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J. Indian Water Resour. Soc. Vol. 30 No. 1, January, 2010

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PROTECTION OF BRIDGE ABUTMENTS FROM SCOUR

V. K. Sarda

ABSTRACT

The failure of bridges due to excessive abutment scour causes high maintenance cost oreven the bridge collapse resulting in interruption of traffic as well as casualties to life. Henceability to protect bridge abutment from scour is critical to bridge safety. Review showed that thereare two approaches to scour mitigation viz. bank hardening and flow-altering. Selection of acountermeasure involves life cycle cost assessment for the particular site along with social andenvironmental issues. However, their application for mobile bed and in compound channels isstill to be established.

Key Words : Abutments; Scour protection; Scour countermeasures; Riprap; Spur dyke; ParallelWalls; Collars; Cable-tied blocks; Geo-bags.

Life Member, Principal, North West Institute of Technology, Dhudike,Dist. Moga (Punjab)Paper No. 1193(f)

INTRODUCTION

Bridge failures due to local scour at bridgefoundations (i.e., bridge abutments and piers) haveheightened interest in scour prediction and scourcountermeasures.

Pier scour and pier scour countermeasures havebeen studied by Ettema (1980), Jones (1989), Johnson(1994), Richardson and Lagase (1999), Mueller andLanders (1999) and Lagasse et. Al. (2001).On the otherhand, an extensive review, recommendations and designsuggestions for a number of bridge protection devicesare available in Kumar (1998) and Parker et.al. (1998).Also several comprehensive technical manuals [FederalHighway Administration] (HEC-18, HEC-20 and HEC-23) for dealing with the problem of bridge scour and itsprotection are in the market.

Literature and data showed that the problem ofscouring at bridge abutments is quite significant. A study(Richardson and Abed, 1993) carried out in 1973 forthe U.S. Federal Highway Administration concluded thatof the 383 bridge failures, 25% involved pier damageand 72% involved abutment damage. On the other hand,of the 108 bridge failures surveyed in New Zealandduring the period of 1960 - 1984, 29 were attributed toabutment scour (Melville, 1992). It was also mentionedin this study that 70% of the expenditure on bridgefailures in New Zealand was due to abutment scour.

In spite of this, the scour at bridge abutments hasreceived less attention, and countermeasures forabutment scour are greatly needed.

BRIDGE ABUTMENT SCOUR MECHANISM

The general scour mechanism at piers is wellunderstood after several comprehensive studies (Ettema,1980; Kumar, 1998; Melville (1975), in which thecombined action of down flow and the horse shoevortices and the wake vortices induced by the presenceof the pier have been found to be responsible for thescour around the pier.

Recent studies by Wong (1982), Tey (1984), Kwan(1984, 1988), Kandasamy (1985) and Dongol (1994) ofabutment scour have shown that the scour mechanismat abutments is very similar to the scour mechanism atpiers. The downflow and the principal vortex at theupstream corner of the abutment, together with thesecondary vortices and wake vortices at the middle partand the downstream corner of the abutment, result incomplex interactions between the fluids and the bedmaterial to cause scour at abutments. Observations offlow patterns around abutments derived from flowvisualization techniques using dye injection, dye crystalsstrategically placed on the sand bed, paper floats, smoketunnel experiments by Liu et al. (1961) and Gill (1972)are summarized in Figure 1.

In addition to the vortex systems, seepage effectshave been found (Hagerty and Parola, 1992) to be very

Journal of Indian Water ResourcesSociety Vol. 30 No. 1, January, 2010


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important to interact with turbulent vortices to aggravatelocal scour. The fluctuating pressure differences,induced by the flow separation at abutments, causeseepage into and out of the abutment foundation. Thisresults in ejection of sediment particles from the bedwhere seepage emerges beside the abutment.

On the other hand, Molinas et al.(1988)experimentally studied the shear stress distributionaround vertical wall abutments. It was found that forFroude numbers ranging from 0.30 to 0.90 and forprotrusion ratios of 0.1, 0.2 and 0.3, the highest valuesof shear stresses occurred at the upstream abutmentcorner. In this study, shear stresses around vertical wallabutments were found to be amplified up to a factor of10 depending upon flow conditions and abutmentprotrusion ratios. It was also found that shear stressamplification due to local effects at the nose region of avertical wall is a function of the opening ratio and turningangle. Shear stress amplification due to channelrestrictions, on the other hand, were found to be afunction of opening ratio, approach Froude number, andprotrusion length. The formulas for estimating shearstress amplification due to local effects at the nose regionof vertical wall abutments and for estimating shear stressamplification due to channel restriction were alsoproposed. The sum of the two shear stress amplificationsequals the total nose shear stress amplification.

In a study by Ahmad and Rajaratnam (2000) forflow around a 45° wing-wall bridge abutment, it wasfound that the approach flow turns into a complex 3Dskewed flow in the upstream and surrounding regionsof the abutment. The bed shear stress was found toincrease substantially near the abutment, reaching apeak value of 63.3

o at the abutment nose, with

and o being the shear stress and the approach shearstress at the bed respectively. It was also found thatthe skewing of the flow around the bridge abutment isgreater than flow around bridge piers.

SCOUR PROTECTION DEVICES FORABUTMENTS

The successful countermeasures for local scour atabutments work by either diverging the erosive flowaway from the structure and are called flow alteringcountermeasures or making the area around theabutment more resistant to erosion which are calledbank hardening countermeasures

In HEC-23 (Lagasse et al. 1997), a countermeasurematrix has been organized to highlight the various groupsof countermeasures and to identify their individualcharacteristics. These countermeasures have beenorganized into groups based on their functionality withrespect to scour and stream instability.

Flow Altering Countermeasures

They reduce flow’s energy to scour.

Spur dykes

Spur dykes have been studied intensively by Lacey(1929), Inglis (1949), Laursen (1952, 1962), Garde etal. (1961), Gill (1970), Cunha (1973), Franco (1982),Copeland (1983), Rajaratnam and Nwachukwu (1983),Zaghloul (1983), Brown (1985), Suzuki et al. (1987),Kwan and Chaudhary (1992), Wu and Lim (1993),Mayerle (1995), Shields et al. (1995), Tominaga et al.(1997), Zhang and Du (1997), Soliman et al. (1997) andKuhnle et al. (1997, 1998, 1999), as river training orriver rehabilitation structures instead of abutment scourcountermeasures. Spur dike length, alignment with flow,flow structure around spur dykes, construction materialsand many other parameters have been investigated.Scour depth predictors around spur dykes and spur dykedesign guide lines have also been provided.

Spur dykes are used to alter flow direction, inducedeposition, or reduce flow velocity [Fig. 2]. Their mainuse is to protect banks that contain bridge abutmentsfrom eroding. Spur dykes are commonly used to realignstreams as they approach a bridge abutment. A bridgeabutment may be in danger of being severely erodedwhen it is subjected to high velocity flow from a channelthat has changed course due to meandering of thechannel. Spur dykes may also be used to establish andmaintain the alignment of a channel. They have beenused to decrease the length of the bridge required andreduce the cost and, maintenance of the bridge inactively migrating braided channels (Lagasse et al.,2001).

Fig. 1 Flow pattern around an abutment


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Liu et al. (1961) conducted clear water and live-bed laboratory experiments on a compound channel usingspur dykes as scour protection device. It was foundthat spur dykes can efficiently protect the abutmentprovided they are properly designed. Design guidelineswere also discussed. The most effective configurationto prevent local scour at the abutment consisted of threespur dykes composed of rock located upstream of theabutment and two at the corners.

Hard Points

HEC-23 (Lagasse et al., 2001) has proposed hardpoints as one of the scour protection countermeasures.They consist of stone fills spaced along an eroding bankline, protruding only short distances into the channel. Aroot section extends landward to preclude flanking.Hardpoints are most effective along straight or relativelyflat convex banks where the streamlines are parallel tothe bank lines and velocities are not greater than 3 m/swithin 15 m of the bank line. Hardpoints may beappropriate for use in long, straight reaches where bankerosion occurs mainly from a wandering thalweg atlower flow rate. They would not be effective in haltingor reversing bank erosion in a meander bend unlessthey were closely spaced, in which case spurs, retarderstructures, or bank revetment would probably cost less(HEC-23).

Guide banks or Parallel Walls

Guide banks are earth or rock embankments placedat abutments to improve the flow alignment and movethe local scour away from the embankment and bridgeabutment [Fig. 3]. The major use of guide banks hasbeen to prevent erosion by eddy action at bridgeabutments or piers where concentrated flood flowtraveling along the upstream side of an approachembankment enters the main flow at the bridge (HEC-23). Spring (1903), Neil (1973), Bradley (1978), Brice

and Blodgett (1978), Smith (1984), Richardson andSimons (1984) and Lagasse et al. (2001) have studiedguide bank orientation, length, crest height, shape andsize, downstream extent and many other concerns.

Design guidelines for guide banks have beenprovided by Neil (1973), Bradley (1978), Ministry ofWork and Development (1979), Central Board ofIrrigation and Power (1989) and Lagasse et al. (1996).Guide banks provide a smooth transition for flow on thefloodplain to the main channel. The effectiveness ofguide banks is a function of river geometry, quantity offlow on the floodplain, and size of bridge opening(Richardson and Simons, 1984). By establishing smoothparallel streamlines in the approaching flow, guide banksimprove flow conditions in the bridge waterway. Scour,if any, is near the upstream end of the guide bank awayfrom the bridge.

Guide banks can protect not only bridge abutmentsfrom local scour, but also the approach embankmentbecause of the still water area behind it. Whenembankments span wide floodplains, the flows from highwaters must be aligned to flow smoothly through thebridge opening. Overbank flows on the floodplain canseverely erode the approach embankment and couldincrease the depth of the scour at the bridge abutment.They can be used to redirect the flow from theembankment and to transfer the scour away from theabutment. They also serve to reduce the separation offlow at the upstream abutment face and maximize thetotal bridge waterway area and reduce the abutmentscour by lessening the turbulence at the abutment face(Lagasse et al., 2001)

There are practically two kinds of guide banks. Oneis the American practice, which is to give guide banksan elliptical form convergent to the opening, whereasthe other one used in Pakistan and India gives that guidebanks should be straight and parallel to the opening with

Fig. 2 Definition Sketch of a spur dyke Fig. 3 Parrallel wall (Guide banks) countermeasure


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a curved section at the upstream and downstream ends.Parallel guide banks straighten the flow more effectivelythan convergent ones (Richardson and Simon, 1984).

Properly designed guide banks and abutment riprapprovide an acceptable alternative to designing bridgeabutments to withstand the development of the full scourprism predicted by equations currently available forestimating abutment scour (Hua et al., 2006). Since theyhave excellent performance in the field, guide banksshould be given serious considerations as scourprotection device for abutment scour when developingplans for repair or replacement of those endangeredbridges. They can eliminate many scour problemsassociated with bridge crossings and their use can resultin a worthwhile savings to the highway program (Huaet al., 2006).

Collars

Collar is a protection device that has been found tobe helpful for arresting scour around bridge pier.Basically it is a piece of circular shaped steel plateattached around the abutment [Fig. 4] sitting horizontallya short distance from the bed. Much literature onattachment of collars on abutment, as scour protection,is not available. However, Liu et al (1961) studied flathorizontal steel collars around wing-wall abutmentending at the mean channel under clear water flowconditions in a laboratory flume. It was found that thesecollars were able to protect the bridge abutmentefficiently by eliminating secondary vortices thatordinarily would cause local scour. The minimum collardimensions that eliminated local scour were 0.7L0.23L[where L is abutment length perpendicular to flowdirection] provided at 0.08y [y is the mean channel flowdepth] below the mean sediment elevation gave the testresults of scour reduction. They also retarded thedevelopment of scour hole.

Bank Hardening Countermeasures

They comprised of various hard materials locatedon the bed and banks in the vicinity of abutment toincrease the ability of the bed and bank to resist scourby flow.

Riprap

Riprap is the most common countermeasureemployed and consists of large rocks arranged flushwith the bed and banks in several layers of thickness. Anumber of researchers like Simons and Lewis (1971),Lewis (1972), Mackey (1986), Simons and Li (1989),Croad (1989), Pagan-Ortiz (1991) and Eve (1999) haveconducted research on the performance of riprapprotection on abutment slopes and aprons under clear-water conditions while Melville et al. (2006) studied thesame under live bed conditions. In these studies, failureof riprap beds has been observed due to (a) dislodgingof the individual rocks due to excessive stream velocity,(b) dislodging of individual rocks at the edge of the riprapblanket due to the flow undermining and lifting the rocksup and into direct contact with the flow, and (c) sinkingof the riprap blanket due to winnowing of the fine bedmaterial up through the rocks where it is carried awayby the flow [Fig. 5].

Design consists of the specification of the rock sizeto avoid direct dislodging, riprap blanket thickness, thelateral extent of the blanket to avoid edge failure, thegradation of riprap, and a filter material to avoidwinnowing of the fines. The size of riprap stone can bedetermined from (Pagan-Ortiz, 1991):

81.023.02

50 1064.1

gS

yUDs

(1)

Here D50 is the median riprap size, U is the cross-sectionally averaged water velocity, Ss is the specific

Fig. 4 Collar device at abutment

Fig. 5 Riprap at abutment


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gravity of the riprap material, and g is the gravitationalconstant.

The thickness of the riprap blanket, t = 1.5D100 ,where D100 is the largest size of the rip rap stone(Lagasse et al., 2001). The lateral extent of the riprapblanket can be found form:

501min DddCW bs (2)

Here, Wmin is the minimum riprap blanket extentacross the channel, C1 is constant equal to 1.68 and1.19 at the upstream and downstream comers of theriprap layer, respectively, ds is the depth of equilibriumscour, and db is the depth of the riprap blanket bottombelow the average channel bed level (Fig. 5). ds can befound for bedform-dominated cases by:

HCd s 2 (3)

Here H is maximum bed-form height and C2 is aconstant values for which are 1.2 and 1.0 for theupstream and downstream comers of the riprap layer,respectively (Melville et al., 2006). Otherwise, add otherscour components to ds. The proper gradation of riprapcan be found Table 1.

