drought characteristics of bangladesh

HYDROLOGICAL PROCESSESHydrol. Process. 22, 2235–2247 (2008)Published online 19 November 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/hyp.6820

Spatial and temporal characteristics of droughts in thewestern part of Bangladesh

Shamsuddin Shahid*Department of Geography, Rhodes University, Grahamstown 6140, South Africa

Abstract:

Spatial and temporal characteristics of droughts in the western part of Bangladesh have been analysed. Standardizedprecipitation index method is used to compute the severity of droughts from the rainfall data recorded in 12 rainfall gaugestations for the period of 1961–1999. An artificial neural network is used to estimate missing rainfall data. GeographicInformation System (GIS) is used to map the spatial extent of droughts of different severities in multiple time scales. Criticalanalysis of rainfall is also carried to find the minimum monsoon and dry months rainfall require in different parts of the studyarea to avoid rainfall deficit. The study shows that the north and north-western parts of Bangladesh are most vulnerable todroughts. A significant negative relationship between multiple ENSO index and rainfall is observed in some stations. Analysisof seasonal rainfall distribution, rainfall reliability and long-term rainfall trend is also conducted to aid prediction of futuredroughts in the area. Copyright 2007 John Wiley & Sons, Ltd.

KEY WORDS droughts; rainfall; standardized precipitation index; GIS; Bangladesh

Received 14 June 2006; Accepted 1 May 2007

INTRODUCTION

Droughts are recurrent phenomena in the western part ofBangladesh. Since independence in 1971, the country hassuffered from nine droughts of major magnitude (Paul,1998). The impact of droughts was higher in the west-ern part of the country compared to other parts. In recentdecades, the hydro-climatic environment of north-westernBangladesh has been aggravated by environmental degra-dation and cross- country anthropogenic interventions(Banglapedia, 2003). Scientists have become increasinglyconcerned about the frequent occurrence of drought inwestern districts of Bangladesh, and this paper reportson studies of drought conditions in the western part ofBangladesh.

Although droughts may occur at any time of the year,the impact of droughts during the pre-monsoon period ismore severe in Bangladesh. High yield variety Boro rice,which is cultivated in 88% of the potentially availableareas of the country, grows during this time. A deficitof rainfall during this period causes huge damage toagriculture and to the economy of the country. As forexample, drought in 1995 led to a decrease in riceand wheat production of 3Ð5 ð 106 ton in the country(Rahman and Biswas, 1995). This necessitated the importof huge amount of food grains to offset the shortagein national stocks and meet the national demand on anemergency basis (Paul, 1998). In this paper, pre-monsoondrought as well as droughts due to a deficit of monsoonrainfall have been studied.

* Correspondence to: Shamsuddin Shahid, Department of Geography,Rhodes University, Grahamstown 6140, South Africa.E-mail: sshahid [email protected]

Drought is a dynamic phenomenon, which changesover time and space. Therefore, complete analysis ofdrought requires study of its spatial and temporal extents.Hydrological investigation over a large area requiresassimilation of information from many sites, each with aunique geographic location (Shahid et al., 2000). Geo-graphic Information System (GIS) maintains the spatiallocation of sampling points, and provides tools to relatethe sampling data through a relational database. There-fore, it can be used effectively for the analysis of spatiallydistributed hydro-meteorological data and modelling. Inthe present paper, GIS is used for the spatial modellingof droughts in western Bangladesh at various time-scales.

The common indicators of drought include meteoro-logical variables such as precipitation and evaporation,as well as hydrological variables such as stream flow,groundwater levels, reservoir and lake levels, snow pack,soil moisture, etc. Based on these indicators, numer-ous indices have been developed to identify the sever-ity of drought conditions (Dracup et al., 1980; Wilhiteand Glantz, 1985, 1987). However, most meteorolog-ical drought indices are based on precipitation data,e.g. Percentage of Normal Index (Banerji and Chabra,1964), Precipitation Deciles Index (Gibbs and Maher,1967), Bhalme–Mooley Drought Index (Bhalme andMooley, 1980), Standardized Precipitation Index (McKeeet al., 1993), Effective Drought Index (Byun and Wilhite,1999), etc. Among these methods, the Standardized Pre-cipitation Index (SPI) quantifies the precipitation deficitfor multiple time steps, and therefore facilitates the tem-poral analysis of droughts. It has been found that SPIis better able to show how drought in one region com-pares to drought in another region (Guttman, 1998). It

Copyright 2007 John Wiley & Sons, Ltd.

