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HYDROLOGICAL PROCESSES 22, 2235–2247 (2008) Hydrol. Process. 22, Published Published online 19 November November 2007 in Wiley Wiley InterScien InterScience ce (www.interscience.wiley.com) DOI: 10.1002/hyp.6820
Spatial and temporal characteristics of droughts in the western part of Bangladesh Shamsuddin Shahid* Department of Geography, Rhodes University, Grahamstown 6140, South Africa
Abstract: Spatia Spatiall and tempor temporal al characte characteris ristic ticss of drough droughts ts in the wester western n part part of Bangla Banglades desh h have have been analyse analysed. d. Standa Standardi rdized zed precipitation index method is used to compute the severity of droughts from the rainfall data recorded in 12 rainfall gauge statio stations ns for the period period of 1961– 1961– 1999. 1999. An artiﬁc artiﬁcial ial neural neural networ network k is used used to estimat estimatee missin missing g rainfa rainfall ll data. data. Geograp Geographic hic Information System (GIS) is used to map the spatial extent of droughts of different severities in multiple time scales. Critical analysis of rainfall is also carried to ﬁnd the minimum monsoon and dry months rainfall require in different parts of the study area to avoid rainfall deﬁcit. The study shows that the north and north-western parts of Bangladesh are most vulnerable to droughts. droughts. A signiﬁcant signiﬁcant negative relationship relationship between multiple multiple ENSO index and rainfall rainfall is observed observed in some stations. stations. Analysis of seasonal rainfall distribution, rainfall reliability and long-term rainfall trend is also conducted to aid prediction of future droughts in the area. Copyright 2007 John Wiley & Sons, Ltd. KEY WORDS
Droug Drought ht is a dynam dynamic ic phenom phenomeno enon, n, which which change changess over over time time and space. space. Theref Therefore ore,, comple complete te analys analysis is of Droughts are recurrent phenomena in the western part of drought requires study of its spatial and temporal extents. Bangladesh. Since independence in 1971, the country has Hydrologi ogical cal invest investiga igatio tion n over over a large large area area requir requires es suffered from nine droughts of major magnitude (Paul, Hydrol assimilation of information from many sites, each with a 1998). The impact of droughts was higher in the west2000). GeoGeoern part of the country compared to other parts. In recent unique geographic location (Shahid et al., 2000). decades, the hydro-climatic environment of north-western graphic Information System (GIS) maintains the spatial Bangladesh has been aggravated by environmental degra- location of sampling points, and provides tools to relate dation dation and crosscross- country country anthropog anthropogenic enic interven interventions tions the sampling data through a relational database. There(Banglapedia, 2003). Scientists have become increasingly fore, it can be used effectively for the analysis of spatially concerne concerned d about about the frequent occurrence occurrence of drought drought in distributed hydro-meteorological data and modelling. In western western districts districts of Bangladesh Bangladesh,, and this paper paper reports reports the present paper, GIS is used for the spatial modelling on studies of drought conditions in the western part of of droughts in western Bangladesh at various time-scales. The common indicator indicatorss of drought drought include include meteorometeoroBangladesh. logical logic al variables varia bles such as precipit prec ipitation ation and evaporatio evapo ration, n, Although droughts may occur at any time of the year, well as hydrol hydrologi ogical cal variab variables les such such as stream stream ﬂow, ﬂow, the impact of droughts during the pre-monsoon period is as well more severe in Bangladesh. High yield variety Boro rice, groundwater levels, reservoir and lake levels, snow pack, soil moistu moisture, re, etc. etc. Based Based on these these indica indicator tors, s, numernumerwhich is cultivated in 88% of the potentially available soil areas of the country, grows during this time. A deﬁcit ous indices have been developed to identify the severdrought conditions conditions (Dracup (Dracup et al., 1980; Wilhit Wilhitee of rainfa rainfall ll during during this this period period causes causes huge huge damage damage to ity of drought Glantz, 1985, 1987). 1987). However However,, most meteorolo meteorologgagriculture and to the economy of the country. As for and Glantz, ical drough droughtt indice indicess are based based on precip precipita itatio tion n data, data, exam exampl ple, e, drou drough ghtt in 1995 1995 led led to a decr decrea ease se in rice rice ical 6 Percentage of Normal Normal Index (Banerji (Banerji and Chabra, Chabra, and wheat wheat produc productio tion n of 3Ð5 ð 10 ton in the country country e.g. Percentage 1964), Precipit Precipitatio ation n Deciles Deciles Index (Gibbs (Gibbs and Maher, Maher, (Rahman and Biswas, 1995). This necessitated the import 1964), 1967), Bhalm Bhalme– e– Mooley Mooley Droug Drought ht Index Index (Bhalm (Bhalmee and of huge huge amount amount of food food grains grains to offse offsett the shortage shortage 1967), Mooley, 1980), Standardized Precipitation Index (McKee in national stocks and meet the national demand on an emergency basis (Paul, 1998). In this paper, pre-monsoon et al., 1993), Effective Drought Index (Byun and Wilhite, drought as well as droughts due to a deﬁcit of monsoon 1999), etc. Among these methods, the Standardized Prerainfall have been studied. cipitation Index (SPI) quantiﬁes the precipitation deﬁcit for multiple time steps, and therefore facilitates the temporal poral analys analysis is of drough droughts. ts. It has been found found that that SPI SPI * Corresponden Correspondence ce to: Shamsuddin Shamsuddin Shahid, Shahid, Department Department of Geography, Geography, is better able to show how drought in one region comRhodes University, Grahamstown 6140, South Africa. E-mail: sshahid [email protected] pares pares to drought in another another region (Guttman, (Guttman, 1998). It Copyright
2007 John Wiley & Sons, Ltd.
has also been been report reported ed that that SPI SPI provid provides es a better better spatial standardization than the other indices (Lloyd-Hughes and Saunde Saunders, rs, 2002). 2002). Theref Therefore ore,, SPI SPI is used used to study study the spatial and temporal characteristics of meteorological drought in western Bangladesh. Critical rainfall analysis, seasonal rainfall distribution, rainfall reliability and longterm rainfall trend are also studied to aid prediction of droughts in the area.