To design the filter material the pore space shouldbe finer than the natural riverbed material.

Cable-tied Blocks

Cable-tied block consist of a series of blocks linkedtogether with cable to hold them together as a coherentmat. Where riprap of adequate size is unavailable orwhere environmental or geometric constraints precludeuse of riprap, alternatives to riprap, such as cable-tiedblocks, are necessary.

Cable-tied blocks (CTB) comprise concrete blocksinterconnected with metal or non-metallic cables(Prezedwojski et al., 1995). CTB have the advantagesof ease of construction, minimal encroachment into theriver channel, and a lower weight than riprap per unitarea covered. Previous studies and experiments on theuse of CTB for scour protection of bridge foundations

include Parker et al. (1998), Jones et al. (1995), Hoe(2001), Cheung (2002) and Melville et al. (2006). Threepossible failure mechanisms for cable-tied blocks usedas scour protection at bridge piers were identified(Parker et al., 1998). These are overturning and rollingup of the leading edge, uplift of the center of the mat,and winnowing of sediment between the mat and thebridge pier. The latter is more likely if the mat is notsealed tightly to the pier, or wing wall. Anchoring of theleading edge of the mat is important to preventoverturning and rolling up.

Design issues include primarily the block size, lateralextent, and edge treatment. Block size can be estimatedby the following equation:

2

1Fr

pa

yH

cb

cbb

(4)

Here, Hb is the height of the block, y is the flowdepth, acb is a constant equal to 0.1, cb is the blockdensity, is the fluid density, and Fr is the FroudeNumber.

CTB blocks are typically manufactured as atruncated pyramid shape with a square base and top.The spacing between CTB units should be adequate toallow the mattress to have a sufficient degree offlexibility, and that block shape should not inhibit matflexibility. Typically, synthetic filters are used beneathCTB mats. Lateral extent of the cable-tied blockmattress can be determined from:

W = 1.55(ds -db)(5)

Here W is the apron width, ds is the scour depth (=mat settlement depth) at the outer edge of the mat, anddb is the placement (burial) depth of the mat [Fig. 6].

Table 1. Riprap gradation for abutment protection Stone Size Range

Percentage of gradation smaller than

1.5D50 to 1.7D50 100 1.2D50 to 1.4D50 85 1.0D50 to 1.1D50 50 0.4D50 to 0.6D50 15

Fig. 6 Cable tied blocks at abutment


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To prevent the uplifting of the leading edge of themat, the size can be determined from

33.0

22

1158

ynFr

SyH

cb

b

(6)

Here Scb is the specific gravity of the blocks and nis the Manning coefficient. Care needs to be taken toensure that the leading edge of the mat remains buried.

Geobags

Geobags are bags of pervious material that are filledwith a pervious granular material (sand or gravel) thatare used as bank hardening elements, thereby possessingenough weight to hold sediment in place, but allowingthe flow of water through them to reduce upliftingpressure to reduce the likelihood of uplifting of the bagor winnowing of the fines underneath. The bag materialcan be a geosynthetic fabric such as the filter layer ofriprap discussed above. Design considerations includesizing, linking of bags, angle of placement and placementextent (Korkut et al., 2006).

Minimum size can be determined by that ofequivalent riprap as mentioned above. The individualbags should be tied together to help them function as asingle mattress thereby allowing flexibility to conformto the irregular bed shape. The geobag mattress shouldhave a maximum slope of 2H: 1 V with a toe extendinga downward length equal to at least 2 bags into theriverbed.

CONCLUSIONS

Although many studies have been carried out foreach of these countermeasures, there is still need forfurther studies of their use in protecting bridgeabutments. For instance, spur dikes have not specificallybeen tested and used for abutment protection. Guidebanks have been tested and used for spill-throughabutments on rivers with wide floodplains or rectangularchannel only. For small country bridges with wing-wallabutments terminating on the main channel bank, studiesare still needed. In addition, there are many controversialdesign guidelines and small design issues that need tobe clarified. Collars have been tested for pier scourprotection rather than for abutment protection.

Countermeasures are often damaged or destroyedby the stream, and stream banks and beds often erodeat locations where no countermeasure was installed.

However, as long as the primary objectives are achievedin the short-term as a result of countermeasureinstallation, the countermeasure installation can bedeemed a success (HEC-23). Therefore, to achievelong-term protection, maintenance, reconstruction, andinstallation of additional countermeasures as theresponses of streams and rivers to natural and man-induced changes is needed.

Among armouring countermeasures, riprap andcable tied blocks are of the most interest by hydraulicengineers. These two countermeasures have beeninvestigated by various researchers as erosion controldevices and bank revetments.

However, all these countermeasures are needed tobe tested for oblique flow, for their effectiveness incohesive soil and stratified beds. Also no workingrelationship for determination of maximum scour depthwith any of the above countermeasures on abutment,over a wide range of flow conditions and geometries isavailable. In addition, information on temporal variationof scour and location of deepest scour point in the flowpast abutments using most of these countermeasures isstill lacking.

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54. Rajaratnam, N. and Nwachukwu, B. A. 1983. Erosionnear groyne-like structures. Journal of HydraulicResearch, IAHR, 21 (4), 277 - 287.

55. Richardson, E. V. and Abed, L. 1993. Top width of pierscour holes in free and pressure flow. Proc. Nat. Conf.Hydraulic Engg. Part 1 (of 2), ASCE, July, pp. 25-30.

56. Richardson, E. V. and Lagasse, P. F. 1999. StreamStability and Scour at Highway Bridges. Compendiumof Papers ASCE Water Resources EngineeringConferences 1991 to 1998.

57. Richardson, E. V. and Simons, D. V. 1984. Use of spursand guide banks for highway crossing. Proc.Transportation Research Record, 2nd Bridge Engg.Conf. 2, 184.

58. Shields Jr., F. D. C., Cooper, C. M. and Knight. S. S.1995. Experiment in stream restoration. J. of HydraulicEngg, 121 (6), 494-502.

59. Simons, D. B. and Lewis, G. L. 1971. Flood protectionat bridge crossings. C.S.U. Civil Engineering Rep. No.CER71-72DBS-GL10, prepared for the Wyoming StateHighway Dept. in conjunction with the U.S. Dept. ofTransportation, Washington, D.C.

60. Simons, D. B. and Li, R. M. 1989. Sizing riprap for theprotection of approach embankments and spur dykesand limiting the depth of scour at bridge piers andabutments. Rep. No. FHWA-AZ- 89-260, Arizona Dept.of Transportation, Vol. 1, Pheonix.


9

61. Smith, C. D.1984. Scour Control at Outlook Bridge—aCase Study. Canadian Journal of Civil Engineering,11 (4), 709-716.

62. Soliman, M. M. K., Atria, K.M. A., Kotb, A.M. Talaatand Ahmed, A. F.1997. Spur dike effects on the riverNile morphology after high Aswan dam. Proc., CongoInt. Assoc. Hydraulic Research, Part-V, pp. 805.

63. Spring, F. J. E. 1903. River training and control of theguide bank system. Technical Paper No. 153, RailwayBoard, Government of India, New Delhi.

64. Suzuki, K., Michiue, M. and Hinokidani, O. 1987. Localbed form around a series of spur dikes in alluvialchannel.” Proceedings 22nd Congress, IAHR Lausanne,Switzerland, pp. 316-321.

65. Wong, W. H. 1982. Scour at Bridge Abutments. ReportNo. 275, Dept. of Civil Engineering, University ofAuckland, Auckland, New Zealand.

66. Tey, C. B. 1984. Local scour at bridge abutments. ReportNo. 329, University of Auckland, School of Engineering,Department of Civil Engineering, Private Bag,Auckland, New Zealand.

67. Tominaga, A, Nagao, M. and Nezu, I. 1997. Flowstructures and mixing processes around porous andsubmerged spur dikes. Proc. 27th Congress of the Int.Assoc. of Hydraulic Research. IAHR, Part B-1, pp. 251.

68. Wu, X. and Lim, S.Y. 1993. Prediction of maximum scourdepth at spur dykes with adaptive neural networks.Civil-Comp93, Part 3: Neural Networks andCombinatorial Optimization in Civil and StructuralEngineering Civil-Comp93, pp. 61.

69. Zaghloul, N. A. 1983. Local scour around spur-dykes.J. of Hydrology, 60, 123-140.

70. Zhang, Y. and Du, X.1997. Limited scour around spurdyke and the evaluation of its depth. J. Xi’an HighwayTransportation University, 17 (4), 56.

NOTATIONS

ac constant equal to 0.1C1 constant equal to 1.68 and 1.19 at the upstream

and downstream comers of the riprap layer,respectively

C2 constant the values for which are 1.2 and 1.0 forthe upstream anddownstream comers of the riprap layer

db depth of the riprap blanket bottom below theaverage channel bed level

ds depth of equilibrium scourD50 the median riprap sizeD100 largest size of the rip rap stoneFr Froude Number.g gravitational constantH maximum bed-form heightHb height of the cable tied-blockL abutment length perpendicular to the flow directionn Manning coefficientp porosity of cable-tied blocksScb specific gravity of the blocksSs specific gravity of the riprap materialt thickness of the riprap blanketU cross-sectionally averaged water velocityW apron widthWmin minimum riprap blanket extent across the channely flow depth fluid density

cb cable-tied block density shear stress at the bed

o approach shear stress


10

RAINFALL PATTERN IN NORTHERN KERALA

P. A. Lisha, P. K. Pradeep Kumar and K. V. Jayakumar

ABSTRACT

Available daily rainfall data for 81 years (1901 – 1969, 1990 – 1996 and 2001 – 2005) offive stations Kannur, Kozhikode, Quilandy, Vythiri and Vadakara in northern Kerala have beentransformed into annual, southwest monsoon, northeast monsoon and non monsoon componentsfor each year and analyzed for a study of the rainfall behaviour. Regression analysis of south-west monsoon rainfall and annual rainfall showed a variation in intercept from 734.60 to 1153.86and in slope from 0.87 to 1.07 with standard error of estimate varies from 282.93 to 389.82.Correlation analysis shows the variation in coefficient from 0.80 to 0.95. Maximum values ofannual and southwest monsoon rainfall yield a parallel behaviour. Average annual and south-west monsoon rainfall values also yield a parallel behaviour. Overall southwest monsoon rain-fall is 76.99% of annual rainfall in the area of study. The variation of annual and southwestmonsoon rainfall show a regular pattern in increase or decrease.

Key words: Kerala monsoons, Regression analysis, RFA, RFSWM, RFNEM, RFNM

INTRODUCTION

Kerala experiences rainfall for nearly nine monthsin a year in the form of monsoon rain andthudershowers. The fluctuation in annual rainfall ofKerala is much less than that compared with other partsof the Indian sub-continent. The hills and mountains ofthe Western Ghats located on the eastern boundary ofthe State provide orographic lifting for the southwestmonsoon winds resulting in heavy precipitation over thewestern slopes and good rain over midlands and lowlands. The average annual rainfall of the State is 3500mm. The southwest monsoon (June-September) is theprincipal rainy season as the State receives about 70%of its annual rainfall. Southwest monsoon rainfall aspercentage of annual rainfall decreases from north tosouth and varies from 83% in the northern most Districtof Kasargod to 50% at southern most District ofThiruvananthapuram. Northeast monsoon rainfall aspercentage of annual rainfall increases from north tosouth and varies from 9% in northern most District ofKasaragod to 27% in southern most District ofThiruvananthapuram.

Centre for Water Resources Development and ManagementP.O. Kunnamangalam, Kozhikode - 673 571, KeralaPaper No. 1228

Floods and landslides are a common occurrenceduring the rainy seasons in Kerala. Heavy rains duringsouthwest monsoon period (June-September) andnortheast monsoon (October – December) result inincreased risk of high floods. Rivers in Kerala aregenerally narrow and short, which flow down from theWestern Ghats through steep gorges and are thereforehighly susceptible to flash floods which cause immensedamage to the embankments, corps and cattle.Landslides give rise to blockages, snapping ofcommunications and damage to the property. It isessential to have a detailed knowledge on differentpatterns of rainfall during different seasons at aparticular region for various planning and executionpurposes. In that light, this particular study has beencarried out to know the different rainfall patterns atfive locations of northern Kerala. The outcome of theanalysis may help various developmental agencies intheir activities.

The interaction between topography andmeteorological elements involves several basiccharacteristics of any relief feature. The overalldimensions and the orientation of a mountain range withrespect to prevailing winds are important for large scaleprocesses, relative relief and terrain shape are



11

particularly important on a regional scale, while slopeangle and aspect cause striking local differentiation ofclimates. The effects that an orographic barrierproduces on air motion depend first on the dimensionalcharacteristics of the barrier – its height, length, widthand the spacing between successive ridges – and,second, on the properties of the airflow itself – the winddirection relative to the barrier, the vertical profiles ofwind and of stability. Each of the three dimensions of amountain barrier interacts with a particular atmosphericscale parameter (Smith, 1979). The air arriving at abarrier must have sufficient kinetic energy in order torise over it against the force of gravity (Stringer, 1972).