2236 S. SHAHID

has also been reported that SPI provides a better spa-tial standardization than the other indices (Lloyd-Hughesand Saunders, 2002). Therefore, SPI is used to studythe spatial and temporal characteristics of meteorologicaldrought in western Bangladesh. Critical rainfall analysis,seasonal rainfall distribution, rainfall reliability and long-term rainfall trend are also studied to aid prediction ofdroughts in the area.

HYDRO-CLIMATE OF BANGLADESH

Geographically, Bangladesh extends from 20°340N to26°380N latitude and from 88°010E to 92°410E longi-tude. Climatically, the country belongs to the sub-tropicalregion where monsoon weather prevails throughout theyear in most parts of the country. The average tempera-ture of the country ranges from 17 °C to 20Ð6 °C duringwinter and 26Ð9 °C to 31Ð1 °C during summer. The aver-age relative humidity for the whole year ranges from70Ð5% to 78Ð1%, with a maximum in September and aminimum in March. Three distinct seasons can be rec-ognized in Bangladesh from the climatic point of view:(i) the dry winter season from December to February;(ii) the pre-monsoon hot summer season from March toMay; and (iii) the rainy monsoon season, which lastsfrom June to October (Rashid, 1991).

The spatial distribution of rainfall over the country isshown in Figure 1a. The map has been prepared fromrainfall data for the 30 years 1970–1999, available at50 meteorological stations situated in and around thecountry. The average annual rainfall of the country variesfrom 1329 mm in the north-west to 4338 mm in thenorth-east (Shahid et al., 2005). The map shows that thewestern part of Bangladesh receives much lower rainfallthan other parts of the country. The monthly distributionof rainfall over the western part of the country is shownon the graph in Figure 1b. The monthly distribution iscalculated from rainfall data for the 39 years 1961–1999available at 12 stations in the study area. The rightvertical axis of the graph represents rainfall in millimetresand the left vertical axis represents the rainfall as apercentage of annual total rainfall. The graph shows thatrainfall is very much seasonal in the area, almost 77%of rainfall occurring during the monsoon. In summer, thehottest days experience temperatures of 45 °C or evenhotter. In the winter the temperature falls to 5 °C in someplaces (Banglapedia, 2003). Thus, the region experiencestwo extremities that clearly contrast with the climaticconditions of the rest of the country.

A dryness study of Bangladesh, carried out using theDe Martonne aridity index (Figure 2a) and the Thornth-waite precipitation effectiveness index (Figure 2b) meth-ods (Essenwanger, 2001) from climatic data for the30 years 1970–1999 available at 50 meteorological sta-tions situated in and around Bangladesh, shows that west-ern side of Bangladesh can be classified ‘sub-humid’,the central part ‘humid’ and a small part of the north-eastern side ‘wet’. The lowest index values obtained

by De Martonne and Thornthwaite methods are 20Ð89and 64Ð04, respectively, in the central-western and north-western parts of Bangladesh. As the dryness index valuesin the region are close to those of a dry zone, the climateof these regions of Bangladesh can be considered veryclose to ‘dry’. The total annual evapotranspiration in thispart of Bangladesh is also lower than or equal to theannual rainfall in some years. The location map of thestudy area is shown in Figure 3.

DATA AND METHODS

Rainfall data for the 39 years 1961–1999 from 12meteorological stations in the western part of Bangladeshwas used to study the characteristics of meteorologicaldrought. The main problem encountered during the studyof droughts is missing rainfall data. The methods used toestimate the missing rainfall data and to study droughtcharacteristics are discussed below.

Estimation of missing rainfall data

Numerous methods for estimating missing data havebeen described in the literature (Creutin and Obled, 1982;Seo et al., 1990; Kuligowshi and Barros, 1998; Schnei-der, 2001; Teegavarapu and Chandramouli, 2005). In thepresent study, a feedforward artificial neural network(ANN) approach similar to that proposed by Teegavarapuand Chandramouli (2005) is used for the estimation ofmissing rainfall data. ANNs are computer models thatmimic the structure and functioning of the human brain,and are known for their ability to generalize well on awide variety of problems and are well suited to predic-tion applications (Bishop, 1995). Unlike many statisticalmethods, ANN models do not make dependency assump-tions among input variables and can solve multivariateproblems with nonlinear relationships among input vari-ables. The efficiency of ANN models does not dependon the density of measuring stations, rather on the num-ber of stations used for the estimation of missing data(Teegavarapu and Chandramouli, 2005). As the densityof rain gauges in the study area is low and ANNs aresupposed to be suited to any distribution of rainfall sta-tions, the method is used in this paper for the estimationof missing rainfall data.