by De Marton Martonne ne and Thornt Thornthwa hwaite ite metho methods ds are 20Ð89 and 64Ð04, respectively, in the central-western and northwestern parts of Bangladesh. As the dryness index values in the region are close to those of a dry zone, the climate of these regions of Bangladesh can be considered very close to ‘dry’. The total annual evapotranspiration in this part part of Bangla Banglades desh h is also also lower lower than than or equal equal to the annual rainfall in some years. The location map of the study area is shown in Figure 3.
HYDRO-CLIMATE OF BANGLADESH DATA AND METHODS Geographi Geographicall cally, y, Bangladesh Bangladesh extends extends from 20° 340 N to 26° 380 N lati latitu tude de and and from from 88° 010 E to 92° 410 E longilongi- Rain Rainfa fall ll data data for for the the 39 year yearss 1961–19 1961–1999 99 from from 12 tude. Climatically, the country belongs to the sub-tropical meteorological stations in the western part of Bangladesh region where monsoon weather prevails throughout the was used to study the characteristics of meteorological year in most parts of the country. The average tempera- drought. The main problem encountered during the study ture of the country ranges from 17 ° C to 20Ð6 ° C during of droughts is missing rainfall data. The methods used to winter and 26Ð9 ° C to 31Ð1 ° C during summer. The aver- estimate the missing rainfall data and to study drought age relati relative ve humidi humidity ty for the whole whole year year ranges ranges from from characteristics are discussed below. 70Ð5% to 78Ð1%, with a maximum in September and a minimum in March. Three distinct seasons can be rec Estimation of missing rainfall data ognized in Bangladesh from the climatic point of view: Numerous methods for estimating missing data have (i) the the dry winte winterr season season from from Decemb December er to Februa February; ry; been described in the literature (Creutin and Obled, 1982; (ii) the pre-monsoon hot summer season from March to May; May; and (iii) (iii) the the rainy rainy monsoo monsoon n season season,, which which lasts lasts Seo et al., 1990; Kuligowshi and Barros, 1998; Schneider, 2001; Teegavarapu and Chandramouli, 2005). In the from June to October (Rashid, 1991). present study, study, a feedforwa feedforward rd artiﬁcial artiﬁcial neural network network The spatial distribution of rainfall over the country is present shown in Figure 1a. The map has been prepared from (ANN) approach similar to that proposed by Teegavarapu rainfa rainfall ll data data for the 30 years years 1970– 1970– 1999, 1999, availa available ble at and Chandramouli (2005) is used for the estimation of missing rainfall rainfall data. data. ANNs ANNs are computer models that 50 meteo meteorol rologi ogica call statio stations ns situat situated ed in and around around the missing country. The average annual rainfall of the country varies mimic the structure and functioning of the human brain, from from 1329 1329 mm in the the nort northh-we west st to 4338 4338 mm in the the and are known for their ability to generalize well on a north-east (Shahid et al., 2005). The map shows that the wide variety of problems and are well suited to predicwestern part of Bangladesh receives much lower rainfall tion applications (Bishop, 1995). Unlike many statistical than other parts of the country. The monthly distribution methods, ANN models do not make dependency assumptions among among input input variables variables and can solve multivaria multivariate te of rainfall over the western part of the country is shown tions on the graph in Figure 1b. The monthly distribution is problems with nonlinear relationships among input varicalculated from rainfall data for the 39 years 1961–1999 ables. The efﬁciency of ANN models does not depend avai availa labl blee at 12 stat statio ions ns in the the stud study y area area.. The The righ rightt on the density of measuring stations, rather on the numvertical axis of the graph represents rainfall in millimetres ber of stations used for the estimation of missing data and and the the left left vert vertic ical al axis axis repr repres esen ents ts the the rain rainfa fall ll as a (Teegavarapu and Chandramouli, 2005). As the density percentage of annual total rainfall. The graph shows that of rain gauges in the study area is low and ANNs are rainfall is very much seasonal in the area, almost 77% supposed to be suited to any distribution of rainfall staof rainfall occurring during the monsoon. In summer, the tions, the method is used in this paper for the estimation hottest hottest days experience experience temperat temperatures ures of 45 ° C or even of missing rainfall data. The missing rainfall data is random in most stations, hotter. In the winter the temperature falls to 5 ° C in some however, howe ver, continuous continuous missing missing data for several several years is places (Banglapedia, 2003). Thus, the region experiences two extremit extremities ies that clearly contrast with the climatic climatic also evident at some stations. The percentage of missing rainfa rainfall ll data data varies varies betwe between en 6% and 22% from from statio station n conditions of the rest of the country. A dryness study of Bangladesh, carried out using the to station, except one station (Khepupara), where about De Martonne aridity index (Figure 2a) and the Thornth- 39% of the data is missing. The average level of missing waite precipitation effectiveness index (Figure 2b) meth- rainfall data in the study area is 14%. Although the perods (Essen (Essenwa wange nger, r, 2001) 2001) from from clima climatic tic data data for the formance of ANNs improves with increasing percentage training data, studies have shown shown that training training with with 30 years 1970–1999 available at 50 meteorological sta- of training tions situated in and around Bangladesh, shows that west- 60% of the total data can reliably estimate unknown data ern side side of Bangla Banglades desh h can be classi classiﬁed ﬁed ‘sub-h ‘sub-humi umid’, d’, (Teegavarapu and Chandramouli, 2005). Therefore, it can the central part ‘humid’ and a small part of the north- be assumed that the ANN model estimated missing data easter eastern n side side ‘wet’ ‘wet’.. The lowest lowest index index values values obtain obtained ed in the present study with acceptable accuracy. Copyright