Cloud type in mountain areas is primarily determinedby air mass characteristics and is therefore related tothe regional climate conditions. The spatial distributionof convective upcurrents in mountain regions showssome pronounced effects of topography. There maybe strong contrasts between shaded and sunny slopes.Fujita et al (1968) reported rapid cumulus build-up onthe slope of Mt.Fuji between 0845 – 0915 hrs in July asthe solar altitude increased from 47° to 53° and surfacetemperature on the rocky slopes exceeded 30° C.Verma (1993) correlated the monsoon rainfall valueswith the gridded surface air temperature over northernhemisphere land at various time lags of months to identifyteleconnections of monsoon with the northernhemisphere surface air temperature anomalies. As perthe study, two regions in the higher latitudinal belt of40°N – 70°N over North America and Eurasia showpositive correlations with temperatures during northernwinter. The region located over northwest India andadjoining Pakistan show maximum positive correlationduring the pre-monsoon months of April and May.These relationships suggest that cooler northernhemisphere during the proceeding seasons of winter/spring over certain key regions are generally associatedwith below normal summer monsoon rainfall over Indiaand vice versa which could be useful for predictionsfor long – range forecasting of monsoon.

Systematic and scientific studies on variation ofprecipitation with elevation are limited mainly becauseof lack of sufficient information on the amount ofprecipitation at higher elevation. This is due to non -availability of automated recording precipitation gaugesand problem associated with measurement ofprecipitation at such higher elevation on a routine basis.Nearly 35% of the geographical area in India ismountainous. Of these nearly 58 % is accounted for

by the mighty Himalayas, extending from north-west toeast. Besides, the Khasi and Jayantiya hills in thenortheast, the Vindhya and Satpura hills in central India,the Western Ghats running all along the west coast fromMaharashtra to Kerala and the broken hill ranges ofEastern Ghats largely determine and guide the Country’srainfall pattern during the summer as well as winter.Isolated hill ranges like, the Aravalis and Nilgiris alsoinfluence the rainfall occurrence in those areas.

Dhar et al (1978) carried out a study of the heavyrainfall stations in India. For the purpose of the study,stations with mean annual rainfall of 500 cm wereconsidered as heavy rainfall stations. Some of the heavyrainfall stations lie in the Western Ghats and the restare located in the hills of northeast India. There are,however, none in the Himalayan region. There are somestations in the Darjeeling hills with short period meansover 500 cm. During the onset of the southwestmonsoon, the moisture laden monsoon winds firstapproach the Western Ghats and the Khasi Jayantiyahills and precipitate most of the moisture over theseregions. By the time they approach the Himalayanregions much of the moisture is lost and, therefore, theless rainfall in these areas.

Dhar and Bhattacharya (1976) made a study onthe variations of precipitation with elevation in theCentral Himalayas. A relationship between precipitationand elevation was obtained for the Central Himalayasusing 15 to 20 years data of more than 50 stations.Variation of rainfall with the elevation showed that thereare two zones of maximum precipitation. One near thefoot of the Himalayas and other at an elevation of 2.0to 2.4 km. For higher elevations beyond 2.4 km theprecipitation decreases sharply.

Climatologically the onset of the southwest monsoonover extreme south Kerala is 1st June. The onset,however, can take place earlier or later and in someyears there are multiple onsets when the initial onsettakes place too early. Multiple onsets mean that thereis a recession of the monsoon after the initial onset andanother onset of the current that to take place before itgets established. Between 1901 and 1985, the earliestonset is 11th May in 1918 and 1955 while the mostdelayed onset date is 18th June in 1972 (Menon andRajan, 1989). On 47 occasions out of 85 between 1901and 1985, the onset has taken place between 29th Mayand 7th June. During this period, the maximum numberof onset of southwest monsoon occurred on 1st June.


12

Ananthakrishnan et. al (1979), discussed in detailabout the meteorology of Kerala. It is suggested thatthe most outstanding feature of the meteorology ofKerala is the seasonal reversal of the wind circulationwhich constitutes the summer and winter monsoons.This seasonal reversal is linked with the seasonalreversal of temperature and pressure gradients followingthe apparent north-south movement of the Sun in thecourse of a year. The seasonal progressions oftemperature, pressure and winds at the surface and inthe upper air are illustrated and the significant pointshigh lighted. The rainfall time series of 75 rain recordingstations over Kerala for the 80 year period 1901 to 1980have been statistically examined by Soman et al (1988)for long term trends. Application of Mann – Kendallrank statistic test to the time series of annual andseasonal totals as well as extreme rainfall of 1,2,….,10day durations revealed a significant decreasing trend inthe rainfall over the eastern high lands and adjacentareas to the west. This finding is supported by the factthat the mean rainfall for the second half of the periodis 10 to 20% lower than for the first half over the samearea.

STUDY AREA

Five stations, Kannur, Kozhikode, Quilandy, Vythiriand Vadakara, in Northern Kerala (Fig.1) are selectedfor the study. The locations of the stations in regard totheir respective latitude and longitude are given in Table1.

DATA

Daily rainfall data of the five stations for 81 years(i.e., 1901 to 1969, 1990 to 1996 and 2001 to 2005)have been made available for the present study.

From these data, monthly rainfall data wascalculated and grouped into annual rainfall, southwestmonsoon rainfall, northeast monsoon rainfall and nonmonsoon rainfall.

The following are the short forms used in this paper:

RFA - Annual rainfall (January to December)

Table 1 Details of the raingauge stations Sl.No. Name of station Latitude Longitude

1 2 3 4 5

Kannur Kozhikode Quilandy Vythiri Vadakara

11° 52' 11° 15' 11° 27' 11° 33' 11° 36'

75° 22' 75° 47' 75° 42' 76° 02' 75° 35'

RFSWM - Southwest monsoon rainfall (June toSeptember)

RFNEM - Northeast monsoon rainfall (October toDecember)

RFNM - Non monsoon rainfall (January to May)

RFSWM component has been obtained by addingthe rainfall values of the southwest monsoon months,viz. June, July, August and September. RFNEM is thetotal of the rainfall values of the northeast monsoonmonths, viz. October, November and December. RFNMcomponent is the total rainfall values of non monsoonmonths, viz. January, February, March, April and May.All values of RFA, RFSWM, RFNEM and RFNM aregiven in mm.

ANALYSIS

RFSWM – RFA relationship

It is widely believed that the annual rainfall (RFA)of India, as a whole is a reflection of the total southwestmonsoon rainfall (RFSWM). On the average, theRFSWM is around 77% of the RFA of the country. Toexamine this contention in Northern Kerala, the 81 yearRFSWM – RFA data of each station is tested forregression and correlation. The regression constantsof intercept and slope and also the correlationcoefficients are found and given in Table 2. Thestandard errors of estimate about the regression lineare also calculated for each station and given in Table2.

Fig. 1 Locations of the selected raingauge stations on the reliefmap of northern Kerala


13

KOZHIKODE

RFSWM (x) in mm

4000 3000 2000 1000

RFA

(y) i

n m

m

6000

5000

4000

3000

2000

1000

Observed Linear

y =1153.86+0.87x r = 0.80

Table 2 RFSWM – RFA relationship values

Parameter Kannur Kozhikode Quilandy Vythiri Vadakara

Intercept 771.52 1153.86 788.79 868.91 734.60 Slope 0.95 0.87 0.99 0.98 1.07 Std. error of estimate 308.59 334.58 312.54 282.93 389.82 Correlation coefficient 0.88 0.80 0.91 0.95 0.86

The RFSWM has been found to correlate well withthe RFA in Vythiri (correlation coefficient = 0.95) whilethe correlation is less in Kozhikode (correlation coeffi-cient = 0.80). Also, as RFSWM is a part of RFA, RFA

increases with RFSWM and hence correlation coeffi-cient is positive at all stations. The regression lines foreach station are as shown in Figures 2a – 2e.

KANNUR

RFSWM (x) in mm

5000 4000 3000 2000 1000

RFA

(y) i

n m

m

6000

5000

4000

3000

2000

1000

Observed Linear

r = 0.88

y =771.52+0.95x

Fig. 2a Relationship between RFSWM and RFA at Kannur Fig. 2b Relationship between RFSWM and RFA at Kozhik

QUILANDY

RFSWM (x) mm

5000 4000 3000 2000 1000 0

RFA

(y) i

n m

m

6000

5000

4000

3000

2000

1000

Observed Linear

y =788.79 + 0.99x

r = 0.91

Fig. 2c Relationship between RFSWM and RFAat Quilandy

VYTHIRI

RFSWM (x) in mm

8000 7000 6000 5000 4000 3000 2000 1000

RFA

(y) i

n m

m

9000

8000

7000

6000

5000

4000

3000

2000 1000

Observed Linear

y =868.91 + 0.98x

r = 0.95

Fig. 2d Relationship between RFSWM and RFA at Vythiri


14

VADAKARA

RFSWM (x) in mm

5000 4000 3000 2000 1000

RFA

(y) i

n m

m

6000

5000

4000

3000

2000

1000

Observed Linear

y =734.60 + 1.07x

r = 0.86

Fig. 2e Relationship between RFSWM and RFA at Vadakara

Maximum and Minimum of RFA, RFSWM,RFNEM and RFNM

The maximum and the minimum of RFA, RFSWM,RFNEM and RFNM observed at the five stations duringthe period of study are given in Table 3 in mm alongwith the respective years of occurrence.

Considering maximum RFA at the five stations,maximum was 8233.2 mm at Vythiri during the year1961. The lowest value among maximum RFA was4962.3 mm at Kozhikode during the same year 1961.Among maximum RFSWM at these stations, maximumwas 6959.5 mm at Vythiri and the lowest value was

3918.1 mm at Kozhikode both during the year 1961.Considering the maximum RFNEM at the five stations,maximum was 1227.3 mm at Quilandy during the year1943 and the lowest was 808.7 mm at Vythiri duringthe year 1902. In the case of maximum RFNM at thefive stations, maximum was 1642.6 mm at Vadakaraduring the year 1942 and the lowest was 1010.7 mm atKannur during the year 2004.

Similarly, observing minimum RFA at the fivestations, the highest value was 2437.5 mm at Vythiriduring the year 1993 and the lowest value was 1056.9mm at Quilandy during the year 1964. Among minimumRFSWM, the highest value was 1637.8 mm at Vythiriduring the year 1918 and lowest value was 889.8 mmat Quilandy during the year 1964. In the case ofminimum RFNEM at the five stations, the highest valuewas 116.0 mm at Vadakara during the year 2003 andthe lowest value was 58.0 mm at Quilandy during theyear 1963. Considering minimum RFNM, the highestvalue was 72.9 mm at Kozhikode during the year 1917while the stations Quilandy, Vythiri and Vadakara hadonly 0 mm rain, all during the year 1990.

Figure 3 represents the variation in the maximumand minimum values of RFA, RFSWM, RFNEM andRFNM at the five stations.

From the figure it is clear that maximum RFA andmaximum RFSWM show a parallel behaviour whichoccurred in the same years.

Table 3 Maximum and minimum of the respective groups and its year of occurrence Type Group Kannur Kozhikode Quilandy Vythiri Vadakara

RFA 5660.5 4962.3 5227.0 8233.2 5846.9 YEAR 1961 1961 1959 1961 1907 RFSWM 4663.3 3918.1 4349.5 6959.5 4486.7 YEAR 1961 1961 1959 1961 1907 RFNEM 920.6 951.5 1227.3 808.7 970.5 YEAR 2002 1932 1943 1902 1932 RFNM 1010.7 1206.0 1163.8 1100.0 1642.6

MAX (mm)

YEAR 2004 1933 1955 2004 1942 RFA 1497.3 2311.9 1056.9 2437.5 1597.1 YEAR 1968 1911 1964 1993 2003 RFSWM 1270.9 1069.3 889.8 1637.8 1379.2 YEAR 1968 1918 1964 1918 1918 RFNEM 79.2 68.8 58.0 62.7 116.0 YEAR 1968 1906 1963 1968 2003 RFNM 11.7 72.9 0.0 0.0 0.0

MIN (mm)

YEAR 1934 1917 1990 1990 1990


15

0

500

1000

1500

2000

2500

3000

3500

4000

4500


STATIONS

Ra

infa

ll in

mm

AVE RFA

AVE RFSWM

AVE RFNEM

AVE RFNM

0

1000

2000

3000

4000

5000

6000

7000

8000

9000


STATIONS

Ra

infa

ll in

mm

M A X R FAM A X R FSWM

M A X R FNEMM A X R FNM

M IN RFAM IN RFSWMM IN RFNEM

M IN RFNM

Fig. 3 Variation in maximum & minimum of RFA, RFSWM, RFNEM and RFNM at the five stations

It is significant to observe that the maximum valuesof non monsoon rainfall exceed maximum RFNEMexcept at Quilandy. These values also exceed minimumRFA at Quilandy and Vadakara and exceed minimumRFSWM except at Kannur and Vythiri. These valuesexceed minimum RFNEM at all the five stations.Considering the maximum values it is evident that nonmonsoon rainfall values cannot be neglected towardsthe assessment of rainfall resources in this region.

Averages and percentages

The average RFA, RFSWM, RFNEM and RFNMvalues observed at the five stations are given in

Table 4. and fig. 4 The percentages of RFSWM toRFA at these stations are also given in the Table.

The percentage value of the RFSWM to RFA rangesfrom 73.33% at Kozhikode to 81.37% at Vythiri. Theoverall average percentage comes to 76.99%.

The overall average values of RFA, RFSWM,RFNEM, and RFNM were found to be 3494.4 mm,2698.2 mm, 409.9 mm and 386.3 mm respectively.

Figure 4 shows variation in the averages of RFA,RFSWM, RFNEM and RFNM at the five stations.

