spatio-temporal variation of agricultural drought in the barind region of bangladesh: an application...

IRRIGATION AND DRAINAGE

Irrig. and Drain. (2013)

Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ird.1800

SPATIO-TEMPORAL VARIATION OF AGRICULTURAL DROUGHT IN THE BARINDREGION OF BANGLADESH: AN APPLICATION OF A MARKOV CHAIN MODEL†

A. T. M. JAHANGIR ALAM1*, A. H. M. SAADAT1, M. SAYEDUR RAHMAN2 AND SHAHRIAR RAHMAN3

1Department of Environmental Sciences, Jahangirnagar University, Dhaka, Bangladesh2Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh

3Faculty of Geo-information and Earth Observation (ITC), University of Twente, Enschede, the Netherlands

ABSTRACT

The Barind region of Bangladesh is severely affected by agricultural drought. A geostatistical approach had been conducted tosummarize the spatio-temporal variation of agricultural drought in this region. A Markov chain model of higher order has beenused to evaluate probabilities of getting a sequence of wet–dry weeks over this region from the rainfall data recorded in 12rainfall gauge stations for the period 1971–2008. A drought index (DI) considering crucial parameters (DI = 0 ~ 1.00) has beenused to estimate the severity of agricultural drought. Geospatial analysis has been conducted to delineate the spatial extent ofagricultural drought of different severities in different seasons. The probability of three consecutive dry weeks and probabilityof at least 10 and 12weeks was also calculated to find out the suitability of agricultural production. The maximum variation ofagricultural drought index (DI = 0.12 ~ 0.43) was found during the pre-kharif (March to May) and kharif (June to October)(DI = 0.47 ~ 0.81) seasons. However, no variation in drought index (DI = 0.01 ~ 0.03) was found during the rabi (Novemberto February) season. The results of this study might be useful to agricultural planners and irrigation engineers in identifyingareas where agricultural development should be focused as a long-term drought mitigation strategy. Copyright © 2013 JohnWiley & Sons, Ltd.

key words: agricultural drought index; Markov chain model; rainfall probability; GIS; spatio-temporal analysis

Received 12 March 2013; Revised 11 June 2013; Accepted 18 July 2013

RÉSUMÉ

Barind est une région du Bangladesh gravement touchée par la sécheresse agricole. Une approche géostatistique a été effectuéepour décrire la variation spatio-temporelle de la sécheresse agricole dans cette région. Un modèle à chaîne de Markov d’ordresupérieur a été utilisé pour évaluer les probabilités d’obtenir une séquence de semaines humide–sèche sur cette région à partirdes données pluviométriques enregistrées dans 12 stations pluviométriques pour la période de 1971 à 2008. L’indice desécheresse (DI) compte tenu des paramètres cruciaux (DI = 0 ~ 1.00) a été utilisé pour estimer l’intensité de la sécheresseagricole. L’analyse géospatiale a été menée afin de délimiter l’étendue spatiale de la sécheresse agricole pour différents niveauxd’intensité dans les différentes saisons. La probabilité de trois semaines consécutives de sécheresse et la probabilité d’au moinsdix et douze semaines ont également été calculées pour cerner l’adéquation de la production agricole aux conditions futures desécheresse. La variation maximale de l’indice de sécheresse agricole (DI = 0.12 ~ 0.43) a été observée lors des saisons dupré-kharif (mars à mai) et du kharif (juin à octobre) (DI = 0.47 ~ 0.81). Cependant, aucune variation de l’indice desécheresse (DI = 0.01 ~ 0.03) n’a été observée au cours de la saison de rabi (novembre à février). Les résultats de cetteétude pourraient être utiles aux planificateurs agricoles et aux ingénieurs d’irrigation pour identifier les zones où ledéveloppement agricole passe par une stratégie à long terme d’atténuation de la sécheresse. Copyright © 2013 JohnWiley & Sons, Ltd.

mots clés: indice agricole de sécheresse; modèle à chaîne de Markov; probabilités de précipitations; SIG; analyse spatio-temporelle

*Correspondence to: A.T.M. Jahangir Alam, Department of Environmental Sciences, Jahangirnagar University, Dhaka-1342, Bangladesh. E-mail:[email protected]†Variations spatio-temporelles de la sécheresse agricole dans la région de Barind au Bangladesh: une application d’un modèle à Chaîne de Markov.

Copyright © 2013 John Wiley & Sons, Ltd.