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 of missing missing rainfall rainfall data is is 6 : 4 : 1, as shown shown in Figur Figuree 4. The topology was selected using a trial and error procedure. The input input neuron neuronss use values values from from six neighb neighbour ouring ing statio stations ns around around the statio station n of intere interest st and the output output neuron neuron of the ANN provid provides es the missing missing value at the stat statio ion n of inte intere rest st.. Neur Neural al netw networ ork k trai traini ning ng is done done using a supervise supervised d back-prop back-propagat agation ion training training algorithm algorithm (Rumelha (Rumelhart rt and Mclella Mclelland, nd, 1986; Haykin, 1994). The choice of learning rate, momentum factor and activation function for the ANN determines the rate and reliability of the the trai traini ning ng of the the netw networ ork. k. In the the pres presen entt case case,, a learni learning ng rate rate of 0Ð1 and momentum factor of 0Ð4 was used. These factors were obtained by a trial and error method method (Haykin, (Haykin, 1994). A gradient gradient descent descent techniqu techniquee was was used used to adop adoptt weig weight htss in the the ANN ANN stru struct ctur uree to minimize the mean squared difference between the ANN Copyright
2007 John Wiley & Sons, Ltd.
output and the desired output. In the hidden and output layers, a sigmoidal activation function was used to model the transf transform ormati ation on of values values across across the layers layers.. After After computing the missing rainfall data, a geospatial database of rain rainfa fall ll time time seri series es is deve develo lope ped d with within in a GIS GIS by following the concept proposed by Goodall et al. (2004). Calculation of standardized precipitation index
The standa standardi rdized zed precip precipita itatio tion n index index (SPI, (SPI, Mckee Mckee et al., 1993) is a widely used drought index based on the probability of precipitation for multiple time scales, e.g. 1-, 3-, 6-, 9-, 12-, 18- and 24-month. It provides a comparison of the precipitation over a speciﬁc period with the precipitation totals from the same period for all the years included in the historical record. For example, a 3-month SPI at the end of May compares the March-April-May precipitation total in that particular year with the March Hydrol. Process. 22, 22, 2235–2247 (2008) DOI: 10.1002/hyp
Figure 2. Aridity Aridity maps obtained obtained by (a) De Martonne Martonne aridity index; and (b) Thornthwaite Thornthwaite precipitat precipitation ion effectiveness effectiveness index methods from rainfall data for 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 station are ﬁtted to a gamma probability distribution function:
1 ˇ˛ ˛
˛1 e x/ˇ
where ˛ > 0 is a shap shapee para parame mete ter, r, ˇ > 0 is a sca scale parameter, x > 0 is the amount of precipitation, and ˛ deﬁnes the gamma function. The maximum likelihood likelihood solutions solutions are used to optimally estimate the gamma distribution parameters, ˛ and ˇ for each station and for each time scale:
1 4 A
2007 John Wiley & Sons, Ltd.
and n D number number of precip precipita itatio tion n observ observati ations ons.. This This allows the rainfall distribution at the station to be effectively represented by a mathematical cumulative probability function given by: x
x ˛1 e x/ˇ dx
Since the gamma function is undeﬁned for x D 0 and a precipitation distribution may contain zeros, the cumulative probability becomes:
almost once per decade. Details of the SPI algorithm can be found in Guttman (1998; 1999), McKee et al. (1993; 1995) and Hayes et al. (1999). Rainfall reliability reliability
To compute rainfall reliability, the coefﬁcient of rainfall variation (CV) in percentage is used, CV D 100 ð υ/R where υ D standard deviation, and R of rainfall rainfall (mm).
Figure 3. Study area and the location of meteorological stations
For the mapping of the spatial extent of rainfall and droughts from point data, a Kriging interpolation method is used. Geostatistical analysis tool of ArcMap 9 Ð1 (ESRI, 2004 2004)) is used used for for this this purp purpos ose. e. Krig Krigin ing g is a stoc stocha hasstic interpolation method (Journel and Huijbregts, 1981; Isaaks Isaaks and Srivas Srivastav tava, a, 1989), 1989), which which is widel widely y recogrecognized nized as a standard standard approach approach for surface surface interpolatio interpolation n based on scalar measurements at different points. Studies showed showed that Kriging gives better better global global predictio predictions ns than other methods (van Beers and Kleijnen, 2004). However, Kriging is an optimal surface interpolation method based on spatially dependent variance, which is generally expressed as a semi-variogram. Surface interpolation using Kriging depends depends on the selected selected semi-vari semi-variogra ogram m model, model, and the semi-v semi-vari ariogr ogram am must must be ﬁtted ﬁtted with with a mathematical function or model. Depending on the shape of semi-v semi-vari ariogr ogram ams, s, differ different ent models models are used used in the present study for their ﬁtting.