Fig. 4 Variation in average of RFA, RFSWM, RFNEM and RFNM at the five stations

Table 4 Average RFA, RFSWM, RFNEM and RFNM and % of RFSWM w.r.t. RFA

Station Ave. RFA (mm)

Ave. RFSWM (mm)

Ave. RFNEM (mm)

Ave. RFNM (mm)

% RFSWM / RFA

Kannur 3247.3 2601.0 347.4 298.8 80.10 Kozhikode 3166.8 2322.3 441.3 403.1 73.33 Quilandy 3255.3 2485.2 414.4 355.6 76.34 Vythiri 4278.5 3481.6 436.9 360.0 81.37 Vadakara 3524.2 2600.7 409.7 513.8 73.80 Average 3494.4 2698.2 409.9 386.3 76.99


16

Fig. 5 Variation in annual rainfall at the five stations

Fig. 6 Variation in southwest monsoon rainfall at the five stations

Fig. 7 Variation in northeast monsoon rainfall at the five stations

Fig. 8 Variation in non monsoon rainfall at the five stations


17

The variations in RFA, RFSWM, RFNEM and RFNMover the 81 years at the five stations are presented inFigures 5, 6, 7 and 8 respectively. The lines showingthe variations in the annual and southwest monsoonrainfall values at the five stations over the 81 yearsbroadly present a regularity in increase or decrease in

Table 5 Rainfall frequency distribution (RFA) Kannur Kozhikode Quilandy Vythiri Vadakara

<1000 0 0 0 0 0 1000 - 2000 1 0 2 0 1 2000 - 3000 27 33 30 3 18 3000 - 4000 45 40 37 28 43 4000 - 5000 7 8 10 39 16 5000 - 6000 1 0 2 8 3

>6000 0 0 0 3 0

their respective values at all the stations indicative of auniform pattern.

Rainfall frequency distribution

The frequency distribution of annual rainfall at thefive stations is presented in Table 5. The rainfall valuesand ranges are given in mm.

0

5

10

15

20

25

30

35

40

45

50

<1000 1000 - 2000 2000 - 3000 3000 - 4000 4000 - 5000 5000 - 6000 >6000RAINFALL RANGE

NO

. O

F Y

EA

RS

Kannur

Kozhikode

QuilandyVythiri

Vadakara

Fig. 9 Frequency distribution of annual rainfall at the five stations

Table 6 Southwest monsoon rainfall frequency distribution

Kannur Kozhikode Quilandy Vythiri Vadakara <1000 0 0 1 0 0

1000 - 2000 11 23 17 2 9 2000 - 3000 53 50 48 22 56 3000 - 4000 16 8 13 43 14 4000 - 5000 1 0 2 10 2 5000 - 6000 0 0 0 3 0

>6000 0 0 0 1 0

From Table 5, it can be seen that 70 to 81 of theyears (ie, 86.4% to 100%) are covered by annual rainsranging from 2000 mm to 5000 mm. For the entirezone, there are only 3 rainfall values exceeding 6000mm, viz. 6022.6 mm, 6420.1 mm and 8233.2 mm all atVythiri during the years 1923, 1924 and 1961respectively. There are four annual rainfalls whichare less than 2000 mm, viz. 1912.9 mm during 1963 and1056.9 mm during 1964 both at Quilandy, 1497.3 mm

during 1968 at Kannur and 1597.1 mm during 2004 atVadakara. The 3000-4000 mm range of the annualrainfall values covers all the five stations during morethan 28 years of the 81 years of observations. Figure 9shows the frequency distribution of total annual rainfallat the five stations.

Table 6 illustrates the frequency distribution of theoccurrence of southwest monsoon rainfall at the fivestations.

NO

. OF

YEA

R


18

During the southwest monsoon season of the studyperiod there is only one incident having the value lessthan 1000 mm, i.e.889.8 mm at Quilandy during the year1964. Also, there is only one RFSWM value higher

0

10

20

30

40

50

60

<1000 1000 - 2000 2000 - 3000 3000 - 4000 4000 - 5000 5000 - 6000 >6000RAINFALL RANGE

NO

. O

F Y

EA

RS

Kannur

Kozhikode

Quilandy

Vy thiri

Vadakara

than 6000 mm, i.e. 6959.5 mm at Vythiri during the year1961.

Figure 10 shows the frequency distribution ofsouthwest monsoon rainfall at the five stations.

Fig. 10 Frequency distribution of southwest monsoon rainfall at the five stations

Figure 10 gives the fact that in all the years there issouthwest monsoon rainfall ranging from 1000 to 5000mm at all the stations except at Quilandy having rainless than 1000 mm and at Vythiri having rain higher

than 6000 mm.

The frequency distribution of northeast monsoonrainfall at the five stations is given in Table 7.

Table 7 Northeast monsoon rainfall frequency distribution

Kannur Kozhikode Quilandy Vythiri Vadakara <200 15 4 11 5 9

200 - 400 36 32 25 30 33 400 - 600 26 29 36 33 29 600 - 800 3 14 7 12 7 800 - 1000 1 2 1 1 3

>1000 0 0 1 0 0

All the stations have RFNEM value less than 200 mm.Also, all the stations have covered within 78 to 80 yearsof total 81 years in the range between 0 mm and 800mm. There is only one RFNEM value higher than 1000

mm, i.e. 1227.3 mm at Quilandy during the year 1943.

The frequency distribution of northeast monsoonrainfall at the five stations is given Fig. 11.

0

5

10

15

20

25

30

35

40

<200 200 - 400 400 - 600 600 - 800 800 - 1000 >1000RAINFALL RANGE

NO

. O

F Y

EA

RS

Kannur

Kozhikode

Quilandy

Vythiri

Vadakara

Fig. 11 Frequency distribution of northeast monsoon rainfall at the five stations

NO

. OF

YEA

RN

O. O

F YE

AR


19

Table 8 Non monsoon rainfall frequency distribution Kannur Kozhikode Quilandy Vythiri Vadakara

<200 36 18 20 14 14 200 - 400 28 36 37 46 18 400 - 600 5 8 12 9 26 600 - 800 7 11 6 6 9

800 - 1000 4 6 4 5 8 >1000 1 2 2 1 6

0

510

15

2025

3035

4045

50

<200 200 - 400 400 - 600 600 - 800 800 - 1000 >1000RAINFALL RANGE

NO

. O

F Y

EA

RS

Kannur

Kozhikode

Quilandy

Vy thiri

Vadakara

Fig. 12 Frequency distribution of non monsoon rainfall at the five stations

Table 8 indicates the frequency distribution of nonmonsoon rainfall at the five stations, of which thegraphical representation is Fig. 12.

During non monsoon period Kannur, Quilandy andVythiri cover 69 years out of the total 81 years (85% ofthe years) having the range less than 600 mm rainfallwhile Kozhikode and Vadakara cover 62 years (76.5%)of the 81 years and 58 years (71.6%) of the 81 yearsrespectively by the same range.

CONCLUSIONS

The study was carried out to find out the rainfalldistribution at the five stations of northern Kerala duringdifferent periods; annual, southwest monsoon, northeastmonsoon and non monsoon periods. The analysis wasdone making use of 81 years of daily rainfall data.

Regression analysis of the southwest monsoonrainfall with respect to annual rainfall of 81 years yieldintercept values varying from 734.60 to 1153.86 andslopes varying from 0.87 to 1.07. For each relation,standard error of estimate was also calculated.

Correlation analysis of RFSWM – RFA gives thecoefficients varying between 0.80 and 0.95. This showsvery close relationship between them.

The highest of the maximum annual rainfall (RFA)observed was 8233.2 mm at Vythiri during the year 1961and the lowest of the minimum non monsoon rainfall(RFNM) observed was 0 mm at Quilandy, Vythiri andVadakara all during the year 1990.

Maximum annual rainfall and southwest monsoonrainfall show a parallel behaviour and fall during thesame years.

The overall average RFA, RFSWM, RFNEM andRFNM were found to be 3494.4 mm, 2698.2 mm, 409.9mm and 386.3 mm respectively. Also overall southwestmonsoon rainfall was 76.99% of the overall annualrainfall.

The variations of annual rainfall and southwestmonsoon rainfall at the five stations for the 81 yearsshow a parallel behaviour in increase and decrease ofvalues.

The frequency distributions vary depending uponthe groups of the study. There is only one RFSWMvalue higher than 6000 mm and only one RFNEM valuehigher than 1000 mm.

ACKNOWLEDGEMENTS

The authors express their sincere and heartful

NO

. OF

YEA

R


20

thanks to Mr.C.K.Gopinathan for all the help and supportextended for the study. The immense help rendered byMs.E.Deepa during the analysis of data and computerworks is also gratefully acknowledged.

Due acknowledgement is given to IndiaMeteorological Department for the source of data usedin this study.

REFERENCES

1. Ananthakrishnan, R., Parthasarathy, B. and Pathan,J.M., 1979. Meteorology of Kerala. Contribution toMarine Sciences Dedicated to Dr. C.V. Kurian, pp. 60-125.

2. CWRDM, 1995. Water Atlas of Kerala. Centre for WaterResources Development and Management, Kozhikode.

3. Dhar, O.N. and Bhattacharya, B.K., 1976. Variation ofrainfall with elevation in the Himalayas- a pilot study.Indian Journal of Power and River Valley Development,26, 179-185.

4. Dhar, O.N., Nandal, B.N. and Ghose, G.C., 1978. Heavyrainfall stations of India. Indian Journal of Power andRiver Valley Development, 25, 123-134.

5. Fujita, T., Baralt, G. and Tsuchiya, K., 1968. Aerialmeasurements of radiation temperatures over Mt. Fujiand Tokyo areas and their application to thedetermination of ground and water – surface

temperatures. Journal of Applied Meteorology, Vol.7,pp. 801-816.

6. Gulezian, R.C., 1979. Statistics for decision making.W.B.Saunders Company, Philadelphia.

7. Jagannadha Sarma, V.V., 2005. Rainfall pattern in thecoastal zone of Krishna Godavary Basin, AndraPradesh, India. Journal of Applied Hydrology, Vol.XVIII, (1 & 2), 1 – 11.

8. Menon, P.A. and Rajan, C.K., 1989. Climate of Kerala.Classic Publishing House, Cochin.

9. Muthuchami, A. and Ravikumar, P.V., 1992. The inverserelationship between the activity of monsoon systemsand the intensity of southern hemispheric equatorialtrough. Vayu Mandal, 22 (1-2), 40-45.

10. Smith, R.B., 1979. The influence of mountains on theatmosphere. Advances in Geophysics, 21, 87-130.

11. Soman, M.K., Krishna Kumar, K. and Nityanand Singh,1988. Decreasing trend in the rainfall of Kerala.Current Science. 57 (1), 7-12.

12. Stringer, E.T., 1972. Foundations of climatology. W.H.Freeman and Co., San Francisco, pp. 141-167.

13. Verma, R.K., 1993. Variability of Indian summermonsoon, relationship with surface air temperatureanomalies over northern hemisphere. Mausam, 44,191-198.


21

SUSTAINABLE MANAGEMENT OF THE WATER STRESSED AQUIFERS INSABARMATI BASIN GUJARAT, INDIA

R. C. Jain1 and A. K. Sinha2

ABSTRACT

The Sabarmati basin is underlain by a multi-aquifer system, with prolific yields but theexcessive groundwater pumping over many years has resulted in de-watering of a substantialaquifer thickness and reduction in pressure heads as reflected in long term decline in groundwater levels. The ground water use in the basin being practiced currently is consideredunsustainable as the ground water draft far exceeds the replenishable recharge. For providingsustainability to ground water withdrawal structures and keeping in view the increasing thrust ondevelopment of ground water resources, there is an urgent need to address the problem of groundwater depletion through both supply side as well as demand side management interventions .Based on the in depth analysis of hydrogeology, long term water level trends , aquifer parametersand the availability of surplus runoff a strategy for managed aquifer recharge (MAR) has beenformulated to augment the ground water resources of the water stressed aquifers. Typical designsof different types of structures suitable for artificial recharge have been included based on thelessons learnt. The paper presents a matrix of recommendations for implementing MAR in thebasin through various stakeholders. For demand side management it is essential that groundwater development is controlled and regulated through appropriate legal and administrativemeasures in view of the fast emerging aquifer depletion in large part of Basin.

Key words : long term decline, managed aquifer recharge, unsustainable, management inter-ventions, virtual water.

INTRODUCTION

Sabarmati river basin is located in the western partof India (Fig.1). The basin in its western ,central andsouthern parts is underlain by a multi-aquifer system,with prolific yields but the excessive groundwaterpumping over many years has resulted in de-wateringof a substantial aquifer thickness and reduction inpressure heads. Agriculture production has beenseriously affected due to conspicuous decline in waterlevels caused by over- exploitation of groundwater(Jain, et al,2001).

The basin is more vulnerable for such adverseeffects due to low rainfall reliability and recurrence ofdroughts.

The detrimental effects of over exploitation ofgroundwater i.e., decline in water levels have not only1

West Central Region, Central Ground Water Board, Ahmedabad.2

Deptt. of Geology, University of Rajasthan, Jaipur.Paper No. 1239

added to the capital cost of construction of the tubewells and the energy bill but have also added to annualcost of maintenance of tube wells (INREMF,2001).Conventional dug wells and dug-cum-bored wells arealready, gradually going out of operation and in manyareas the dug wells have dried up since many years.Further, during drought years sustained ground waterwithdrawals due the pumpage of groundwater farexceeds that of normal years resulting in permanentde-watering of aquifers (Jain, et al,2000).

In fact because of economic factor, farmers arelargely growing the cash crops in the area and are takinghigh risks in construction of the deep tube wells evenwith a reduced life span due to lowering in water levelsand consequent reduction in discharge, higher and highermaintenance cost. Large financial investments made inconstruction of tube wells need to be protected fromimplied hazards of over exploitation of ground water.



