when moisture transport
is weak and convergence and evaporation are large.
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JOURNAL OF HYDROMETEOROLOGY
VOLUME 13
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.
To better quantitatively estimate the relationship between the hydrological variables, we calculate the correlations between them (Table 2). From this table, we can
find good relationships between the hydrological variables. 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 coefficient of 0.86. Significant correlations can also be found
between soil moisture and atmospheric moisture content, precipitation, temperature, and actual evapotranspiration; the highest correlation coefficient appears
OCTOBER 2012
ZHANG ET AL.
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 evapotranspiration, (e) actual evapotranspiration, and (f) soil moisture. Units are the same as in Fig. 5.
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JOURNAL OF HYDROMETEOROLOGY
VOLUME 13
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.
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 relationship 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
OCTOBER 2012
TABLE 2. The correlations between the areal annual mean hydrological variables over the whole Sudan [temperature (TMP),
potential evapotranspiration (PET), actual evapotranspiration (AET),
and soil moisture (SM); boldface indicates significant at 0.05 level].
Q
PREC
TMP
PET
AET
SM
2)
3)
4)
5)
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ZHANG ET AL.
Q
PREC
TMP
PET
AET
SM
1.0
0.43
1.0
20.53
20.25
1.0
20.33
20.2
0.87
1.0
0.26
0.86
0.02
0.05
1.0
0.56
0.85
20.53
20.42
0.70
1.0
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.
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 evapotranspiration. Precipitation and actual evapotranspiration
are the leading terms in the atmospheric water balance
over Sudan. The significant decreases in annual soil
moisture are associated with the decrease of annual
precipitation and the increase of annual temperature.
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 between 1969–2009 and 1948–68 for atmospheric moisture
content, precipitation, and actual evapotranspiration can be found in Sudan and the greater negative
anomalies are located in central Sudan, while positive 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.
Significant correlations are found between the soil
moisture and other hydrologic variables (such as precipitation, 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.
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 evapotranspiration 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 number 171783 (FRIMUF), ‘‘985 Project’’ (Grant 370003171315), and by the Open Fund of State Key Laboratory
of Satellite Ocean Environment Dynamics and the Institute of Desert Meteorology, 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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