RESULTS AND DISCUSSION
Figure Figure 4. Topology Topology of artiﬁcial artiﬁcial neural network used for the estimation estimation of missing rainfall data
where, q is the probab probabili ility ty of a zero. zero. The cumulati cumulative ve probability Hx is then then transf transform ormed ed to the standa standard rd normal distribution to yield the SPI (McKee et al., 1993). As the precipitation rate is ﬁtted to a gamma distribution for different time scales for each month of the year, the resulting resulting function function represents represents the cumulati cumulative ve probaprobability of a rainfall event for a station for a given month of the datase datasett and at differ different ent time scales scales of intere interest. st. This allows one to establish classiﬁcation values for SPI. McKee et al. (1993) classiﬁed drought severity according to SPI values as given in Table I. An SPI of 2 or more represents a very severe drought, and happens about 2 Ð3% of the time or about once in every ﬁfty years. An SPI between 1Ð5 and 1Ð99 represent representss a severe severe drought, drought, and happens about 4Ð4% of the time or once in every 25 years. An SPI between 1Ð0 and 1Ð49 represents a moderate drought, and happens about 9Ð2% of the time or Copyright
2007 John Wiley & Sons, Ltd.
The occurrence of droughts in the study area is identiﬁed from SPI time series of multiple-time steps. In the present study, SPI for 3- and 6-months time steps are computed to study the characteristics of drought in short and medium time periods. The 3-month SPI is used to describe the pre-monsoon drought, while the 6-month SPI is used to characterize seasonal droughts that occur due to rainfall deﬁcit in monsoon and non-monsoon months. Temporal and spatial distribution of drought
The The regi region onal al SPI SPI time time seri series es are are calc calcul ulat ated ed by a Thiessen 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
0 to 0Ð99 1Ð00 to 1Ð49 1Ð50 to 1Ð99 2Ð00 and less
Probability of occurrence (%)
Near normal or mild drought Moderate drought Severe drought Extreme drought
Figure 5. Arial values of SPI for (a) 3-month and (b) 6-month time steps
drough droughts ts identi identiﬁe ﬁed d from from the areal areal SPI time time series series are in the years years of 1963, 1963, 1966, 1966, 1968, 1968, 1973, 1973, 1977, 1977, 1979, 1979, 1982, 1989, 1992 and 1994–1995. Spatial extension of the 3-month SPI at the end of May and 6-month SPI at the the end end of Nove Novemb mber er for for the the four four wors worstt drou drough ghtt year yearss in Bang Bangla lade desh sh afte afterr inde indepe pend nden ence ce in 1971 1971 is shown shown in Figure Figuress 6 and 7, respec respectiv tively ely.. The 3-mont 3-month h SPI calcul calculate ated d for May May uses uses the precip precipita itatio tion n total total for March, April and May while the 6-month SPI calculated for Novem November ber uses uses the precip precipita itatio tion n total total for June June to Nove Novemb mber er.. The The 3-mo 3-mont nth h SPI SPI show showss a prepre-mo mons nsoo oon n drought and the 6-month SPI at the end of November shows a seasonal monsoon drought. Figure 6 shows that in 1982, 62% of the study area was affected by drought, among which, 12% was affected by severe droughts and 9% was by very severe pre-monsoon drough drought. t. In 1989 1989 and 1992, the whole whole study study area area was was affected by drought. About 78% of the area in 1989 and 26% 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 and 22% experiencing very severe pre-monsoon drought. The spatial extent of the 6-month SPI (Figure 7) shows that in 1982, almost 44% of the study area was affected by drough drought, t, with with almost almost 21% of the area affec affected ted by < severe severe drought drought (SPI 1Ð5). In 1989, about 65% of the area area was affect affected ed by drough drought, t, with with 13% affec affected ted by severe drought and 4% by very severe drought. In 1992 almost 72% of the area had an SPI less than 1Ð0 and 23% of the area had an SPI less than 2Ð0. In 1995 about 45% of the area had a 6-month SPI below 1Ð0 and 29% had an SPI less than 1Ð5. The spatial extent of both 3and 6-month SPIs show that in most of the drought years the central-western, north-western and northern areas had an SPI less than 1Ð0. Copyright