The missing rainfall data is random in most stations,however, continuous missing data for several years isalso evident at some stations. The percentage of missingrainfall data varies between 6% and 22% from stationto station, except one station (Khepupara), where about39% of the data is missing. The average level of missingrainfall data in the study area is 14%. Although the per-formance of ANNs improves with increasing percentageof training data, studies have shown that training with60% of the total data can reliably estimate unknown data(Teegavarapu and Chandramouli, 2005). Therefore, it canbe assumed that the ANN model estimated missing datain the present study with acceptable accuracy.

Copyright 2007 John Wiley & Sons, Ltd. Hydrol. Process. 22, 2235–2247 (2008)DOI: 10.1002/hyp

DROUGHTS OF BANGLADESH 2237

Figure 1. (a) Spatial distribution of annual mean rainfall over Bangladesh; (b) monthly distribution of rainfall in the western part of Bangladesh

The topology of the ANN used for the estimation ofmissing rainfall data is 6 : 4 : 1, as shown in Figure 4. Thetopology was selected using a trial and error procedure.The input neurons use values from six neighbouringstations around the station of interest and the outputneuron of the ANN provides the missing value at thestation of interest. Neural network training is doneusing a supervised back-propagation training algorithm(Rumelhart and Mclelland, 1986; Haykin, 1994). Thechoice of learning rate, momentum factor and activationfunction for the ANN determines the rate and reliabilityof the training of the network. In the present case, alearning rate of 0Ð1 and momentum factor of 0Ð4 wasused. These factors were obtained by a trial and errormethod (Haykin, 1994). A gradient descent techniquewas used to adopt weights in the ANN structure tominimize the mean squared difference between the ANN

output and the desired output. In the hidden and outputlayers, a sigmoidal activation function was used to modelthe transformation of values across the layers. Aftercomputing the missing rainfall data, a geospatial databaseof rainfall time series is developed within a GIS byfollowing the concept proposed by Goodall et al. (2004).

Calculation of standardized precipitation index

The standardized precipitation index (SPI, Mckeeet al., 1993) is a widely used drought index based on theprobability of precipitation for multiple time scales, e.g.1-, 3-, 6-, 9-, 12-, 18- and 24-month. It provides a com-parison of the precipitation over a specific period with theprecipitation totals from the same period for all the yearsincluded in the historical record. For example, a 3-monthSPI at the end of May compares the March-April-Mayprecipitation total in that particular year with the March


2238 S. SHAHID

Figure 2. Aridity maps obtained by (a) De Martonne aridity index; and (b) Thornthwaite precipitation effectiveness index methods from rainfall datafor the 30 years 1970–1999

to May precipitation totals of all the years. Consequently,it facilitates the temporal analysis of drought phenomena.

To compute SPI, historic rainfall data at each stationare fitted to a gamma probability distribution function:

g�x� D 1

ˇ˛�˛��˛�1e�x/ˇ for x > 0

where ˛ > 0 is a shape parameter, ˇ > 0 is a scaleparameter, x > 0 is the amount of precipitation, and �˛�defines the gamma function.

The maximum likelihood solutions are used to opti-mally estimate the gamma distribution parameters, ˛ andˇ for each station and for each time scale:

˛ D 1

4A

(1 C

√1 C 4A

3

)

ˇ D x

˛

where:

A D ln�x� �∑

ln�x�

n

and n D number of precipitation observations. Thisallows the rainfall distribution at the station to be effec-tively represented by a mathematical cumulative proba-bility function given by:

G�x� D∫ x

0g�x�dx D 1

ˇ˛�˛�

∫ x

0x˛�1e�x/ˇdx

Since the gamma function is undefined for x D 0 and aprecipitation distribution may contain zeros, the cumula-tive probability becomes:

H�x� D q C �1 � q�G�x�



Figure 3. Study area and the location of meteorological stations

Figure 4. Topology of artificial neural network used for the estimation ofmissing rainfall data

where, q is the probability of a zero. The cumulativeprobability H�x� is then transformed to the standardnormal distribution to yield the SPI (McKee et al., 1993).