22

Magnitude of ground water depletion

Because of increased pace of development duringlast three decades for meeting the increasing demandsfor irrigation, drinking water supplies and industrial needsthe dependence on ground water has tremendouslyincreased (Govt. of Gujarat,2005). The increase in theground water draft has been mainly due to increasedpumping for meeting the ever increasing water demandof irrigation, drinking water and industrial sector. Thisis clearly evident from a perusal of few representativehydrographs shown in figure 2 which very clearly depictthe alarming declines ranging from more tahn 5 to 15 min the shallow aquifers and from more than 20 up to 60m in the deeper aquifers during the last one decade orso. (Jain,2008)

Fig. 1 Location map of Sabarmati Basin

During the early sixties the tube wells were drilled between60 and 100 m and their water levels ranged between 10 and 15m bgl. These tube wells have also gone out of use. The tubewells being drilled now are between 250 and 300m depth withthe water levels ranging between 80 to more than 120 m bgl.(GOG,2002 ).

The adoption of high yielding crops for boosting theagricultural production ,fodder production to meet thedemand for dairy cattle and the impetus in rural electrificationhas resulted in substantial increase in ground water pumping.Earlier through conventional open well with discharge 4-6m3/hr. an area of 0.4 to 0.6 ha could be irrigated. Now withenergisation of open wells and construction of deep tubewells the average ground water abstraction rates have goneas high as 90 to 112 m3/hr. with command areas ranging from40 to 60 ha ( Jain,2003).

Groundwater Resource Availability Scenario

In the Sabarmati River basin the net ground wateravailability is 2342.59 MCM/yr. The gross ground waterdraft has increased from 1580 MCM/yr as in1991(CGWB,1995) to an alarming level of 2415 MCM/yr as in 2004 (CGWB,2006). As per the latest groundwater resource computations a negative balanceof (-)72.37 MCM/yr is indicated. The stage of groundwater development has increased from nearly 60% ofreplenishable recharge in 1991 in the basin to >103%of replenishable recharge in 2004 over a period of lastone and a half decade. A comparative depiction ofchange in stage of ground water development inimportant talukas in the basin is shown in Fig.3.

Based on overall stage of ground waterdevelopment of 103.09 % of replenishable recharge ,the basin is categorised as OVER EXPLOITED .Thepresent status of development is unsustainable as theaquifers are under stress .the stage of ground waterdevelopment varies widely from as low as 28% ofreplenishable recharge as in Anand taluka in the southernpart of the basin to nearly 230% replenishable rechargeof in Mansa taluka in western part of the basin (2004).

Groundwater depletion – Management options

The Sabarmati river basin has the problem ofintensive ground water extraction / overexploitationleading to long term decline in water levels in thenorthern, central and eastern part ,while in the southernpart the level of ground water extraction is comparativelylow due to availability of ample surface water irrigationfacilities through the imported water from the MahiRiver Basin. At present the gross irrigated area in theSabarmati basin is 7,89,500 ha., which will increase to12,43,500 ha by the year 2025 (ICID,2005).

The total mean annual surface water resources inGujarat part of the basin are estimated at 2018.20 MCMincluding 150 MCM from neighbouring states. Furtheras per Plans of the Gujarat Govt. surface water potentialto the extent of 1587.13 MCM is committed , leavingnearly 431.07 MCM untapped , which accounts for 21%of mean annual runoff in the Sabarmati Riverbasin(CDO,2003).

For providing sustainability to ground waterwithdrawal structures in Sabarmati River basin andkeeping in view the increasing thrust on developmentof ground water resources for meeting the growing


23

Fig. 2 Hydrographs of select observation wells in Sabarmati Basin


24

demands of water in agriculture, industrial and domesticsectors, there is an urgent need to adopt both the supplyside and demand side management interventions toaugment and conserve the depleting ground waterresources in the active recharge zone.

Managed aquifer recharge - Supply sidemanagement

Natural replenishment of ground water reservoir isslow process. It is unable to keep pace with theexcessive continued exploitation of ground waterresources . In order to augment the natural supply ofground water, managed aquifer recharge ( MAR) isan important and frontal management strategy whichcan provide succor to address the problem of groundwater depletion (Jain& Sinha,2004). MAR is theprocess by which the ground water reservoir isaugmented at a rate exceeding that under naturalconditions of replenishment. The recharge efforts arebasically augmentation of natural movement of surfacewater into ground water reservoir through suitable civilstructures under suitable hydrogeological and hydrologicconditions. As the rainfall occurrence in the basin islimited to about 10 to 22 days during three monthsmonsoon period,the natural recharge to ground waterreservoir is restricted to this period only. The Managedaquifer Recharge aims at increasing the rechargeperiod in the post-monsoon season by about 3 moremonths . This results in providing sustainability to groundwater development during the lean season.

The efficacy of artificial recharge schemes dependslargely on the source water availability and capabilityof ground water reservoir to accommodate it ,whichrequires detailed knowledge of geological andhydrological features of the area for site selection and

design of artificial recharge structures(Kovalevsky, etal, 2004). While assessing the availability of source water,which is one of the prime requisites for ground waterrecharge, careful consideration has to be given tocommitted monsoon storages, so that the harvesting ofnon committed surplus monsoon run off from the areadoes not have adverse environmental impact like dryingup of existing lakes/ponds/reservoirs due to reductionin inflows from their catchments. Detailed knowledgeof dimensional data of the aquifer viz. their thicknessand lateral extent is necessary for evaluation of thestorage potential. The availability of sub-surface storagespace and its replenishment capacity governs the extentof recharge.

Identification of areas feasible for managed aquiferrecharge in Sabarmati Basin

The basin is having total area of 22,260 sq.kms inGujarat. The geological formations in the basin arealluvium, igneous, metamorphic and other soft rocks. Inthe major part of the basin alluvium consisting of re-worked aeolian sand, silt, kankar and clay forms theprincipal aquifer (Fig. 4).

The unsaturated thickness of rock formations,occurring beyond eight metres below ground level hasbeen considered to assess the requirement of water tobuild up the sub-surface storage by saturating the entirethickness of the vadose zone to 8 metres below groundlevel. The upper 8 m of the unsaturated zone is notconsidered for MAR; in view of the prevailinghydrological, hydrogeological, geomorphic and

Fig. 4 Hydrogeological map of Sabarmati River basin

Fig. 3 Stage of ground water development in Sabarmati Riverbasin during 1991-2004.


25

topographic conditions. The post-monsoon depth towater level represents a situation of minimum thicknessof vadose zone available for recharge which can beconsidered with reference to surplus monsoon run offin the area.

The average post monsoon water level (1998-2007)ranges between less than 8 and 35 mbgl in the Sabarmatibasin (Fig 5). The quality of ground water is good in

general except the western part which is saline havingTDS more than 2500 mg/l (Fig 6). Based on the quality,post monsoon water level and the declining trend thefeasible areas for managed aquifer recharge areidentified in the northern and western parts of the basin(Fig.7). The south, central and the western part ofbasin having post monsoon decadal average depth towater level less than 8mbgl and saline ground waterare not considered feasible for MAR.

It is estimated that an area of 14,517 Sq.km. havingdepth to water table more than 8 m bgl, spread over allthe geological formations is feasible for managed aquiferrecharge through different type of recharge structuresat appropriate sites. The total volume of vadose zoneof this area is 41,348 MCM. Further in an area of 464sq.kms the declining trend of water level is observedand the volume of vadose zone in this area is 66.93MCM. Thus the total volume of de-saturated zonefeasible for recharge in the basin is 41,414 MCM.Considering percentage of clay in alluvium, coefficientreplenishment of different formations, it is estimatedthat about 3,534 MCM of water is required for rechargeto bring the water level up to 8 m bgl .

The non-committed monsoon flow of 431 MCM/annum could be used as source water for managedaquifer recharge for augmenting the storage in thestressed aquifers (UNDP,1988). This surplus runoff canbe utilised for MAR through proven techniques ofrecharge like injection wells, dug wells, check dams,spreading channels and percolation tanks in the area(Jain, 2003). Based on an average recharge rate of0.088 MCM/annum for a check dam/percolation tank,0.015MCM/annum for injection wells and 0.019MCM/annum for dug wells it is estimated that 13,300 Dugwells, 4,700 injection tube wells/connector wells, 500percolation tanks and 1,400 check dams are needed fortransferring the non-committed runoff to the stressedaquifer. This will cost about Rs. 1400 Million. All thestructures are not required to be constructed afresh, Infact , a large number of existing dug wells /tube wellscan be provided with suitable siltation/filtration chambersfor removing the physical impurities in source waterderived from monsoon flows. Similarly the deepeningof existing ponds for increased storage and percolationinto the aquifer would also be very useful , as creationof new surface storages adds more to the evaporationlosses than to percolation to the underlying aquifers .Thespreading channels are feasible in the limited area ofcommon recharge zone at the foot hill of Arvalli’s in

Fig. 5 Average post monsoon depth to ground water level (1998-2007) in Sabarmati Basin

Fig. 6 Ground water quality in Sabarmati Basin duringpre-monsoon 2007

Fig. 7 Areas feasible for artificial recharge to ground water inSabarmati basin


27

Fig. 11. Typical design of an injection well in alluvial area

6000 MM

2000 MM

1500 MM 1500 MM

MAN HOLE COVER

400 MM DIA INJECTION WELL

150 MM DIA PVC CASING PIPE UPPER 10 M

BLANK & LOWER 35 M STAINER

(6KG / CM2)

15 MM DIA AIR VENT PIPE 30 M LONG WITH 5 MM DIA HOLES IN LOWER 10 M LENGTH PLAN

2500 MM

2000 MM

6250 MM

2600 MM

15 MM DIA AIR VENT PIPE 30 M LONG WITH 5 MM DIA HOLES IN LOWER 10 M LENGTH

400 MM DIA INJECTION WELL

150 MM DIA PVC CASING PIPE UPPER 10 M

BLANK & LOWER 35 M STAINER

(6KG / CM2)

ANNULUS WITH 10 TO 20 MM Ø PEBBLE IN SPACE

6000 MM RCC M 20 GRADE SLAB

PCC 1:3:6, 150 MM THICK

1500 X 2000 X 1500 MM SIZES FILTER CHAMBERS, ONE FILLED WITH COARSE SAND & ANOTHER FILLED WITH PEBBLES OF 10 TO 20 MM DIA SIZE. ALL THE WALLS ARE B. M. WALLS IN C. M. (1:3), 230 MM THICK. PROVIDE 3 PIPES 150 MM DIA WITH PIPE CAP HAVING HOLES ON ENTRY SITE.

SECTIONAL VIEW

2800 MM

SOURCE WATER DRAIN

CONNECTING PIPE 150 MM DIA WITH CONTROL VALVE

SCUM BOARD


28

north eastern part of the area. Typical design of varioustype of structures is given in Fig.8,9,10 &11.

As the predominant contribution of recharge todeeper aquifers is through the vertical leakage fromthe aquitards underlying the shallow aquifers, therecharge of surplus monsoon water in the shallow aquiferwill benefit both the phreatic as well as deeper aquifers.This will go a long way in arresting the rate of declinein ground water levels and providing sustainability tothe fast depleting stressed aquifers (Jain & Sinha,2002).

Demand side management - Regulation andcontrol of ground water development-

As the available ground water resources are finiteand aquifer depletion is fast emerging in large part ofSabarmati basin ,it is essential that ground waterdevelopment is controlled and regulated throughappropriate legal and administrative measures. To beginwith any additional abstraction from the deeper aquifersshould be banned, because the ground water resourceof deeper aquifers get replenished over large no. ofyears unlike the shallow aquifer which gets replenishedevery year(World Bank,1999). The in-storage reservesof ground water in the deep multi-aquifer system shouldbe treated as strategic resource to be utilised in timesof emergency only (UNDP,1976). However, exceptioncan be made for drinking water supply ,where a limitedquantity of withdrawal can be permitted not exceedingsafe yield of the aquifer. Fortunately, Gujarat state hasa State Ground Water Authority in place, but the needof the hour is to control and regulate the ground waterabstraction by effective implementation of the groundwater legislation.

In addition to the requirement of water for dairydevelopment, fodder for the cattle is also grown whichleads to over exploitation of ground water. If the fodderrequirement could be met from agriculture productionin the central and southern part of Gujarat having lowstage of ground water development, which can then betransported to over exploited areas .Thus a largequantity of ground water can be conserved in the formof virtual water.

More emphasis should be laid on adoption ofmicro-irrigation technology like drip irrigation etc,which will help in conservation of the precious resource.

Strategy for implementation of MAR

By far the most critical response to stressed aquifersin the Sabarmati Basin demands exploring synergiesfrom a variety of players for the basin wide rechargeprogram. Evolving a groundwater recharge strategyneeds to begin with an appreciation of the variety ofactors that can contribute through different kinds ofrecharge structures as suggested in the Table 1.

Public agencies with strong science and engineeringcapabilities need to play a major role in constructingand managing large recharge structures (PlanningCommission,2007). However, an intelligent strategy canalso involve millions of farmers and householders andthousands of their communities—each of whom cancontribute small volumes to recharge dynamicgroundwater. The Table 1 indicates who can play whatrole to achieve the purpose of resource augmentationunder prevailing conditions.

Table 1. Actors and their roles in implementation of MAR

Actors

Aquifers affected

Key players Numbers of actors who can contribute

Recharge Volumes/ structure

Location of structures

Small structures for recharging wells and roof-water harvesting structures

Dynamic groundwater in alluvial and hard-rock areas

Individual farmers and urban citizens

millions 100-5000 m3 Private farm lands and homes

Check dams, percolation tanks, Sub-surface dykes, etc

Dynamic groundwater in alluvial and hard-rock areas

Communities using a common aquifer system

Tens of thousands

100,000-5,000,000 m3

Common-property or government land

Large structures on government land for recharge to confined aquifers

Confined aquifers: large alluvial aquifers

Public agencies with hydro-geology expertise;

few 0.1 to 1 km3 or more

Government waste lands or forest lands


29

In view of the criticality of groundwater recharge,a Special Purpose Vehicle (SPV) needs to be createdfor overseeing private and NGO-implementedgroundwater recharge programs as well as forexecuting, operating and maintaining large-scalegroundwater recharge program. Besides the scientifictalent such a SPV needs to build engineering andmanagement capacity needed for the purposes on hand.