2007 John Wiley & Sons, Ltd.
Figure 6. Spatial distribution of 3-month SPI computed for the month of May in the four worst drought years after Bangladesh independence Hydrol. Process. 22, 22, 2235–2247 (2008) DOI: 10.1002/hyp
DROUGHTS OF BANGLADESH
The north-w north-west estern ern part of the area is found found to be the most likely to suffer severe drought at the 3-month time step. With increase in the time step from 3 months to 6 months, months, the area with potential potentially ly high occurrence occurrence is found to expand from the north-western to the northern part of the area (Figure 9b). The central part of the area is also found to have some potential for severe drought at both both 3- and and 6-mo 6-mont nth h time time step steps. s. The The perc percen enta tage ge occurrence of droughts in these categories is less in the southern coastal parts. Figure 10a shows that very severe droughts at the 3month time step occur in the northern part of the area with high percentage and in the western part with moderate percentage. The high drought occurrence area is found to expand to the north-western part as the time step is increased from 3 months to 6 months (Figure 10b). The central part of the area is found to have less potential for very severe severe drought drought in both both 3- and 6-month 6-month time time steps. The analys analysis is of drough droughtt occurr occurrenc ences es for differ different ent catego categorie riess and time-s time-step tepss indica indicates tes that that northe northern rn and north-wes north-western tern parts of the country are most vulnerable vulnerable to severe severe and very severe severe droughts. droughts. Moderate Moderate drought drought occurrences are higher in the southern part of the country compar compared ed to other other parts. parts. The centra centrall part part of the study area has moderate potential for both moderate and severe droughts, but less potential for very severe droughts. Critical rainfall analysis
Figure 7. Spatial distribution of 6-month SPI computed for the month of November in the four worst drought years after Bangladesh independence
The area potentially liable to suffer drought at different time steps is identiﬁed on the basis of their occurrences. The percentage of drought occurrences is computed by taking the ratio of drought occurrences in each time step to the total drought occurrences in the same time step and drought category (McKee et al., 1993). The percentage of occurrences of moderate, severe and very severe droughts are shown in Figures 8, 9 and 10, respectively. In each ﬁgure, the upper map shows the drought for a 3-month time time step step and and the the lowe lowerr one one show showss the the drou drough ghtt for for a 6-month time step. The The spat spatia iall dist distri ribu buti tion on of mode modera rate te drou drough ghts ts (Figure 8a) indicates that they tend to occur in the southeastern part of the area at the 3-month time step. The south-wes south-western tern and northern northern parts parts experien experience ce moderate moderate drought with lower frequencies at the 3-month time step. As the time step increases to 6 months (Figure 8b), the high drought potential zone is found to shift to the southwestern part of the area. The northern part has less potential for moderate drought at the 6-month time-step. The distribution of severe droughts (Figure 9a) shows a completely different pattern from the moderate drought. Copyright
2007 John Wiley & Sons, Ltd.
Critical or threshold rainfall determines the minimum moisture moisture input required required for non-droug non-drought ht conditio conditions ns in various time steps. As the SPI values below zero indicate a deﬁcit deﬁcit in rainfall, rainfall, rainfall rainfall corresponding corresponding to zero SPI is considere considered d the critical value in this present research research (Sonmez et al., 2005). 2005). The critic critical al rainfa rainfall ll values values are computed for each station and then used to map its spatial distribution. The distribution of rainfall required during the monso monsoon on (June (June to Novem November ber)) and dry (Dece (Decembe mberr to May) months in the study area for normal conditions are shown in Figure 11a and b, respectively. The ﬁgure shows shows that that a minim minimum um of 1550 1550 mm monsoo monsoon n rainfa rainfall ll is required for normal conditions in the northern part of Bangladesh, which is one of the most drought-prone areas of the country. country. The north-wes north-western tern and centralcentral-west western ern parts require a minimum of 1250 mm monsoon rainfall for normal conditions. Figure 11b shows that in the northwestern part of the area, rainfall less than 225 mm during the months months of Decem December ber to May May may may cause cause a rainfa rainfall ll deﬁcit. The required rainfall varies from 225 mm to more than 375 mm in the northern part of the country during this time period. Average rainfall during rainy and dry months over the study area for the last 39 years is shown in Figure 12a and b, respectively. Figure 12a shows that average monsoon soon rainfa rainfall ll varies varies betwe between en 1238 1238 mm in the centra centrallwestern western part and more than 1551 mm in the northern northern and southern parts. The dry months rainfall varies between 251 251 mm in the the nort northh-we west st and and more more than than 426 426 mm in Hydrol. Process. 22, 22, 2235–2247 (2008) DOI: 10.1002/hyp
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 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 higher than the minimum rainfall required for normal condition in the area. area. Howeve However, r, reliab reliabili ility ty analys analysis is of rainfa rainfall ll Copyright
2007 John Wiley & Sons, Ltd.
during the monsoon months (Figure 13a) and dry months (Figur (Figuree 13b) 13b) shows shows that that the rainfa rainfall ll is highly highly variab variable le in some some part partss of the the area area.. The The vari variat atio ion n in mons monsoo oon n Hydrol. Process. 22, 22, 2235–2247 (2008) DOI: 10.1002/hyp
DROUGHTS OF BANGLADESH
e g a r e v 4 0 Ð A 0 0 l a e r A a r i h k t a S
rainfa rainfall ll is about about 30% and in non-mo non-monso nsoon on rainfa rainfall ll is more than 50% of the average rainfall in north-western and central-western parts of the area. The rainfall variabilit ability y in the northe northern rn part part is compar comparati ativel vely y less less durduring dry months months,, but is high high (betwe (between en 28% and 30%) during during the monsoo monsoon. n. As critic critical al rainfa rainfall ll values values in the nort northh-we west ster ern n part part of the the area area are are very very near near to the the averag averagee rainfa rainfall ll of the area and the year to year year varivariatio ation n of rain rainfa fall ll in the the area area is very very high high,, it can can be assumed that the area is very vulnerable to future severe droughts.