As the precipitation rate is fitted to a gamma distribu-tion for different time scales for each month of the year,the resulting function represents the cumulative proba-bility of a rainfall event for a station for a given monthof the dataset and at different time scales of interest.This allows one to establish classification values for SPI.McKee et al. (1993) classified drought severity accordingto SPI values as given in Table I. An SPI of 2 or morerepresents a very severe drought, and happens about 2Ð3%of the time or about once in every fifty years. An SPIbetween �1Ð5 and �1Ð99 represents a severe drought,and happens about 4Ð4% of the time or once in every25 years. An SPI between �1Ð0 and �1Ð49 represents amoderate drought, and happens about 9Ð2% of the time or

almost once per decade. Details of the SPI algorithm canbe found in Guttman (1998; 1999), McKee et al. (1993;1995) and Hayes et al. (1999).

Rainfall reliability

To compute rainfall reliability, the coefficient of rain-fall variation (CV) in percentage is used,

CV D 100 ð �υ/R�

where υ D standard deviation, and R D arithmetic meanof rainfall (mm).

Spatial interpolation

For the mapping of the spatial extent of rainfall anddroughts from point data, a Kriging interpolation methodis used. Geostatistical analysis tool of ArcMap 9Ð1 (ESRI,2004) is used for this purpose. Kriging is a stochas-tic interpolation method (Journel and Huijbregts, 1981;Isaaks and Srivastava, 1989), which is widely recog-nized as a standard approach for surface interpolationbased on scalar measurements at different points. Stud-ies showed that Kriging gives better global predictionsthan other methods (van Beers and Kleijnen, 2004). How-ever, Kriging is an optimal surface interpolation methodbased on spatially dependent variance, which is gener-ally expressed as a semi-variogram. Surface interpolationusing Kriging depends on the selected semi-variogrammodel, and the semi-variogram must be fitted with amathematical function or model. Depending on the shapeof semi-variograms, different models are used in thepresent study for their fitting.

RESULTS AND DISCUSSION

The occurrence of droughts in the study area is identifiedfrom SPI time series of multiple-time steps. In the presentstudy, SPI for 3- and 6-months time steps are computed tostudy the characteristics of drought in short and mediumtime periods. The 3-month SPI is used to describe thepre-monsoon drought, while the 6-month SPI is used tocharacterize seasonal droughts that occur due to rainfalldeficit in monsoon and non-monsoon months.

Temporal and spatial distribution of drought

The regional SPI time series are calculated by aThiessen polygon method for 3 and 6-months time steps,and are shown in Figure 5a and b, respectively. Major

Table I. Drought categories defined for SPI values

SPI value Drought category Probability ofoccurrence (%)

0 to �0Ð99 Near normal or mild drought 34Ð1�1Ð00 to �1Ð49 Moderate drought 9Ð2�1Ð50 to �1Ð99 Severe drought 4Ð4�2Ð00 and less Extreme drought 2Ð3


2240 S. SHAHID

Figure 5. Arial values of SPI for (a) 3-month and (b) 6-month time steps

droughts identified from the areal SPI time series arein the years of 1963, 1966, 1968, 1973, 1977, 1979,1982, 1989, 1992 and 1994–1995. Spatial extension ofthe 3-month SPI at the end of May and 6-month SPIat the end of November for the four worst droughtyears in Bangladesh after independence in 1971 isshown in Figures 6 and 7, respectively. The 3-monthSPI calculated for May uses the precipitation total forMarch, April and May while the 6-month SPI calculatedfor November uses the precipitation total for June toNovember. The 3-month SPI shows a pre-monsoondrought and the 6-month SPI at the end of Novembershows a seasonal monsoon drought.

Figure 6 shows that in 1982, 62% of the study area wasaffected by drought, among which, 12% was affected bysevere droughts and 9% was by very severe pre-monsoondrought. In 1989 and 1992, the whole study area wasaffected by drought. About 78% of the area in 1989 and26% of the area in 1992 was affected by severe drought.In 1995, almost 95% of the area was affected by drought,with 49% of the area experiencing severe drought and22% experiencing very severe pre-monsoon drought.

The spatial extent of the 6-month SPI (Figure 7) showsthat in 1982, almost 44% of the study area was affectedby drought, with almost 21% of the area affected bysevere drought (SPI <1Ð5). In 1989, about 65% of thearea was affected by drought, with 13% affected bysevere drought and 4% by very severe drought. In 1992almost 72% of the area had an SPI less than �1Ð0 and23% of the area had an SPI less than �2Ð0. In 1995 about45% of the area had a 6-month SPI below �1Ð0 and 29%had an SPI less than �1Ð5. The spatial extent of both 3-and 6-month SPIs show that in most of the drought yearsthe central-western, north-western and northern areas hadan SPI less than �1Ð0. Figure 6. Spatial distribution of 3-month SPI computed for the month of

May in the four worst drought years after Bangladesh independence



Figure 7. Spatial distribution of 6-month SPI computed for the month ofNovember in the four worst drought years after Bangladesh independence

The area potentially liable to suffer drought at differenttime steps is identified on the basis of their occurrences.The percentage of drought occurrences is computed bytaking the ratio of drought occurrences in each time stepto the total drought occurrences in the same time step anddrought category (McKee et al., 1993). The percentage ofoccurrences of moderate, severe and very severe droughtsare shown in Figures 8, 9 and 10, respectively. In eachfigure, the upper map shows the drought for a 3-monthtime step and the lower one shows the drought for a6-month time step.