A. T. M. J. ALAM ET AL.

INTRODUCTION

Agriculture is the single most and largest productive sectorof Bangladesh and it contributes about 33% of nationalGDP and employs around 60% of the total labour force(Shahid, 2010). Drought affects agricultural production inBangladesh in all seasons. According to Rahman (2000),during the kharif season about 0.574–1.748 million ha ofT-aman rice crops are severely affected by drought everyyear. In 1995, production of rice and wheat decreased byabout 3.5 × 106 tons in Bangladesh (Rahman and Biswas,1995; Shahid, 2008). Drought is also responsible for othercash crop damage, such as in jute, and other crops, pulses,potatoes, oilseeds, minor grains, winter vegetables and sugarcane. Bangladesh has been affected by nine major droughtssince 1971 (Paul, 1998; Shahid, 2008) and most of theevents occurred in the Barind region (Ramsey et al., 2007;Shahid, 2008; Saadat et al., 2009). Agricultural drought isa dynamic process and changes over space and time; there-fore it is necessary to understand the spatial extent and tem-poral variability of agricultural drought in relation to climatechange to assess the drought risk (Razie et al., 2010). Thisresearch was to define the extent of agricultural drought inthe Barind region of Bangladesh in relation to long-termrainfall data.

To delineate the extent of agricultural drought, indicatorssuch as precipitation, temperature, evaporation, stream flow,groundwater levels, reservoir and lake levels, snow pack,soil moisture, etc. are usually considered. The moisture-holding capacity of the soil of the Barind Region is verylow (Karim et al., 1990), and it also seems to have greaterevapotranspiration than precipitation for 7–8months of theyear. In addition, more than 85% of annual rainfall occursduring the monsoon (Barkotulla, 2007). Agricultural activi-ties are influenced by rainfall in the Barind region, henceprecipitation is considered the indicator of agriculturaldrought in this research work (Banik et al., 2002).

Several studies had already been conducted consideringdrought indices for modelling drought, such as the percentageof normal index (Banerji and Chabra, 1964), precipitationdeciles index (Gibbs and Maher, 1967), Bhalme–Mooleydrought index (Bhalme and Mooley, 1980), standardizedprecipitation index (McKee et al., 1993), effective droughtindex (Byun andWilihite, 1999),Markov chain model (Gabrieland Neumann, 1962; James and Caskey, 1963; Rahman 1999a,1999b; Banik et al., 2002) etc. Among these studies thereliability of meteorological persistence can be best describedthrough a Markov chain model of proper order (Rahman,1999a, 1999b). Moreover, the Markov chain model is foundto be promising in simulating the length of the longest dryand wet spells and largest rainfall amounts during themonsoon (Sharma, 1996; Biamah et al., 2005). Therefore,the Markov chain model was used to study spatial and


temporal variation of agricultural drought in the Barindregion of Bangladesh. However, there is still bewilder-ment among scientists about the choice of proper orderof the Markov chain model. Dastidar et al. (2010) statethat occurrence of precipitation can be best described bytwo state Markov chains of second and third order byanalysing Akai information criteria (AIC) and Bayesianinformation criteria (BIC). On the other hand, Harrisonand Waylen (2000) found that blind acceptance of theresults of an AIC: BIC-type analysis can lead to signif-icant underestimation of variance of the original data,and thereby lead to erroneous conclusions regardingthe nature of precipitation in an area. To overcome thiscontradiction, the higher order of Markov chain modelcalculated until it reached in constant state. The probabilityof getting three consecutive dry weeks and of getting atleast ten to twelve wet weeks were studied to demarcatethe suitability of agricultural production and to summarizethe prediction of agricultural drought in the Barind regionof Bangladesh.

MATERIALS AND METHODS

Climate of study area

The Barind region is the driest part of the country with aver-age annual rainfall between 1300 and 1400mm (Figure 1a).The region is characterized by highly fluctuating rainfall andthe ratio of dry to rainy months is the highest in Bangladesh.Although the whole region has a long cool winter, themaximum number of days in summer is observed to havea temperature above 40 °C (Rahman, 1999a).