In our country water policy has so far focussed onwhat governments and government agencies can do.Now, it needs to target networks of players, each with

distinct capabilities and limitations. If MAR is to be amajor response to provide succour to the stressedaquifers in the Sabarmati River Basin , the state needsto evolve an integrated groundwater recharge strategy(Jain,& Sinha, 2004)and work with role and space forvarious players to contribute as outlined in theTable 2.

Acknowledgements : The author acknowledges theChairman, Central Groundwater Board, Govt. of Indiafor the permission to publish this paper .

Table 2. Outline of an MAR Strategy for Sabarmati River Basin

Key actors Arid alluvial aquifer areas

Hard rock aquifer areas

Roles that need to be played by State Govt, Recharge SPV, other public agencies Vigorous Information, Education, Communication campaign to promote MAR to stressed aquifers through dug wells Technical support in construction of desiltation/filtration pits for recharge, and periodic desiltation of wells

Farmers Dug wells, roof-water harvesting; other private recharge structures

Dug wells, farm ponds, roof-water harvesting; other private recharge structures

Financial incentives and support to farmers adopting MAR

Technical and financial support to local communities, NGOs for construction and maintenance

Supportive policy environment and incentive structures

NGOs, local communities

Percolation ponds, check dams, sub-surface dykes on streams

Support for building local institutions for groundwater recharge Create a Special Purpose Vehicle to execute, operate and maintain large-scale recharge structures

Groundwater recharge SPV

Recharge canals to transport surplus flood waters for recharge in groundwater-stressed areas e.g., Sujalam- Sufalam in North Gujarat Large recharge structures in recharge zones of confined aquifers

Build and operate large-scale recharge structures in upstream areas of confined aquifers. e.g. at the base of Aravalli’s in North Gujarat

REFERENCES

1. Central Designs Organisation, 2003. Report onIntegrated river basin planning, development andmanagement of Sabarmati River basin. Govt. of Gujarat.

2. Central Ground Water Board, 2002. Master Plan forArtificial Recharge to Ground Water in India, Ministryof Water Resources, Government of India.

3. Central Ground Water Board, 2006. Dynamic GroundWater Resources of India, Ministry of Water Resources,Government of India.

4. Central Ground Water Board, 2007. Ground water YearBook, Tech. Report, CGWB, WCR, Ahmedabad.

5. Government of Gujarat, 2000.White paper on water inGujarat , Narmada Water Resources and Water SupplyDepartment.

6. Government of Gujarat, 2005. Report of the Committeeon Estimation of Ground water resources andIrrigation Potential in Gujarat. Narmada and WaterResources Department.

7. Indian Natural Resource Economics and managementFoundation, 2001. Integrated water resourcesmanagement in Sabarmati Basin: Some issues andoptions.

8. International commission on Irrigation and Drainage,2005, Water Resources Assessment of Sabarmati RiverBasin, India.

9. Jain,R.C., Nagar,A., Jain,P.K. and Krishna, V.S.R.,2000.Declining Water levels in deep aquifers of Gujarat. Tech.Report ,CGWB, WCR, Ahmedabad.

10. Jain, R.C., Jain,P.K., Jain,A.K. and Krishna,V.S.R.,2001.Hydrogeological framework of Gujarat State with


30

special emphasis on issues related to Ground WaterDevelopment and Management. Workshop onSustainable Development and Management of GroundWater Resources in Gujarat . Tech. Report ,CGWB, WCR,Ahmedabad .

11. Jain,R.C. and Sinha,A.K.,2002. Intensive use of Groundwater in North Gujarat, India- Challenges andopportunities. Proceedings of the SINEX Symposium,Barcelona, Spain,2002. Published in “GroundwaterIntensive Use ” Sahuquillo, A.,Capilla,J., Martinez-Cortina,L., Sanchez-Villa,X.,(Editors),Publication No.SP 7 of the International Association ofHydrogelogists.2005.

12. Jain,R.C. , 2003. Hydrological opportunities andlimitations of Artificial Recharge to Ground Water inGujarat. Publication of The International WaterManagement Institute, Anand Centre, Gujarat.

13. Jain,R.C. and Sinha,A.K. , 2004. Hydrogeologicalframework of the over-exploited multi-aquifer systemin North Gujarat, India –Problems and prospects ofsustainability. Proceedings of the 32nd InternationalGeological Congress, Italy,2004.

14. Jain,R.C. and Sinha,A.K., 2008. Groundwatermanagement in Sabarmati river basin - Perspectives& Options. International Groundwater Conference onGroundwater Dynamics and Global Change , March11-14, 2008.

15. Jain, R.C., 2009. Hydrogeological Framework,Groundwater Resources and Strategies for sustainablemanagement in Gujarat. Proceedings of the National

Seminar on Water for Future, Organised by Narmadaand Tapi river Basin Organisation, Central WaterCommission, March 4-5,2009. Ahmedabad, Gujarat,India.

16. Jain, R.C., 2009.Emerging challenges for sustainableground water management in India. Journal of AppliedHydrology, Vol. XXII, No.1, 2009.

17. Jain, R. C., 2009. Trends and sustainability ofgroundwater in highly stressed aquifers of Gujarat,India . Trends and Sustainability of Groundwater inHighly Stressed Aquifers .Proc. of Symposium JS.2 atthe Joint IAHS & IAH Convention, Hyderabad, India,September 2009. IAHS Publ. 329, 2009.

18. Kovalevsky, Kruseman,G.P.and Rushton,K.R.,2004.Ground Water Studies -An international guide forhydrogeological investigations, Unesco-IHP-VI,Serieson Ground water No.3,p.430.

19. Planning Commission , 2007. Report of the Expert groupon Ground water Management and Ownership. Govt.of India.

20. United Nations Development Programme, 1976.Ground water surveys in Rajasthan and Gujarat.Technical Report.

21. United Nations Development Programme, 1988. Pilotproject on artificial recharge to ground water inMehsana and coastal area of Gujarat. Terminal report-summary of findings and recommendations.

22. World Bank, 1999.Ground water Regulation andManagement , Water sector report. .New Delhi.


31

RUNOFF ESTIMATION BY DISTRIBUTED CURVE NUMBER TECHNIQUEUSING REMOTE SENSING AND GIS

Susanta Kumar Jena1, Kamlesh Narayan Tiwari2 and Ashish Pandey3

ABSTRACT

A study was undertaken to compare composited and distributed Curve Number (CN)techniques for estimation of runoff from a medium size agricultural watershed namely Tarafeni(West Bengal). Indian remote sensing satellite digital images were used for classifying the landuse/ land cover of the study watershed. The study watershed was divided into grids and weightedCN was calculated for individual grids by intersecting the grid with CN coverage. Using distributedCN technique runoff was simulated at each grid and averaged at the outlet of the watershed andfinally compared with the values obtained from traditional compositing technique. The differentinitial abstractions (Ia) of 0.2S and 0.3S (S is the maximum potential retention) have also beentaken in different simulations and results were compared with the observed runoff. The studyrevealed that there is definite increase in runoff value estimated by distributed CN technique overcomposited technique. The amount of increase in runoff value calculated is more for the case ofIa=0.3S than Ia=0.2S. The percent increase in runoff is very high for small events, moderate formedium and low for high rainfall events. The results from statistical analysis, which includespercent deviation, model efficiency, coefficient of determination, coefficient of residual mass,root mean square error and student’s t-test for significant difference, show that distributed CNwith Ia=0.2S estimated runoff values are closely matching the observed runoff.

Key words: Distributed curve number, GIS, Remote sensing, Runoff, Statistical test

INTRODUCTION

Watershed management, which is a comprehensiveterm meaning the rational utilization of land and waterresources for optimal production and minimum hazardto natural resources can solve the problems of soilerosion and degradation in quality and quantity of waterresources. In many countries, non-availability of dataon amount and rate of runoff, sediment yield, nutrientloss, etc., is the major handicap to start with watersheddevelopment programme. There are numerous methodsto estimate runoff for ungauged watersheds, which isthe most important input required for design of hydraulicstructures, flood forecasting and erosion prediction andto link with water quality problems etc. in a watershedmanagement programme.

1 Directorate of Water Management (Formerly WTCER) (ICAR),Chandrasekharpur, Bhubaneswar-7510232 Dept. of Agricultural & Food Engineering, Indian Institute ofTechnology Kharagpur-7213023 Dept. of WRD&M, Indian Institute of Technology RoorkeePaper No. 1243

One of the most widely used techniques forestimating direct runoff depths from storm rainfall isthe United States Department of Agriculture (USDA)Soil Conservation Service’s (SCS) (now the NaturalResources Conservation Service) Curve Number (CN)method (Mishra and Singh, 2003). The SCS methodhas been used by many researchers to determine therainfall runoff relationship (Stuebe and Johnston, 1990;Sharma et al., 2001; Sharma and Kumar, 2002; Mishraet al. 2004; Pandey and Dabral, 2004; Pandey and Sahu,2004; Pandey et al. 2005; Mishra et al. 2005; Jain et al.2006). Prior to the widespread use of computers,averaging techniques were necessary to reducecalculations in manual runoff analysis. Although high-speed personal computers have reduced the tedious ofrepetitive calculations, compositing is still widely used.This is because many practicing professionals have notyet been able to take full advantage of new computertechnology, and because accepted approaches areusually slow to change unless significant advantagesfor an alternate approach are widely publicized. Runoffestimation using traditional composite technique is still



32

in use worldwide. The increased availability of digitalspatial databases and the use of GIS in recent timeshas helped in improving the practicality of estimatingrunoff depth using distributed curve numbers. However,if distributed CN approaches are to be adopted, it isimportant to understand how results from this approachcompare with results from the traditional compositedCN technique. Keeping this in mind, this study wasundertaken to estimate runoff depth using different CNmethods and with different values of initial abstractionand results thus obtained were compared with theobserved runoff from a medium agricultural watershed.

THEORETICAL CONSIDERATIONS

The SCS-Curve Number method (Mishra and Singh,2003.) is based on the water balance equation and twofundamental hypotheses. The first hypothesis states thatthe ratio of the actual amount of direct runoff to themaximum potential runoff is equal to the ratio of theamount of actual infiltration to the amount of the potentialmaximum retention. The second hypothesis states thatthe amount of initial abstraction is some fraction of thepotential maximum retention. Expressed mathematically,the water balance equation and the two hypotheses,respectively, are

QFIP a (1)

SF

IPQ

a

(2)

SIa (3)

where, P = total precipitation (mm), Ia= initialabstraction (mm), F = cumulative infiltration excludingIa (mm), Q = direct runoff (mm), and S = potentialmaximum retention or infiltration (mm).

The current version of the SCS-CN methodassumes equal to 0.2 for usual practical applications.As the initial abstraction component accounts forsurface storage, interception, and infiltration beforerunoff begins, can take any value ranging from 0 to. Combining equations (1) and (2), one can write anequation for Q as follows:

SIP)IP(

Qa

2a

(4)

Substituting Ia by 0.2S, the equation in its standardform is

)S8.0P()S2.0P(Q

2

(5)

254CN

25400S (6)

where CN is the curve number which depends uponland use, hydrologic soil group and antecedent soilmoisture condition.

Narayana (1993) recommended equal to 0.3 formost of the regions in India except for the regions havingblack clay soils where = 0.1 seems more appropriate.

MATERIALS AND METHODS

The study was undertaken in the Tarafeni watershedin Midnapore district of West Bengal in India. Thewatershed lies within 22037 to 22047 N latitude and86038 to 86048 E longitudes. The study area falls undersubtropical humid zone with an annual rainfall of 1350mm. The area of the watershed is 158.06 km2 and therelief varies from 110 m to 290 m above mean sea level.The shape of the watershed is almost circular and it iscovered by Survey of India (SOI) toposheet number:73/J-9, 10, 13 and 14 (1:50000 scale). The outlet of riverTarafeni is considered at the Tarafeni barrage, whichcomes under Kangsabati irrigation project, West Bengal.

Hydrologic data

The “Irrigation and Waterways Department” ofGovernment of West Bengal measures the hydrologicdata such as runoff at the outlet of the watershed everyone hour. An automatic rain gauge and another non-recording rain gauge were installed at the outlet of thewatershed from which daily rainfall as well as eventwiserainfall amount and intensity with respect to time werefound out. The data are available from the year 1995onwards.

Land use/ land cover data

Indian Remote Sensing satellite digital image (IRS-1A, LISS-II-B1, row: 20, path: 52 with spatial resolutionof 36.25 m) of year 1989 (dates of pass 21st Februaryand 13th November) as well as (IRS-1D, LISS-III, row:107, path: 56 with resolution 23.5 m) for the year 2000(dates of pass 22nd February and 14th November) wereimported to the ERDAS/ IMAGINE image processingsoftware. These images were rectified andgeometrically corrected with respect to already rectifiedand mosaic topo-sheets of that area. This process of


33

rectification was done by transferring the coordinatesof permanent features like road and rail crossings;bridges, barrages etc. on the river; as well as road-canal or rail-canal crossings present on rectified topo-sheets to satellite images. The three basic colours ofred, blue and green were assigned to different bands ofimage, which produced false colour composite (FCC)image. Then the Tarafeni watershed portion was subsetfrom the full image. The classification of FCC wascarried out with ERDAS/ IMAGINE software usingmaximum likelihood classifier (MLC) algorithm andground truth information. All the three levels of landuse/land cover classification system (NRSA, 1995) wereadopted for classification. A total of eight classes wereconsidered for classification. The classified images inraster format was changed to vector format (polygons)using ERDAS/IMAGINE software. Each polygon haddifferent attributes, like its area, perimeter and land use/land cover class. Next, this coverage was intersectedwith the watershed grid coverage to find out grid wiseland use /land cover values. The resolution of the gridwas kept at 400 m × 400 m. In individual grids, therewere several polygons having different attributes.