The spatial distribution of moderate droughts(Figure 8a) indicates that they tend to occur in the south-eastern part of the area at the 3-month time step. Thesouth-western and northern parts experience moderatedrought with lower frequencies at the 3-month time step.As the time step increases to 6 months (Figure 8b), thehigh drought potential zone is found to shift to the south-western part of the area. The northern part has less poten-tial for moderate drought at the 6-month time-step.

The distribution of severe droughts (Figure 9a) shows acompletely different pattern from the moderate drought.

The north-western part of the area is found to be themost likely to suffer severe drought at the 3-month timestep. With increase in the time step from 3 months to6 months, the area with potentially high occurrence isfound to expand from the north-western to the northernpart of the area (Figure 9b). The central part of the areais also found to have some potential for severe droughtat both 3- and 6-month time steps. The percentageoccurrence of droughts in these categories is less in thesouthern coastal parts.

Figure 10a shows that very severe droughts at the 3-month time step occur in the northern part of the area withhigh percentage and in the western part with moderatepercentage. The high drought occurrence area is foundto expand to the north-western part as the time step isincreased from 3 months to 6 months (Figure 10b). Thecentral part of the area is found to have less potentialfor very severe drought in both 3- and 6-month timesteps.

The analysis of drought occurrences for differentcategories and time-steps indicates that northern andnorth-western parts of the country are most vulnerableto severe and very severe droughts. Moderate droughtoccurrences are higher in the southern part of the countrycompared to other parts. The central part of the studyarea has moderate potential for both moderate and severedroughts, but less potential for very severe droughts.

Critical rainfall analysis

Critical or threshold rainfall determines the minimummoisture input required for non-drought conditions invarious time steps. As the SPI values below zero indicatea deficit in rainfall, rainfall corresponding to zero SPIis considered the critical value in this present research(Sonmez et al., 2005). The critical rainfall values arecomputed for each station and then used to map its spatialdistribution. The distribution of rainfall required duringthe monsoon (June to November) and dry (Decemberto May) months in the study area for normal conditionsare shown in Figure 11a and b, respectively. The figureshows that a minimum of 1550 mm monsoon rainfallis required for normal conditions in the northern part ofBangladesh, which is one of the most drought-prone areasof the country. The north-western and central-westernparts require a minimum of 1250 mm monsoon rainfallfor normal conditions. Figure 11b shows that in the north-western part of the area, rainfall less than 225 mm duringthe months of December to May may cause a rainfalldeficit. The required rainfall varies from 225 mm to morethan 375 mm in the northern part of the country duringthis time period.

Average rainfall during rainy and dry months over thestudy area for the last 39 years is shown in Figure 12aand b, respectively. Figure 12a shows that average mon-soon rainfall varies between 1238 mm in the central-western part and more than 1551 mm in the northern andsouthern parts. The dry months rainfall varies between251 mm in the north-west and more than 426 mm in


2242 S. SHAHID

Figure 8. Moderate drought occurrences at (a) 3-month and (b) 6-month time steps

Figure 9. Severe drought occurrences at (a) 3-month and (b) 6-month time steps



Figure 10. Very severe drought occurrences at (a) 3-month and (b) 6-month time steps

Figure 11. Maps showing spatial distribution of minimum (a) monsoon rainfall; and (b) dry months rainfall required to avoid precipitation deficit inthe study area


2244 S. SHAHID

Figure 12. Maps showing spatial distribution of average (a) monsoon; and (b) dry months rainfall for the 39 years 1961–1999 over the study area

Figure 13. Maps showing the spatial distribution of rainfall reliability during (a) monsoon; (b) dry months in the study area

the eastern part of the area (Figure 12b). This is higherthan the minimum rainfall required for normal conditionin the area. However, reliability analysis of rainfall

during the monsoon months (Figure 13a) and dry months(Figure 13b) shows that the rainfall is highly variablein some parts of the area. The variation in monsoon