6 4 0 Ð 0 Ð 0 0
r u p 4 Ł g 4 Ð 3 Ð n a 1 0 R
Annual trend of rainfall
i h 1 a 9 4 h Ð 1 Ð s 0 j 0 a R a 7 n 3 l Ð 1 Ð u 0 2 0 h s K n o i t a a t s r a t p Ł Ł n u e 9 8 r p 4 3 e e Ð Ð f f h 3 0 i d K n i l l e 4 2 a r f 0 n o Ð 0 Ð i s a s 0 0 r e J n a e m i l 7 a d 8 8 u r Ð 1 Ð u n h 0 s 1 n I a f o d r n e u p 5 6 r d 4 Ð 1 Ð T i 1 0 r . a I I F e l b a T r u Ł p 4 8 j a 1 2 Ð Ð n 2 i 0 D a r 6 g 2 Ð 0 Ð o 1 B 0 0 a 9 l o 7 Ð 2 Ð h 1 0 B l a 9 8 h s 3 Ð 0 Ð i r 0 0 a B n o i s s e u r g a e T R l l r a a e d n n e i L K Copyright
. . ) ) d d e l e i l a i t a - t 2 2 ( ( l e l e v e v l e l 1 5 0 Ð Ð 0 0 e 0 e h h t t t a t a t t n n a c a c ﬁ ﬁ i n i n g g i s i s s i s d i d n e n r e r T T Ł
The trend trend in annual annual rainfal rainfalll over over the study area is calculated using a linear regression method to visualize the future future hydrologi hydrological cal conditions conditions in the area. area. Rainfall Rainfall data for the 39 years 1961–1999 is used for this purpose. As the the time time seri series es of rain rainfa fall ll is not not very very long long,, the the use of linear regression to estimate time trends may be questionable. Therefore, Kendall-tau trend estimation is also used to compare with the result obtained from linear regression. The ﬁeld signiﬁcance of the time trends has also been assessed using a Mann–Kendall test. The result obtained is shown in Table II. The study shows that there is no signiﬁ signiﬁca cant nt change change in annual annual areal areal rainfa rainfall ll in the study area. However, at local scale, signiﬁcant increase in rainfall is observed at three stations, one is the coastal area (Khepupara) and the other two are in the northern part of Bangladesh (Rangpur and Dinajpur). Among the remai remainin ning g 11 statio stations, ns, 10 show show a negati negative ve change change in rainfall, but these are not statistically signiﬁcant. The spatial distribution of rainfall trend over the study area is shown in Figure 14. Plus ( C) signs in the ﬁgure mean mean an increa increase se in annual annual rainfall rainfall,, minus minus signs signs () indicate a decrease in annual rainfall and zero (0) means no observable change in annual rainfall during the time period period 1961– 1961– 1999. 1999. The ﬁgure ﬁgure shows shows that that rainfa rainfall ll has declined in the central part of the study area. Maximum decl declin inat atio ion n of rain rainfa fall ll is foun found d to occu occurr at a rate rate of 1 1Ð88 mm year in Ishurd Ishurdii near near the centra central-w l-west estern ern part part of the study area. Betwee Between n the two most most drough droughtt vulner vulnerabl ablee areas, areas, a signiﬁ signiﬁcan cantt increa increase se in rainfa rainfall ll is observed in the northern part and no change in rainfall is observed in the north-western part of the study area.
Table III. El Ni˜no years and drought years of Bangladesh El Ni˜ Nin˜ o year
e g a r e 2 v 8 2 Ð 2 Ð A 0 0 l a e r A a r i h k t a S
a r i h k t a S
i h 5 a 5 0 h Ð 0 Ð s 0 j 0 a R
r u 0 1 p 1 1 g Ð Ð n a 0 0 R
a 7 n 9 l Ð 0 Ð u 0 h 0 0 K s n o i t a t s t n e r e f f i d r o f s n a e m y l r a e y I P S d n a I E M e h t f o n o i t a l e r r o C . V e l b . a l e T
v e l 5 0 Ð 0 e h a 2 0 t t r 1 1 a g Ð Ð t o 0 0 B n a c ﬁ i n g s a 3 9 i l y o 4 Ð 3 Ð l l h 0 0 a B i c t s i t a t s l s a 7 1 n h a s 4 Ð 4 Ð i e r 0 m a 0 B l d o b n i n s r a e n m b o r s m a r e u a e p N
A relationship between drought in Bangladesh and El Nino has been observed. El Nino years and drought years in Bangladesh are compared in Table III. The relationship between the Multivariate ENSO Index (MEI) and rainfall and and SPI SPI in the the stud study y area area is esti estima mate ted d usin using g a twotwosided Pearson and Spearman Spearman correlation correlation test. test. Pearson Pearson and Spearman Spearman correlation correlation coefﬁcie coefﬁcients nts of yearly yearly means means of Multivariate ENSO Index (MEI) with yearly means of precipitation are 0Ð28 and 0Ð22, respectively, for the study area. However, the correlations are not statistically signiﬁca signiﬁcant. nt. The negative negative correlation correlation between between MEI and yearly means of precipitation suggests that precipitation decreases decreases when MEI increases. increases. MEI is also correlated correlated with with prec precip ipit itat atio ion n at each each stat statio ion n in the the stud study y area area.. Signiﬁcant negative correlation between precipitation and MEI MEI is foun found d in thre threee stat statio ions ns.. Table able IV show showss the the correlati correlation on matrix matrix of the MEI and precipitatio precipitation n yearly yearly means for each station. Correlation between MEI and SPI for the study area is also investiga investigated. ted. No signiﬁcan signiﬁcantt correlati correlation on between between yearly means of MEI and SPI is found for the study area. At local scale statistically signiﬁcant negative correlation between yearly means of MEI and SPI is found in two stations. The correlation matrix of the MEI and SPI yearly means for each station is shown in Table V.