Soil coverage

Soil map of the study area was procured from AllIndia Land Use and Soil Survey, and it was scanned,rectified and geometrically corrected. Then differentsoil polygons representing different types of soil texturewere digitized using ARC/INFO software. Differentattributes of soil such as texture, hydrologic soil groups,etc., were assigned to these polygons.

Curve number coverage

The generated land use coverage and the soilcoverage were merged using UNION command ofARC/INFO software. The resultant coverage containedattributes of both the coverage. The antecedent moisturecondition was assumed to be AMC II. Using ARC/INFO software, all the polygons having a particular landuse and a hydrologic soil group were selected at a timeand then curve numbers were assigned to these polygonsusing conditional assignment of attributes. Thus, a curvenumber coverage is generated in which differentpolygons have different curve number values. Similarlycurve number coverages were developed for otherAMC conditions. Finally, these coverages wereintersected with watershed grid. For each grid, the curvenumber was determined by using weighted area method.If a grid contains n number of polygons having different

curve number values corresponding to different landuses, then the weighted curve number for that grid wasfound out by using the formula

AACNACNACNCN nn

...2211 (7)

where A1, A2, ……, An correspond to area of eachpolygon having CN value CN1, CN2,…, CNnrespectively, and A = area of each grid=A1+A2+…..+An.

Composited CN method

In composited method, CN was estimated using aprocedure in which area weighted average curvenumbers are calculated either for the entire area underconsideration, or for a small number of relativelyhomogeneous sub-areas. Composite curve numberswere determined by overlaying land-use and soils mapsto delineate polygons with unique land use andhydrologic soil group (HSG) combinations within thewatershed or sub-watershed being studied. A CN valuewas then assigned to each grid and the area-weightedaverage was calculated to determine the composite CNfor the watershed.

Distributed CN method

In the distributed approach, runoff depth wasestimated for each individual grid cell or polygon in thewatershed, based on the land use and soil conditions atthat location. There was no CN averaging, ratherseparate CN values were determined for each cell orpolygon and separate runoff values were calculated foreach cell or polygon. These runoff values were thenaveraged to find out the total runoff depth for the wholewatershed.

Simulation studies

Four different simulations with differentcombinations of CN methods and initial abstractionvalues were considered in this study. In simulation I,runoff was estimated using composited CN method andinitial abstraction (Ia) value as 0.3S, for simulation II itwas distributed CN with initial abstraction (Ia) value as0.3S, simulation III was composited CN with Ia as 0.2S,and for simulation IV it was distributed CN with Ia as0.2S. Then runoff values obtained by these fourcombinations were compared with the observed runoffvalues for different rainfall events. The runoff valuesobtained by all these four methods were then statistically


34

evaluated to find out the superior method for estimatingrunoff. The statistical methods used are explained inthe following paragraphs.

Evaluation criteria of different CN methods

The different curve number approaches wereevaluated using different statistical techniques assuggested by ASCE Task Committee (1993). Thefollowing performance evaluation criteria were adoptedin this study.

The coefficient of determination (R2) describes theproportion of the total variance in the observed datathat can be explained by the model. It ranges from 0.0to 1.0, with higher values indicating better agreement,and is given by:

2

5.02N

1iavgi

5.0N

1i

2avgi

avgi

N

1iavgi

2

SSOO

SSOO

R

(8)

where, Oi = the ith observed data, Oavg = mean ofthe observed data, Si= the ith simulated value, Savg =the mean of simulated value, and N = total number ofevents.

The percent deviation, Dv is one “goodness of fit”criterion (Martinec and Rango, 1989), which isexpressed by

100V

VV(%)Dv

(9)

where V is the observed value of variable and V isthe simulated value. Smaller value of Dv indicates bettermodel prediction. For a perfect model, Dv is equal tozero. Another basic goodness of fit criterion is the modelefficiency, E (Nash and Sutcliffe, 1970) which is givenby

N

1i

2avgi

N

1i

2ii

OO

SO

1E(10)

The notations of above equation are the same as

that of equation (8). The E values can vary from 0 to 1,with 1 indicating a perfect fit. The model efficiency, Ehas been widely used to evaluate the performance ofhydrologic models (Wilcox et al., 1990). Model efficiencyrepresents an improvement over the R2 for modelevaluation purposes, in that it is sensitive to differencesin the observed and model simulated means andvariances.

Another technique is determination of coefficientof residual mass (CRM) (Hack-ten Broeke andHegmans, 1996). CRM statistics gives the degree towhich the prediction is overestimated or underestimated.Positive value of CRM indicates that the modelunderestimates the measured or observed and a negativevalue of CRM indicates a tendency to overestimate.The expression of CRM can be given as:

N

1ii

N

1i

N

1iii

O

SO

CRM(11)

Another measure of the accuracy of predictioncapability of a model is the root mean square error(RMSE), defined as (Thomann, 1982):

N

SO

RMSE

N

1i

2ii

(12)

RMSE describes the difference between the modelsimulations and observations in the units of the variable.

Based on the probability concept, pairwisecomparison for significant difference between themeans of observed and model simulated values weredone by using Student’s t statistics and given by:

D

5.0av

c SND

t (13)

where Dav is the average of the differencesbetween the model simulated (Si) and observed (Oi)values, N is the number of observations and SD is thesample standard deviation of the difference values.Following hypotheses is tested for determining thatwhether the difference between the observed andpredicted values is significant or not.H0: Dav = 0.0 and H1: Dav 0.0


35

Criteria for acceptance: if |tc| t0.975, N-1 accept H0.The calculated value of student’s t (tc), less than that ofthe t-tabulated value at N-1 degree of freedom and 5%level of significance (i.e. probability of type-I error/significance level of the test () = 0.025 for two tailed‘t’-test), indicates that the difference between meansis not significant at 5% significance level. The rejectionof null hypothesis at 5% significance level indicates thatthere exists significant difference between the meansof observed and simulated values.

RESULTS AND DISCUSSION

The results of land use/ land cover classificationare presented in Table 1. The classification accuracyfor the Tarafeni watershed for the year 2000 is 91.2%and for 1989 is 89.3%. From Table 1, it is observed thatin the Tarafeni watershed dense forest, degraded andfallow land have been reduced, open forest areaincreased from 13.3% to 19.3%, waste land developedto 2.3% area of total watershed from nil.

The distributed CN technique was applied to theTarafeni watershed. The gridwise curve numbercoverage was generated both for seedbed preparationstage and growing stage for both the years. The runoff

was then calculated at each grid level and averaged tofind out the runoff at the outlet. Runoff was calculatedusing both composited and distributed CN techniquesusing two different values of Ia : 0.2S and 0.3S. Theresults are presented in figure 1.

It is observed from figure 1 that there is definitelyan increase in runoff value estimated by distributed CNtechnique over composited CN technique for both thecases of initial abstraction values of 0.2S and 0.3S.However, the percentage increases in runoff incase of0.3S is comparatively higher than that of 0.2S case.Hence for choosing which CN technique should be usedfor estimation of runoff in a watershed, it is desirable tocompare runoff values obtained by different techniqueswith the observed values. The results are presented inFigure 2 and Table 2.

It is observed from figure 2 that runoff estimatedusing both composite and distributed CN techniquesconsidering Ia=0.3S yields lower runoff compared tothe observed one in all the events considered for rainfallamounts varying from 25 mm to 80 mm during 1999 to2001. Runoff estimated using composite technique forIa=0.2S yields comparable results with the observedrunoff for rainfall events of more than 63 mm of rainfall.However runoff estimated using distributed techniqueand Ia=0.2S gave almost same results as observed.

To support the above findings, statistical analysiswas carried out to find out superiority of one approachover the other in terms of closeness of predicted andthe observed values. The results are presented in Table2. It is observed from the table that percent deviationof runoff estimated using composited method for Ia=0.3Sfrom the observed runoff varies from 21.27% to 99.88%.The lowest deviation is for the largest event (80 mm on20th July 1999) and the highest deviation of 99.88% isfor the smallest event (25 mm on 1st September 2000)considered for this study. Similarly for distributed CNmethod with Ia=0.3S, percent deviation varies from

0

5

10

15

20

25

30

35

40

30 40 50 60 70 80 90 100 110 120 130

Rainfall (mm)

Inc

rea

se

in r

un

off

(%

)

seed bed (2000)

growing (2000)

seed bed (1989)

growing (1989)

(a) Ia=0.2S

0

20

40

60

80

100

120

140

30 40 50 60 70 80 90 100 110 120 130

Rainfall (mm)

Inc

rea

se

in r

un

off

(%

)

seed bed (2000)

growing (2000)

seed bed (1989)

growing (1989)

(b) Ia= 0.3S

Fig. 1 Increase in runoff estimated by distributed CN overcomposited CN technique for Ia=0.2S and Ia=0.3S.

Incr

ease

in r

unof

fIn

crea

se in

run

off

0.0

5.0

10.0

15.0

20.0

25.0

30.0

25.0 30.0 32.6 34.0 36.0 38.0 40.0 43.0 45.5 48.6 51.0 63.0 70.0 77.0 80.0

Rainfall, mm

Runo

ff, m

m

Composited,0.3S

Distributed, 0.3S

Composited,0.2S

Distributed, 0.2S

Observed

Fig. 2. Funoff estimated by different methods


36

Table 1. Land use/ land cover statistics of the Tarafeni watershed

Land use/ land cover Year 1989 Year 2000 Change inArea (ha) % Area (ha) % area (%)

Dense forest 2715.0 17.18 1874.9 11.86 -5.32Open forest 2109.6 13.35 3046.6 19.28 +5.93Degraded forest 3337.0 21.11 2932.7 18.55 -2.56Cropland 1961.6 12.41 2207.0 13.96 +1.55Fallow land 4029.8 25.5 3815.4 24.14 -1.36Wet land 605.0 3.83 639.4 4.05 +0.22Waste land 0.0 0.0 367.9 2.33 +2.33Water bodies 1048.1 6.63 922.13 5.83 -0.80Total 15806.1 100 15806.1 100

Table 2. Evaluation of different CN techniques for estimation of runoff (t- critical value for two tail t distribution=2.145)

13.91% to 63.64%. This deviation is lower as comparedto the composited method with same initial abstractionvalue. For initial abstraction of 0.2S the compositedmethod estimated runoff with percent deviation varyingfrom 0.15% to 63.95%. Though it has very low percentof deviation for larger events, the deviation is greaterfor small events. For the distributed CN method andIa=0.2S, the percent deviation varies from 16.86% to23.03%. Though this method does not give definite trendto predict runoff, the percent deviation is lowest incomparison to all other methods considered in this study.

From the other statistical analysis results presentedin Table 2, it is observed that the coefficient of residualmass (CRM) is positive and it varies from 0.399 to 0.103for composited methods (for both Ia=0.2S and 0.3S)and also for distributed method (Ia=0.3S). In distributedCN approach for Ia=0.2S, the CRM is very low (-0.022)which reflects the closeness of prediction with a verylittle tendency of over prediction. Model efficiency variesfrom 0.699 to 0.990. The highest value being fordistributed CN with Ia=0.2S. Coefficient of determination(R2) value is very high for all the cases. This is due to


37

the fact that runoff estimated using differentmethodologies use the same equation. RMSE is only0.73 mm for distributed CN (Ia=0.2S), whereas it is ashigh as 4.12 mm for composited (Ia=0.3S). From theabove results, it is seen that percentage deviation andRMSE are lowest for runoff values estimated usingdistributed CN technique with Ia=0.2S. In students’ t-test, it is found that calculated t value for first threetechniques yield higher value than the critical table value(t0.975,14) for two tail distribution. This proves thesuperiority of distributed CN technique (Ia=0.2S) overall other techniques as the calculated t value of –1.191is lower than the critical table value of 2.145.

Figure 3 shows a graph of observed versussimulated runoff using distributed CN technique forIa=0.2S. It is clearly seen that the data points are veryclose and clustered around the 1:1 line. Hence it can beinferred that for the Tarafeni watershed distributed CNtechnique considering initial abstraction as 0.2S is abetter predictor of runoff using SCS CN technique.

CONCLUSIONS

In Indian watersheds, usually composite CNtechnique is used with initial abstraction as 0.1S forblack clay soil and 0.3S for rest of the soils (Narayana,1993), But from the above investigation, it is found thatthese techniques yield lower runoff values comparedto the observed value. The pertinent observations fromthe study is that there is a definite increase in runoffvalue estimated by distributed CN technique overcomposited and the amount of increase is more for the

case of Ia=0.3S than Ia=0.2S. The percent increase inrunoff is very high for small events, moderate formedium and low for high rainfall events. Hence,distributed CN technique with initial abstraction as 0.2times of potential maximum retention of soil should beused for runoff estimation in medium or large agriculturalwatersheds having large variation in land use. Further,remote sensing and GIS can be effectively used toaccurately estimate runoff through distributed curvenumber technique.

REFERENCES

1. ASCE task committee. 1993. Criteria for evaluation ofwatershed models. J. Irri. and Drain. Engg., Vol. 119,No. 3, pp. 429-442.

2. Hack-ten Broeke M J D; Hegmans J H B M. 1996. Use ofsoil physical characteristics from laboratorymeasurements or standard series of modelingunsaturated water flow, Agril. Water. Manag. Vol.29,No. 2, pp. 201-213.