Rainfall data

Daily rainfall data from 1971–2008 of 12 rainfall stationsof the Barind region were collected from the BangladeshWater Development Board (BWDB). Figure 1(b) repre-sents the location of rainfall stations in the study area.Daily rainfall data were converted to weekly rainfall data.The choice of threshold value for the Markov chain modelis very important, especially when it is used for agricul-tural purposes. A small amount of water in dry regionsmay be very useful, whereas the same amount may be in-significant in humid regions. A week with rainfall greaterthan the threshold value (a minimum amount, say 2.5mm)is considered to be a wet week (Banik et al., 2002). Theexpected number of wet weeks in a given period oftime can decide the crop production for an area. The prob-ability of sequences of wet weeks indicates the adequacyof water and that of dry weeks indicates the risk ofcrop failure.


Figure 1. (a) Annual average rainfall of the study area, (b) location of rainfall stations in the study area

SPATIO-TEMPORAL VARIATION OF AGRICULTURAL DROUGHT

Calculation of the Markov chain model

Let X0, X1, X2,……………, Xn, be random variables distrib-uted identically and taking only two values, namely 0 and 1,with probability one, i.e.

P Xð

w

Copy

Xn ¼0 if the nth week is dry

1 if the nth week is wet

�

Firstly, it may be assumed that

nþ1 ¼ xnþ1jXn ¼ xn;Xn�1 ¼ xn�1;………;X0 ¼ x0Þ¼ PðXnþ1 ¼ xnþ1jXn ¼ xnÞ

herex0; x1;…………:; xnþ1∈ 0; 1f g(1)

In other words, it is assumed that the probability ofwetness of any week depends only on whether the previousweek was wet or dry. Given the event on the previous week,the probability of wetness is assumed to be independentof further preceding weeks. So, the stochastic process{Xn, n = 0, 1, 2…} is a Markov chain (Medhi, 1994).

Considering the transition matrix as

Pij ¼P00 P01

P10 P11

� �(2)

where Pij=P (X1 = j∣X0 = i) i, j = 0,1. Note P00 +P01 = 1 andP10 +P11 = 1.

For a higher-order Markov chain the nth-order transitionmatrix is: Pij

k where k= 1, 2…n. In the present study the

right © 2013 John Wiley & Sons, Ltd.

transition matrix was calculated until it was reached in thestable condition.

Pijk ¼ Pij

kþ1 (3)

where k = 1, 2…n.Let p =P (X0 = 1). Here p is the absolute probability of a

week being wet. Clearly, P (X0 = 0) = 1� p.For a stationary distribution

1� P P½ � P00

P10

P01

P11

" #¼ 1� P P½ � (4)

which gives

p ¼ P01

1� P11 � P01ð Þ (5)

It is further assumed that Pij is the remaining constantover the years. The maximum likelihood estimates of P01

and P11 are appropriate relative functions.For a second-order Markov chain:

p ¼ P10P100 þ P11P10

1� P00P000 � P10P100 þ P01P010 � P11P110ð Þ(6)

The expressions for the chain of third and higher ordersare not being presented because of their complicated nature.

A wet spell of length k is defined as sequences of k wetweeks preceded and following by weeks. Dry spells are


2001–

2008

0.01

0.01

0.02

0.01

0.01

0.01

0.01

0.01

0.01

0.02

0.01

0.01


defined correspondingly. By ‘probability of wet spell oflength k’ we mean the probability of a wet spell of lengthk given that this week is wet, i.e.

Table

Crite

0.0000.1250.180.2350.310

1991–

2000

0.02

0.02

0.01

0.01

0.02

0.01

0.02

0.01

0.01

0.02

0.02

0.02

Copy

P W ¼ kð Þ ¼ 1� P11ð ÞP11k�1

(7)

bi 1– 0 1 2 1 1 1 1 2 2 2 1 0 2

and probability of wet sequences with length greater thank is

Ra

1– 0198

199

10.0

30.0

00.0

20.0

10.0

10.0

40.0

10.0

20.0

10.0

10.0

30.0

P W > kð Þ ¼ ∑∞

t¼kþ1P W ¼ tð Þ ¼ P11

k: (8)

197

198

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

Similarly, probability of a dry spell of length m is

1– 8 1 1 1 2 1 1 1 1 1 1 1 2

P D ¼ mð Þ ¼ 1� P01ð ÞP01m�1 (9)