r u 1 6 p 3 1 g Ð Ð n a 0 0 R
3 3 0 Ð 0 Ð 0 0
i h 3 a 3 1 h Ð 1 Ð s 0 s j 0 n a o R i t a t s t n a e l 7 n 2 r e Ð 2 Ð u 2 f 0 0 f h i K d r o f s a n r a a e p 1 m u 5 0 Ð 0 Ð p y e 0 0 l r h a e K y n o i t a e 7 2 t i r 0 Ð 0 Ð s p o i s 0 0 c e e J r p d n a i 2 8 I d Ð 2 Ð E r u 2 0 h M I s 0 e h t f o r n u 2 p 8 o d 4 4 i Ð Ð t i 0 0 a r l a e r r F o C . V r u 4 8 I p 4 3 e j Ð Ð l a b n 0 0 a i T D
Drought and ENSO phenomena relationship
4 5 1 Ð 1 Ð 0 0
a r a p 4 3 u 1 Ð 1 Ð p e 0 0 h K
e r o s s e J
8 6 2 Ð 1 Ð 0 0
i d r u h s I
3 6 0 Ð 0 Ð 0 0
r u p d i r a F
7 2 4 4 Ð Ð 0 0
r u p j a n i D
2 0 4 4 Ð Ð 0 0
a r g o B
a l o h B l a h s i r a B
2007 John Wiley & Sons, Ltd.
. l e v e l 5 Ð 6 4 0 0 0 Ð 0 Ð e 0 0 t h t a t n a c ﬁ 2 0 i n 0 g Ð 2 Ð s 0 0 i y l l a c i t s i t a t 1 2 s s 1 Ð 1 Ð a 0 0 n e m d l o b n i n s r a e n m b o r s m a r e u a e p N
The spatial and temporal characteristics of meteorological ical drough droughts ts in the wester western n part part of Bangla Banglades desh h have have been studied by reconstructing historical occurrences of drought drought for multiple multiple time steps and drought drought categorie categoriess by employing an SPI approach. The major outcome of the study is the production of a drought potential map of the western part of Bangladesh. Drought potential mapping is one of the major steps in drought mitigation and planning. The study reveals that north and north-western areas are most likely to suffer drought. It is observed that there is no relation between the rainfall distribution and drought drought potential potential zones. The northern northern region region normally normally receives more than the average rainfall of the study area, but the area has a higher potential for drought. The maximum rainfall demand in this area is also high compared to other drought-prone areas. It is hoped that the study will assist in guiding the operational responses in drought risk reduction in Bangladesh. A signiﬁca signiﬁcant nt negative negative relation relation between between precipita precipitation tion deﬁ deﬁcit and and MEI is foun found d for fou four stat statio ions ns in the study area. Signiﬁcant negative relation is also observed between SPI and MEI at two stations. Therefore, information on the ENSO could be useful for Bangladesh for drought management. The trend analysis in annual mean rainfall in the western part of Bangladesh shows that there is no change in average precipitation. Rainfall is found to change signiﬁcantly at local scale at some stations. With an average mean annual rainfall very near to the critical rainfall, high rainfall variability and no signiﬁcant change Hydrol. Process. 22, 22, 2235–2247 (2008) DOI: 10.1002/hyp
DROUGHTS OF BANGLADESH
Figure Figure 14. Map showing the spatial spatial distribution distribution of rainfall rainfall trend computed from rainfall data for the 39 years 1961–1999
in annual rainfall, the north-western part of Bangladesh is expec expected ted to experi experienc encee more more severe severe drough droughts ts in the future.
REFERENCES Banerji Banerji S, Chabra BM. 1964. Drought Drought characteri characteristics stics and estimating estimating probabilities of their occurrences. Surfacewaters symposium , Publication No. 63, 189–192, WMO/IASH: Belgium. Banglapedia 2003. National Encyclopaedia of Bangladesh , Islam S (ed). Asiatic Society of Bangladesh: Dhaka. Bhal Bhalme me HN, HN, Mool Mooley ey DA. DA. 1980 1980.. Larg Largee scal scalee drou drought ghts/ s/ﬂo ﬂoods ods and and 108: 1197– monsoon circulation. Monthly Weather Review , 108: 1197– 1211. 1211. Pattern Recognition Recognition. Oxford Bishop CM. 1995. Neural Networks for Pattern Oxford University Press: Oxford. Byun HR, Wilhite DA. 1999. Objective quantiﬁcation of drought severity 12: 2747– and duration. Journal of Climate 12: 2747– 2756. 2756. Creutin JD, Obled C. 1982. Objective analyses and mapping techniques for rainfall ﬁelds: an objective comparison. Water Resources Research 18: 18: 413–431. Dracup JA, Lee KS, Paulson EG Jr. 1980. On the deﬁnition of droughts. Water Resources Research 16: 16: 297–302. ESRI 2004. ArcMap Environmentall Systems Systems Research Research Institute: Institute: ArcMap 9Ð1 . Environmenta Redlands. General Climatology Climatology:: Classiﬁcation Classiﬁcation of CliEssenwange Essenwangerr OM. 2001. General mates . Elsevier: The Netherlands.