Tabl

eII

.T

rend

ofan

nual

mea

nra

infa

llin

diff

eren

tst

atio

ns

Bar

isha

lB

hola

Bog

raD

inaj

pur

Fari

dpur

Ishu

rdi

Jess

ore

Khe

pupa

raK

huln

aR

ajsh

ahi

Ran

gpur

Satk

hira

Are

alA

vera

ge

Lin

ear

Reg

ress

ion

�0Ð39

�1Ð79

0Ð12

2Ð14

�1Ð45

�1Ð88

0Ð04

3Ð49

�2Ð03

�0Ð49

1Ð44

�0Ð06

�0Ð04

Ken

dall-

Tau

�0Ð08

�0Ð2

0Ð06

0Ð28Ł

�0Ð16

�0Ð17

�0Ð02

0Ð38ŁŁ

�0Ð17

�0Ð11

0Ð3Ł

�0Ð04

0

ŁŁT

rend

issi

gnifi

cant

atth

e0Ð0

1le

vel

(2-t

aile

d).

Ł Tre

ndis

sign

ifica

ntat

the

0Ð05

leve

l(2

-tai

led)

.

rainfall is about 30% and in non-monsoon rainfall ismore than 50% of the average rainfall in north-westernand central-western parts of the area. The rainfall vari-ability in the northern part is comparatively less dur-ing dry months, but is high (between 28% and 30%)during the monsoon. As critical rainfall values in thenorth-western part of the area are very near to theaverage rainfall of the area and the year to year vari-ation of rainfall in the area is very high, it can beassumed that the area is very vulnerable to future severedroughts.

Annual trend of rainfall

The trend in annual rainfall over the study area iscalculated using a linear regression method to visualizethe future hydrological conditions in the area. Rainfalldata for the 39 years 1961–1999 is used for this purpose.As the time series of rainfall is not very long, theuse of linear regression to estimate time trends may bequestionable. Therefore, Kendall-tau trend estimation isalso used to compare with the result obtained from linearregression. The field significance of the time trends hasalso been assessed using a Mann–Kendall test. The resultobtained is shown in Table II. The study shows that thereis no significant change in annual areal rainfall in thestudy area. However, at local scale, significant increasein rainfall is observed at three stations, one is the coastalarea (Khepupara) and the other two are in the northernpart of Bangladesh (Rangpur and Dinajpur). Among theremaining 11 stations, 10 show a negative change inrainfall, but these are not statistically significant.

The spatial distribution of rainfall trend over the studyarea is shown in Figure 14. Plus (C) signs in the figuremean an increase in annual rainfall, minus signs (�)indicate a decrease in annual rainfall and zero (0) meansno observable change in annual rainfall during the timeperiod 1961–1999. The figure shows that rainfall hasdeclined in the central part of the study area. Maximumdeclination of rainfall is found to occur at a rate of�1Ð88 mm year�1 in Ishurdi near the central-westernpart of the study area. Between the two most droughtvulnerable areas, a significant increase in rainfall isobserved in the northern part and no change in rainfall isobserved in the north-western part of the study area.

Table III. El Nino years and drought years of Bangladesh

El Nino year Drought year

1962–63 19631965–66 1966, 19681972–73 19731977–78 1977, 19791982–83 19821987–88 19891991–93 19921994–95 1994–1995