197

200

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

and probability of dry sequences with length greater thanm is

2001–

2008

0.73

0.76

0.75

0.75

0.74

0.74

0.81

0.73

0.77

0.75

0.74

0.68
P D > mð Þ ¼ 1� P01ð Þm (10)
arind,

Bangladesh

Kharif

71–

801981

–1990

1991–

2000

50.58

0.58

00.53

0.71

70.50

0.57

40.64

0.60

60.63

0.66

00.64

0.61

80.66

0.72

20.62

0.56

10.71

0.64

30.62

0.69

10.60

0.57

70.66

0.54

Index of drought proneness

P11 gives the probability of a week being wet given that theprevious week was also wet. When P11 is large, the chanceof wet weeks is also large. But only a small value of P11 maynot indicate high drought proneness. In this case, a largevalue of P01 implies a large number of short wet spellswhich can prevent the occurrence of drought. Hence, anindex of drought proneness may be defined as

sin

B 19 19 0.6

0.7

0.4

0.7

0.6

0.6

0.7

0.6

0.7

0.6

0.6

0.7

DI ¼ P11 � P01 (11)

ofdroughtindexin

differentperiodsandseason

Pre-kharif

1971–

1980

1981–

1990

1991

–2000

2001

–2008

1971–

2008

0.24

0.22

0.27

0.31

0.64

0.20

0.49

0.26

0.43

0.70

0.15

0.22

0.16

0.28

0.56

0.23

0.26

0.21

0.34

0.68

0.23

0.31

0.33

0.38

0.67

0.15

0.22

0.23

0.29

0.65

0.28

0.29

0.27

0.31

0.74

0.12

0.14

0.18

0.33

0.63

0.23

0.36

0.22

0.28

0.71

0.16

0.22

0.19

0.27

0.67

0.26

0.22

0.27

0.33

0.62

0.24

0.24

0.18

0.29

0.66

This index of drought proneness is bounded by zero andone. The higher the value of DI, the lower will be the degreeof drought proneness. To get a more accurate result thecalculation of P11 and P01 is continued up to they reachedat the constant state. The extent of drought proneness isgiven in Table I (Banik et al., 2002).

Spatial interpolation

A kriging interpolation technique was used for mapping thespatial extent of agricultural droughts. All the geostatisticalanalysis was conducted in a well-known GIS platform(ArcGIS 9.2). Geostatistics based on the theory of regional-ized variables is increasingly preferred because it allows thecapitalization of spatial correlation between neighbouring

I. Index of drought-proneness

ria Degree of drought-proneness

≤ DI ≤ 0.125 Chronic< DI ≤ 0.180 Severe0< DI ≤ 0.235 Moderate< DI ≤ 0.310 Mild< DI ≤ 1.000 Occasional

Table

II.Tem

poraldistributio

n

Statio

ns1971

–2008

Chapainaw

abganj

0.26

Godagari

0.27

Nachole

0.19

Nith

pur

0.26

Badalgachi

0.23

Manda

0.21

Mohadevpur

0.28

Bholahat

0.18

Nazirpur

0.27

Shibganj

0.20

Tanore

0.26

Rohanpur

0.23

right © 2013 John Wiley & Sons, Ltd. Irrig. and Drain. (2013)

Figure 2. Spatial extent of agricultural drought during 1971–2008: (a) pre-kharif season, (b) kharif season and (c) rabi season. (Maps were prepared using thekriging method.)


observations to predict attribute values where samples areabsent (Goovaerts, 2000). Kriging is a stochastic interpola-tion method that takes into account distances betweenobserved and estimated points (Delhomme, 1978). Severalstudies (Phillips et al., 1992; Haberlandt, 2007; Attorreet al., 2007) have shown that using kriging for interpolationprovides more reliable results than almost all other methods.However, surface interpolation using kriging depends on theselected semi-variogram model, and the semi-variogrammust be fitted with a mathematical function or model; differ-ent semi-variogram models such as circular, spherical,Gaussian and exponential were used for best fitting. Detailsof the geostatistical analysis and semi-variogram modellingare available in Johnston et al. (2001). In this research workthe maps were produced using the ordinary kriging method.

Kriging assumes that the distance or direction betweensample points reflects a spatial correlation that can be usedto explain variation in the surface. The kriging tool fits amathematical function to a specified number of points, orall points within a specified radius, to determine the outputvalue for each location. Kriging is a multi-step process; itincludes exploratory statistical analysis of the data. Thegeneral formula for kriging interpolators is formed as aweighted sum of the data:

Copy

Z s0ð Þ ¼ ∑Ni¼1λiZ sið Þ (12)

where

Z s0ð Þ ¼

right © 2

the value we are trying to predict for location s0
N = the number of measured sample points
surrounding the prediction location that will beused in the prediction

013 John Wiley & Sons, Ltd.

λi =
are the weights assigned to each measured pointthat we are going to use. These weights willdecrease with distance
Z(si) =
is the observed value at the location si
RESULTS AND DISCUSSION

The occurrence and extent of agricultural drought in thestudy area have been identified using a higher-order Markovchain model in multiple time scales. Pre-kharif droughtduration was considered to be from March to May, kharifdrought duration from June to October and rabi droughtduration from November to February. About 13, 21 and 18standard weeks were considered for the pre-kharif, kharifand rabi seasons respectively.