2007 John Wiley & Sons, Ltd.
Gibbs Gibbs WJ, WJ, Maher Maher JV. JV. 1967. 1967. Rainfall Rainfall deciles deciles as drought drought indicators indicators. Australian Australian Bureau Bureau of Meteorology Meteorology Bulletin Bulletin 48, Common Commonwea wealth lth of Australia: Melbourne. Goodall JL, Maidment DR, Sorenson J. 2004. Representation of spatial and and temp tempor oral al data data in ArcG ArcGIS IS.. AWRA AWRA GIS and Water ater Resour Resources ces Conference III , Nashville, TN. Guttma Guttman n NB. 1998. 1998. Compar Comparing ing the Palmer Palmer Drought Drought Index Index and the Journal of the Americ American an Water ater Standardized Standardized Precipitat Precipitation ion Index. Journal Resources Association 34: 34: 113–121. Guttman NB. 1999. Accepting the Standardized Precipitation. Journal of American. Water Resources Association 35: 35: 311–322. Hayes MJ, Svoboda MD, Wilhite DA Vanyarkho OV 1999. Monitoring the 1996 drought using the Standardized Standardized Precipitat Precipitation ion Index. BAMS 80: 80: 429–438. Neural Network Networks: s: A Compre Comprehen hensiv sivee Founda Foundatio tion n. Haykin Haykin S. 1994. 1994. Neural Macmillan: New York. Introduc ductio tion n to Applie Applied d GeoGeoIsaaks Isaaks HE, HE, Srivas Srivastav tavaa RM. 1989. 1989. An Intro statisitics . Oxford University Press: New York. Journel Journel AG, Huijbregts Huijbregts CJ. 1981. Mining Geostatistics . Academic Press: New York. Kuligowski Kuligowski RJ, Barros PA. 1998. Using artiﬁcial neural networks networks to estimate missing rainfall data. Journal of the American Water Resources Association 34: 34: 1437– 1437– 1447. 1447. Lloyd-Hughes B, Saunders MA. 2002. A drought climatology for Europe. International Journal of Climatology 22: 22: 1571– 1571– 1592. 1592. McKee McKee TB, Doeske Doesken n NJ, Kleist Kleist J. J. 1993. 1993. The relati relations onship hip of drought drought Applied freque frequency ncy and durati duration on to time time scales scales.. 8th Conference on Applied Climatology, 17–22 January, Anaheim, CA; 179–184. McKee McKee TB, Doeske Doesken n NJ, Kleist Kleist J. 1995. 1995. Drought Drought monitor monitoring ing with with multiple time scales. 9th Conference Conference on Applied Applied Climatology Climatology, Dallas, TX; 233–236. McKee McKee TB, Doeske Doesken n NJ, Kleist Kleist J. J. 1993. 1993. The relati relations onship hip of drought drought Eighth Conference Conference on Applied Applied frequency and duration to time scales. Eighth Climatology , American American Meteorological Meteorological Society: Society: Jan. 17– 23, Anaheim CA; 179– 186. 186. Paul Paul BK. 1998. 1998. Coping Coping mechan mechanism ismss practi practised sed by drought drought victim victimss Applied Geography Geography 18: (1994/5 (1994/5)) in North North Bengal, Bengal, Bangla Banglades desh. h. Applied 18: 355–373. Rahman A, Biswas PR. 1995. Devours resources. Dhaka Courier 11(42): 11(42): 7–8. Geography of Bangladesh Bangladesh. Univer Rashid HE. 1991. Geography Universit sity y Press Press Ltd: Ltd: Dhaka; 579. Parallel Distributed Distributed Processing Processing. Rumelhart Rumelhart DE, DE, Mclelland Mclelland JL. 1986. Parallel MIT Press: Cambridge, Cambridge, MA. Schneider Schneider T. 2001. Analysis of incomplete incomplete climate data: estimation estimation of mean values and covariance matrices and imputation of missing values. 14: 853–871. Journal of Climate 14: Seo DJ, DJ, Krajewski Krajewski WF, WF, Bowles DS. 1990. Stochastic Stochastic interpolation interpolation of rainfall rainfall data from rain gages and radar using cokriging— cokriging— 1. Design of experiments. Water Resources Research 26: 26: 469–477. Shah Shahid id S, Chen Chen X, Haza Hazari rika ka MK. MK. 2005 2005.. Asse Assess ssme ment nt arid aridit ity y of Bangladesh Bangladesh using geographic geographic information information system. system. GIS Development 9(12): (12): 40 –43. Shahid S, Nath SK, Roy J. 2000. Ground water potential modelling in a 21: softrock area using GIS. International Journal of Remote Sensing 21: 1919–1924. Sonmez Sonmez FK, FK, Komusc Komuscu u AU, Erkan Erkan A, Turgu Turgu E. E. 2005. 2005. An analys analysis is of spatial spatial and temporal temporal dimension of drought drought vulnerabili vulnerability ty in Turkey Turkey Hazards 35: using using the Standa Standardi rdized zed Precip Precipita itatio tion n Index. Index. Natural Hazards 35: 243–264. Teegavarapu RSV, Chandramouli V. 2005. Improved weighting methods, determ determini inisti sticc and stocha stochasti sticc data-d data-driv riven en models models for estima estimatio tion n of Journall of Hydrol Hydrology ogy 312(1–4): missing missing precipitati precipitation on records. records. Journa 312(1–4): 191–206. van van Beer Beerss WCM, WCM, Klei Kleijn jnen en JPC. JPC. 2004. 2004. Krig Krigin ing g inte interp rpol olat atio ion n in Proceedi edings ngs of the 2004 2004 Winte Winterr Simula Simulatio tion n simulation: simulation: a survey. survey. Proce Conference, Ingalls Ingalls RG, Rosset Rossetti ti MD, MD, Smith Smith JS, JS, Peters Peters BA (eds), (eds), Washington, DC, December. Wilhite DA, Glantz MH. 1985. Understanding the drought phenomenon: 10(3): 111–120. the role of deﬁnitions. Water International 10(3): Understand anding ing the Droug Drought ht Wilh Wilhit itee DA, DA, Glan Glantz tz MH. MH. 1987. 1987. In Underst Phenomenon: Phenomenon: the Role of Deﬁnitions Deﬁnitions . Wilhite Wilhite DA, DA, Easterling Easterling WE, Wood DA (eds). Planning for Drought. Vestview Press: Boulder CO; 11–27.