2246 S. SHAHID

Tabl

eIV

.C

orre

lati

onof

the

ME

Ian

dpr

ecip

itat

ion

year

lym

eans

for

diff

eren

tst

atio

ns

Bar

isha

lB

hola

Bog

raD

inaj

pur

Fari

dpur

Ishu

rdi

Jess

ore

Khe

pupa

raK

huln

aR

ajsh

ahi

Ran

gpur

Satk

hira

Are

alA

vera

ge

Pear

son

�0Ð47

�0Ð43

�0Ð12

�0Ð44

�0Ð48

�0Ð22

�0Ð07

0Ð05

�0Ð22

�0Ð13

�0Ð10

0Ð03

�0Ð28

Spea

rman

�0Ð41

�0Ð39

�0Ð10

�0Ð38

�0Ð42

�0Ð28

�0Ð02

0Ð01

�0Ð27

�0Ð13

�0Ð11

0Ð03

�0Ð22

Ł Num

bers

inbo

ldm

eans

stat

istic

ally

sign

ifica

ntat

the

0Ð05

leve

l. Tabl

eV

.C

orre

latio

nof

the

ME

Ian

dSP

Iye

arly

mea

nsfo

rdi

ffer

ent

stat

ions

Bar

isha

lB

hola

Bog

raD

inaj

pur

Fari

dpur

Ishu

rdi

Jess

ore

Khe

pupa

raK

huln

aR

ajsh

ahi

Ran

gpur

Satk

hira

Pear

son

�0Ð11

�0Ð02

0Ð06

�0Ð42

�0Ð47

�0Ð03

0Ð28

0Ð14

�0Ð09

�0Ð05

�0Ð31

0Ð14

Spea

rman

�0Ð12

�0Ð20

�0Ð04

�0Ð40

�0Ð42

�0Ð06

0Ð16

0Ð13

�0Ð07

�0Ð05

�0Ð16

0Ð15

Ł Num

bers

inbo

ldm

eans

stat

istic

ally

sign

ifica

ntat

the

0Ð05

leve

l.

Drought and ENSO phenomena relationship

A relationship between drought in Bangladesh and ElNino has been observed. El Nino years and drought yearsin Bangladesh are compared in Table III. The relationshipbetween the Multivariate ENSO Index (MEI) and rainfalland SPI in the study area is estimated using a two-sided Pearson and Spearman correlation test. Pearsonand Spearman correlation coefficients of yearly meansof Multivariate ENSO Index (MEI) with yearly means ofprecipitation are �0Ð28 and �0Ð22, respectively, for thestudy area. However, the correlations are not statisticallysignificant. The negative correlation between MEI andyearly means of precipitation suggests that precipitationdecreases when MEI increases. MEI is also correlatedwith precipitation at each station in the study area.Significant negative correlation between precipitation andMEI is found in three stations. Table IV shows thecorrelation matrix of the MEI and precipitation yearlymeans for each station.

Correlation between MEI and SPI for the study areais also investigated. No significant correlation betweenyearly means of MEI and SPI is found for the study area.At local scale statistically significant negative correlationbetween yearly means of MEI and SPI is found in twostations. The correlation matrix of the MEI and SPI yearlymeans for each station is shown in Table V.

CONCLUSIONS

The spatial and temporal characteristics of meteorolog-ical droughts in the western part of Bangladesh havebeen studied by reconstructing historical occurrences ofdrought for multiple time steps and drought categoriesby employing an SPI approach. The major outcome ofthe study is the production of a drought potential map ofthe western part of Bangladesh. Drought potential map-ping is one of the major steps in drought mitigation andplanning. The study reveals that north and north-westernareas are most likely to suffer drought. It is observed thatthere is no relation between the rainfall distribution anddrought potential zones. The northern region normallyreceives more than the average rainfall of the study area,but the area has a higher potential for drought. The max-imum rainfall demand in this area is also high comparedto other drought-prone areas. It is hoped that the studywill assist in guiding the operational responses in droughtrisk reduction in Bangladesh.

A significant negative relation between precipitationdeficit and MEI is found for four stations in thestudy area. Significant negative relation is also observedbetween SPI and MEI at two stations. Therefore, infor-mation on the ENSO could be useful for Bangladesh fordrought management. The trend analysis in annual meanrainfall in the western part of Bangladesh shows that thereis no change in average precipitation. Rainfall is found tochange significantly at local scale at some stations. Withan average mean annual rainfall very near to the criticalrainfall, high rainfall variability and no significant change



Figure 14. Map showing the spatial distribution of rainfall trend com-puted from rainfall data for the 39 years 1961–1999

in annual rainfall, the north-western part of Bangladeshis expected to experience more severe droughts in thefuture.

REFERENCES

Banerji S, Chabra BM. 1964. Drought characteristics and estimatingprobabilities of their occurrences. Surfacewaters symposium , Publica-tion No. 63, 189–192, WMO/IASH: Belgium.

Banglapedia 2003. National Encyclopaedia of Bangladesh, Islam S (ed).Asiatic Society of Bangladesh: Dhaka.

Bhalme HN, Mooley DA. 1980. Large scale droughts/floods andmonsoon circulation. Monthly Weather Review , 108: 1197–1211.

Bishop CM. 1995. Neural Networks for Pattern Recognition. OxfordUniversity Press: Oxford.

Byun HR, Wilhite DA. 1999. Objective quantification of drought severityand duration. Journal of Climate 12: 2747–2756.

Creutin JD, Obled C. 1982. Objective analyses and mapping techniquesfor rainfall fields: an objective comparison. Water Resources Research18: 413–431.

Dracup JA, Lee KS, Paulson EG Jr. 1980. On the definition of droughts.Water Resources Research 16: 297–302.