Spatio-temporal distribution of agricultural drought

The spatial characteristics of agricultural drought in thestudy area showed that during the 1971–2008 period in thepre-kharif season 28% of the study area was affected bymoderate drought and the rest of the area was exaggeratedby mild drought. At this time the highest drought indexwas found at Mohadevpur station (DI = 0.28) and the lowestat Bholahat station (DI = 0.18) (Table II). In the kharif andrabi seasons, the whole study area was affected by occa-sional and chronic drought respectively (Figure 2). Duringthe kharif season the drought index was highest atMohadevpur station (DI = 0.74) and the lowest at Nacholestation (DI = 0.56) (Table II). In the rabi season the rangeof drought index was between DI = 0.01 and DI = 0.02(Table II). In the chi-square test these results also rejectedthe value of the null hypothesis in 1.d.f at the 5% level.




Significant variation in the drought index was found inthe pre-kharif season when considering a 10-yr periodinstead of the total period from 1971 to 2008. As indi-cated in Figure 3, nearly 13% of the study area was af-fected by severe drought, whereas 72 and 15% of thearea was affected by moderate and mild drought respec-tively during 1971–1980. In the case of the kharif andrabi seasons the results remained the same. During1971–1980 the lowest drought index was found atNachole, being 0.15 and 0.47 in the pre-kharif and kharifseasons respectively. During rabi seasons five stations,namely Chapainawabganj, Manda, Bholahat, Badalgachiand Shibganj, had the same drought index of 0.01, andthe highest drought index of 0.04 was found atMohadevpur station. Throughout 1971–1980 all the

Figure 4. Spatial extent of agricultural drought during 1981–1990: (a) pre-kharif skriging met


results rejected the null hypothesis in 1.d.f at the 5%level in the chi-square test except for Nachole station inthe rabi season.

The similar types of drought index was observed duringthe other ten years period i.e. 1981–1990, 1991–2000 and2001–2008 (Figures 4(b), 5(b) and 6(b) respectively).During the 1991–2000 period the lowest drought indexwas found at Rohanpur station (DI = 0.54). During the rabiseason, for all time periods the study area was found to beaffected by chronic drought and the drought index in thisseason during the 10-yr years period ranges from DI = 0.01to DI = 0.02 (Table II).

As presented in Figure 4(a), in the pre-kharif seasonaround 80% of the study area was affected by droughtduring 1981–1990, of which 5, 30 and 41% of the area

eason, (b) kharif season and (c) rabi season. (Maps were prepared using thehod.)





was found to be affected by severe, moderate and milddrought respectively. During 1991–2000 in the pre-kharifseason, around 12, 35 and 51% of the study area wasaffected by severe, moderate and mild drought (Figure 5(a)). From 2001 to 2008, about 49% of the study area wasaffected by severe drought and rest of the study area wasprone to occasional drought in the pre-kharif season. Duringboth of these time frames in the pre-kharif season all the re-sults rejected the null hypothesis in 1.d.f at the 5% level inthe chi-square test.

For a 10-yr period, in the kharif seasons all the resultsrejected the null hypothesis in 1.d.f. at the 5% level, exceptBholahat station during 2001–2008. For the rabi season, outof 60 calculations only 4 did not reject the null hypothesisin1.d.f. at the 5% level in the chi-square test.


Drought potential zones

The area potentially liable to suffer agricultural drought atdifferent seasons was identified on the basis of their occur-rences. The percentage of agricultural drought occurrenceswas computed by taking the ratio of drought occurrencesin each season to the total number of years considered inthe same season and drought category (McKee et al., 1993;Shahid, 2008). The percentages of occurrences of chronic,severe and moderate agricultural droughts are represented inFigures 7, 8 and 9 respectively.