ESRI 2004. ArcMap 9Ð1 . Environmental Systems Research Institute:Redlands.

Essenwanger OM. 2001. General Climatology: Classification of Cli-mates . Elsevier: The Netherlands.

Gibbs WJ, Maher JV. 1967. Rainfall deciles as drought indicators .Australian Bureau of Meteorology Bulletin 48, Commonwealth ofAustralia: Melbourne.

Goodall JL, Maidment DR, Sorenson J. 2004. Representation of spatialand temporal data in ArcGIS. AWRA GIS and Water ResourcesConference III, Nashville, TN.

Guttman NB. 1998. Comparing the Palmer Drought Index and theStandardized Precipitation Index. Journal of the American WaterResources Association 34: 113–121.

Guttman NB. 1999. Accepting the Standardized Precipitation. Journal ofAmerican. Water Resources Association 35: 311–322.

Hayes MJ, Svoboda MD, Wilhite DA Vanyarkho OV 1999. Monitoringthe 1996 drought using the Standardized Precipitation Index. BAMS80: 429–438.

Haykin S. 1994. Neural Networks: A Comprehensive Foundation.Macmillan: New York.

Isaaks HE, Srivastava RM. 1989. An Introduction to Applied Geo-statisitics . Oxford University Press: New York.

Journel AG, Huijbregts CJ. 1981. Mining Geostatistics . Academic Press:New York.

Kuligowski RJ, Barros PA. 1998. Using artificial neural networks toestimate missing rainfall data. Journal of the American Water ResourcesAssociation 34: 1437–1447.

Lloyd-Hughes B, Saunders MA. 2002. A drought climatology for Europe.International Journal of Climatology 22: 1571–1592.

McKee TB, Doesken NJ, Kleist J. 1993. The relationship of droughtfrequency and duration to time scales. 8th Conference on AppliedClimatology, 17–22 January, Anaheim, CA; 179–184.

McKee TB, Doesken NJ, Kleist J. 1995. Drought monitoring withmultiple time scales. 9th Conference on Applied Climatology, Dallas,TX; 233–236.

McKee TB, Doesken NJ, Kleist J. 1993. The relationship of droughtfrequency and duration to time scales. Eighth Conference on AppliedClimatology , American Meteorological Society: Jan. 17–23, AnaheimCA; 179–186.

Paul BK. 1998. Coping mechanisms practised by drought victims(1994/5) in North Bengal, Bangladesh. Applied Geography 18:355–373.

Rahman A, Biswas PR. 1995. Devours resources. Dhaka Courier 11(42):7–8.

Rashid HE. 1991. Geography of Bangladesh . University Press Ltd:Dhaka; 579.

Rumelhart DE, Mclelland JL. 1986. Parallel Distributed Processing .MIT Press: Cambridge, MA.

Schneider T. 2001. Analysis of incomplete climate data: estimation ofmean values and covariance matrices and imputation of missing values.Journal of Climate 14: 853–871.

Seo DJ, Krajewski WF, Bowles DS. 1990. Stochastic interpolation ofrainfall data from rain gages and radar using cokriging—1. Designof experiments. Water Resources Research 26: 469–477.

Shahid S, Chen X, Hazarika MK. 2005. Assessment aridity ofBangladesh using geographic information system. GIS Development9(12): 40–43.

Shahid S, Nath SK, Roy J. 2000. Ground water potential modelling in asoftrock area using GIS. International Journal of Remote Sensing 21:1919–1924.

Sonmez FK, Komuscu AU, Erkan A, Turgu E. 2005. An analysis ofspatial and temporal dimension of drought vulnerability in Turkeyusing the Standardized Precipitation Index. Natural Hazards 35:243–264.

Teegavarapu RSV, Chandramouli V. 2005. Improved weighting methods,deterministic and stochastic data-driven models for estimation ofmissing precipitation records. Journal of Hydrology 312(1–4):191–206.

van Beers WCM, Kleijnen JPC. 2004. Kriging interpolation insimulation: a survey. Proceedings of the 2004 Winter SimulationConference, Ingalls RG, Rossetti MD, Smith JS, Peters BA (eds),Washington, DC, December.

Wilhite DA, Glantz MH. 1985. Understanding the drought phenomenon:the role of definitions. Water International 10(3): 111–120.

Wilhite DA, Glantz MH. 1987. In Understanding the DroughtPhenomenon: the Role of Definitions . Wilhite DA, Easterling WE,Wood DA (eds). Planning for Drought. Vestview Press: Boulder CO;11–27.


drought characteristics of bangladesh

Documents