The spatio-temporal analysis of chronic drought occur-rence in the pre-kharif season (Figure 7(a)) identified thefrequent impact of chronic drought in the western part ofthe study area, while the north-eastern part was found to


(a) (b)

Figure 8. Severe drought occurrences in percentages: (a) pre-kharif season, (b) kharif season

Figure 7. Chronic drought occurrences in percentages: (a) pre-kharif season, (b) kharif season


be less frequently affected. The central part experiencedmedium frequencies of chronic drought. The figure alsoidentified the western part of the study area suffering fromchronic drought once in every four years. In the kharifseason, the central western part of the study area was signif-icantly affected by agricultural drought and most of thestudy area was found to be a potential for chronic droughtwith lower frequencies (Figure 7(b)).

The distribution of severe agricultural droughts (Figure 8(a))showed a different pattern than the chronic drought. The


south-western corner and central eastern part of the study areaexperienced severe drought of higher frequency in the pre-kharifseason.When the occurrences of severe agricultural drought hadbeen considered for the kharif season (Figure 8(b)), the areawith potentially high occurrences was found to be expandedin the south-western part. This part of the study area may beaffected by severe drought once in every 20 yr.

Moderate drought in the pre-kharif season tends to occur inthe central western part with high percentages (Figure 9(a)).The high drought potential zones were found in the


Figure 9. Moderate drought occurrences in percentages: (a) pre-kharif season, (b) kharif season

Figure 11. Probability of getting at least 10 wet weeks


north- western part of the study area. It was observed that thefrequency of moderate drought was higher in the south-western part of the study area than any other parts duringkharif season (Figure 9b). The figure also assumed that thesouth-western part of Barind region affected by moderatedrought once in every twelve to thirteen years. However,moderate drought probability was found to be lowest in thecentral and northern part of the high Barind tract.

The analysis of agricultural drought occurrences fordifferent categories and different seasons indicated that thewestern part of the study area was more vulnerable to chronicand severe drought in both the pre-kharif and kharif seasons.However, severe drought potential in the pre-kharif seasonis located in the eastern part, and moderate drought potentialis identified in the central western part of the study area.

Suitability of agricultural production in theBarind region

About 10–12 wet weeks are necessary in order to harvest agood crop and a continuous span of at least three dry weeks

Figure 10. Probability of three consecutive dry weeks


between the wet weeks would be responsible for total cropfailure (Banik et al., 2002). Hence the probability ofsequences of more than three dry weeks was computedusing Equation (10) and the probability of getting 10 and12 wet weeks was calculated using Equation (8) by consid-ering the higher-order constant value of P01 P11. The results

Figure 12. Probability of getting at least 12 wet weeks



of the probability of three consecutive dry weeks is shown inFigure 10 and the probability of getting 10 and 12 wetweeks is shown in Figures 11 and 12 respectively.

The probability of getting three consecutive dry weekswas higher in the rabi season and lower in the kharif season.During the pre-kharif season the probability of gettingthree consecutive dry weeks varies greatly. The highestprobability (p =D> 3) of three consecutive dry weeksduring the pre-kharif season was found at the Bholahatstation (p =D> 3 = 0.19) and lowest was at the Godagaristation (p =D> 3 = 0.11). In the case of the kharif seasonthe probability of getting three consecutive dry weeksranges between (p =D> 3 = 0.01) to (p =D . 3 = 0.02). Forthe rabi season this probability of three consecutive dryweeks ranges between (p=D> 3= 0.66) to (p=D> 3= 0.72)from Rohanpur to Nachole and Bholahat staions. Figures 11and 12 indicate that the probability of getting at least 10 and12 wet weeks was higher during the kharif season. But theseprobabilities during the rabi and pre-kharif seasons showalmost similar results. The probability of getting at least 10and 12 wet weeks was almost zero in the rabi and pre-kharifseasons (Figure 12).

CONCLUSION

The drought index of the study area was calculated in threedifferent crop seasons for long- as well as for short-termperiods. The agricultural drought condition of the study areawas most devastating in the rabi season and was suitablefor agricultural practice only in the kharif season. In thepre-kharif season the drought condition was totallyunpredictable. The long-term probability calculation alsosuggested that the area may potentially suffer future chronicand severe drought. The result of the study can be replicatedconsidering crop diversity and various crop growth periodsin different seasons under the prevailing agro-climatic condi-tions in the Barind region. The outcome of this study willbe helpful for agricultural planners and irrigation engineersin operational responses in agricultural drought risk reduc-tion in drought-prone areas of Bangladesh.

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spatio-temporal variation of agricultural drought in the barind region of bangladesh: an application...

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