3. Jain, M.K., Mishra, S.K. and Singh, V.P. 2006.Evaluation of AMC-dependent SCS-CN-based modelsusing watershed characteristics,’ J. Water ResourcesManagement, 20(4): 531-555.

4. Martinec J; Rango A. 1989. Merits of statistical criteriafor the performance of hydrological models. WaterResour. Bull. AWRA, Vol. 25, No. 2, pp. 421-432.

5. Mishra, S.K. and V.P., Singh, 2003. Soil ConservationService Curve Number (SCS-CN) Methodology, KluwerAcademic Publishers, Dordrecht, The Netherlands,ISBN 1-4020-1132-6.

6. Mishra, S.K., Jain, M.K., and Singh, V.P. 2004.Evaluation of the SCS-CN-based model incorporatingantecedent moisture, J. Water Resources Management(WARM), (18): 567-589.

7. Mishra, S.K., Jain, M.K., Bhunya, P.K. and Singh, V.P.2005.Field applicability of the SCS-CN-inspiredMishra-Singh general model and its variants,’ J. WaterResources Management, 19(3): 37-62.

8. Narayana, V. V. D. 1993. Soil and water conservationresearch in India, Indian Council of Agril. Research,Krishi Anusandhan Bhawan, Pusa, New Delhi.

9. Nash J E; Sutcliffe J V. 1970. River flow forecastingthrough conceptual models, Part-1: a discussion ofprinciples, J. Hydrol., Vol. 10, No. 3, pp. 282-290.

10. NRSA. 1995. Integrated mission for sustainabledevelopment. technical guidelines, National RemoteSensing Agency, Dept. of Space, Balanagar, Hyderabad,pp. 86-87.

0

5

10

15

20

25

30

0 5 10 15 20 25 30observed runoff (mm)

sim

ulat

ed ru

noff

(mm

)

Distributed CN, Ia=0.2S

1:1 line

Fig. 3. Observed versus simulated runoff using distributed CNtechnique with Ia=0.2


38

11. Pandey, A. and Dabral, P.P. 2004. Estimation of runofffor hilly catchment using satellite data and GIS. J.Indian Soc. Remote Sensing 32 (2): 235-240.

12. Pandey, A. and Sahu, A.K. 2004. Estimation of runoffusing IRS-1 B LISS-II data. Indian J. Soil Cons. 32 (1):58-60.

13. Pandey, A., Chowdary, V.M., Mal, B. C. and Dabral, P.P.2005. Estimation of surface water potential ofagricultural watershed using geographic informationsystem. Asian J. Geoinformatics. 5 (4): 29-36.

14. Sharma, D. and Kumar, V., 2002, Application of SCSmodel with GIS data base for estimation of runoff in anarid watershed, Journal of Soil and WaterConservation, 30 (2) , 141-145.

15. Sharma, T., Satya Kiran, P.V., Singh, T.P., Trivedi, A.V.and Navalgund, R.R., 2001, Hydrologic response of a

watershed to landuse changes: A remote sensing andGIS approach. International Journal of RemoteSensing, 22 (11), 2095-2108.

16. Stuebe, M. M. and Douglas M. J., 1990, Runoff volumeestimation using GIS technique, Water ResourcesBulletin, American Water Resources Association, 26(4),611-620.

17. Thomann, R. V. 1982. Verification of water qualitymodels, J. Environ. Engg. Div., 108 (5), 923-940.

18. SCS (Soil Conservation Services), 1985, NationalEngineering Hand book, section 4: Hydrology(Washington, D.C., Soil Conservation Services, USDA).

19. Wilcox B P; Rawls W J; Brakensiek D L; and Weight J R.1990. Predicting runoff from rangeland catchments: acomparison of two models, Water Resour. Res., 26, 2401-2410.


39

SEDIMENT RUNOFF MODELING USING ARTIFICIAL NEURAL NETWORKS

Archana Sarkar1, M. Mohan Raju2 and Anil Kumar2

ABSTRACT

The magnitude of sediments transported by rivers is a major concern for the water resourcesplanning and management. The methods available for sediment estimation are largely empirical,with sediment rating curves being the most widely used. In this study, Artificial Neural Network(ANN) technique has been applied to model the sediment-discharge relationship of a river. Dailydata of sediment concentration and discharge of Pranhita River (a sub-basin of Godavari River)in India have been used. A comparison has been made between the results obtained using ANNsand sediment rating curves. The sediment load estimations in the river obtained by ANNs havebeen found to be significantly superior to the corresponding classical sediment rating curveones.

INTRODUCTION

The sediment outflow from a catchment is inducedby processes of detachment, transportation anddeposition of soil materials by rainfall and runoff. Theassessment of the volume of sediments beingtransported by a river is required in wide spectrum ofproblems such as the design of reservoirs and dams;hydroelectric power generation and water supply;transport of sediment and pollutants in rivers, lakes andestuaries; determination of the effects of watershedmanagement; and environmental impact assessment.Keeping this in view, in the present study, runoff-sediment modeling has been carried out for PranhitaRiver, a major tributary of Gopdavari River. Pranhitasub-basin system, which conveys the combined watersof Penganga, Wardha and Wainganga influences theGodavari river system to the maximum possible extent(with 34% drainage area i.e., 1,09,100 km2 area) bymeans of rainfall, runoff and sediment transportation.

For estimating the sediment concentration/yield,there exist various models and techniques, such assediment rating curves, erosion modeling, etc. Themodels vary from a simple regression relationship tocomplex simulation models. As the sediment-dischargerelationship is not linear, conventional statistical toolsused in such situations such as regression and curve1 National Institute of Hydrology, Roorkee-247667, Uttarakhand,India2 Department of Soil and Water Conservation Engineering, College ofTechnology, G. B. Pant University of Agriculture and Technology,Pantnagar-263145, Uttarakhand, India.Paper No. 1181

fitting methods are unable to model the non-linearity inthe relationship. On the other hand, the application ofphysics-based distributed process computer simulationoffers another possible method of sediment prediction.But the application of these complex software programsis often problematic, due to the use of idealizedsedimentation components, or the need for massiveamounts of detailed spatial and temporal environmentaldata, which are not available. Simpler approaches aretherefore required in the form of ‘black-box’ modelingtechniques. Neurocomputing provides one possibleanswer to the problematic task of sediment transferprediction. In recent years, artificial neural networks(ANNs) which are simplified mathematicalrepresentation of the functioning of the human brainhave been widely used in runoff and sediment yieldmodeling. Three layered feed forward ANNs have beenshown to be a powerful tool for input-output mappingand have been widely used in water resourcesengineering problems (ASCE Task Committee, 2000).

The application of ANN approach for modelingsediment-discharge process is very recent, and hasalready produced very encouraging results. In aresearch project by Rosenbaum (2000), ANN techniquehas been used to predict sediment distribution in Swedishharbors. Baruah et al., (2001) developed neural networkmodels of Lake surface chlorophyll and sedimentcontent from LandsatTM imagery in order to assessthe water quality of the lake Kasumigaura in Japan andfound that back propagation neural network with onlyone hidden layer could model both the parameters better



40

than conventional regression techniques. Jain (2001)used the ANN approach to establish an integrated stage-discharge-sediment concentration relation for two siteson the Mississipi River and showed that the ANN resultswere much closer to the observed values than theconventional technique. Nagy et al., (2002) appliedANN technique to estimate the natural sedimentdischarge in rivers in terms of sediment concentrationand addressed the importance of choosing an appropriateneural network structure and providing field data to thatnetwork for training purpose. Sarkar et al., (2004)applied ANN technique to model the sediment-dischargerelationship of Kosi River of Bihar in India and foundthat sediment load estimations in the river obtained byANNs were superior to the sediment rating curve ones.Kerem et al., (2006) estimated the river suspendedsediment using ANN algorithm and compared with theconventional sediment rating curve and found thesuperiority of the ANN application. Kisi (2007) and Rai& Mathur (2008) have reported good applicationefficiency of ANNs in the sediment yield modeling whencompared with the conventional modeling techniques.

In the present study, two techniques, namely,sediment rating curve and artificial neural networks(ANNs), have been applied for modeling the sediment-discharge relationship for Pranhita River basin and acomparison of these techniques has been made.

SEDIMENT RATING CURVES

Sediment rating curves are widely used to estimatethe sediment concentration being transported by a river.A sediment rating curve is a relation between thesediment concentration and river discharge. Sedimentrating curves may be plotted showing average sedimentconcentration or load as a function of discharge averagedover daily, monthly, or other time periods. Rating curvesare developed on the premise that a stable relationshipbetween concentration and discharge can be developedwhich, although exhibiting scatter, will allow the meansediment yield to be determined on the basis of thedischarge history. A problem inherent in the rating curvetechnique is the high degree of scatter, which may bereduced but not eliminated. Concentration does notnecessarily increase as a function of discharge(Ferugson 1986).

Mathematically, a rating curve may be constructedby log-transforming all data and using a linear leastsquare regression to determine the line of best fit. Thelog-log relationship between load and discharge is of

the form:

C = aQb (1)

And the log-transformed form will plot as a straight lineon log-log paper:

log C = log a + b log (Q) (2)

Where, C = sediment concentration (or load), Q =discharge, a & b are regression constants.

ARTIFICIAL NEURAL NETWORKS (ANNs)

An ANN is a computing system made up of a highlyinterconnected set of simple information processingelements, analogous to a neuron, called units. Theneuron collects inputs from both a single and multiplesources and produces output in accordance with apredetermined non-linear function. An ANN model iscreated by interconnection of many of the neurons in aknown configuration. The primary elementscharacterizing the neural network are the distributedrepresentation of information, local operations and non-linear processing. Fig.1 shows the general structure ofa three layer back propagation ANN.

The main principle of neural computing is thedecomposition of the input-output relationship into seriesof linearly separable steps using hidden layers (Haykin,1994). Generally there are four distinct steps indeveloping an ANN-based solution. The first step isthe data transformation or scaling. The second step isthe network architecture definition, where the numberof hidden layers, the number of neurons in each layer,and the connectivity between the neurons are set. Inthe third step, a learning algorithm is used to train thenetwork to respond correctly to a given set of inputs.Lastly, comes the validation step in which theperformance of the trained ANN model is tested through

Input

Input layer Hidden layer

Output

Output layer

i =1, 2, 3……ni j=1, 2, 3 …….nh k=1, 2, 3…….no

.

.

.

.:

.

.

.

y1

y2

yno

x1

x2

xni

h4

h1

hnh

wh11 wo11

whnh,ni wono,ni

Fig 1. Structure of a multi-layer feed forward artificialneural network model.


41

some selected statistical criteria. The theory of ANNhas not been described here and can be found in manybooks such as Haykin (1994).

STUDY AREA, DATA AVAILABILITY ANDSELECTION OF INPUT/OUTPUT VARIABLES

In the present study the runoff-sediment modelinghas been carried out for Pranhita sub-basin system,which conveys the combined waters of Penganga,Wardha and Wainganga. The hydrological data for thestudy has been collected at Tekra site on Pranhita river(Fig. 2). After the Tekra site, Pranhita river joins themain Godavari in Andhra Pradesh.

The daily data of sediment concentration anddischarge were available at the Tekra site for four water

years (June 1, 2000 – May 31, 2004) constituting a totalof 1461 patterns. Out of this, 730 patterns were usedfor training, 365 patterns for testing and 366 patternsfor validation.

The first step in developing any model is to identifythe input and output variables. The output from themodels is the sediment concentration at time step t; Ct.It has been shown by many authors that the currentsediment concentration can be mapped better byconsidering, in addition to the current value of discharge,the sediment and discharge at the previous times.Therefore, in addition to Qt, i.e., discharge at time stept, other variables such as Qt-1, Qt-2, and Ct-1, Ct-2, werealso considered in the input.

Fig. 2 Pranhita river system and hydrological study location (Tekra site)


42

RATING CURVE ANALYSIS

Based on the sediment rating curve technique givenby equation (1), the sediment rating equation betweensediment load and discharge for Pranhita River at Tekrasite for the training period is

C = (4.94E-04)Q0.770 (3)

Where, C = Sediment concentration in the RiverPranhia at Tekra in gm/l at time t

Table 1: Various ANN Runoff-Sediment Models

ANN Model

Architecture Output Variable

Input Variables

ANN-1 [1 – 2 – 1] Ct Qt ANN-2 [3 – 3 – 1] C t Qt, Qt-1, Ct-1 ANN-3 [5 – 4 – 1] C t Qt, Qt-1, Qt-2, Ct-1, Ct-2 ANN-4 [7 – 6 – 1] C t Qt, Qt-1, Qt-2, Qt-3, Ct-1, Ct-2, Ct-3 ANN-5 [9 – 7 – 1] C t Qt, Qt-1, Qt-2, Qt-3, Qt-4, Ct-1, Ct-2, Ct-3, Ct-4 ANN-6 [1 – 2 – 5 – 1] Ct Qt ANN-7 [3 – 3 – 5 – 1] C t Qt, Qt-1, Ct-1 ANN-8 [5 – 4 – 5 – 1] C t Qt, Qt-1, Qt-2, Ct-1, Ct-2 ANN-9 [7 – 6 – 5 – 1] C t Qt, Qt-1, Qt-2, Qt-3, Ct-1, Ct-2, Ct-3 ANN-10 [9 – 7 – 5 – 1] C t Qt, Qt-1, Qt-2, Qt-3, Qt-4, Ct-1, Ct-2, Ct-3, Ct-4

Q = Discharge in the River Pranhia at Tekra inCumec at time t

DESIGN AND TRAINING OF ANN MODELS

Various combinations of input data considered fortraining of ANN in the present study are given in Table1. However, the input-output variables of ANN-1 havebeen used for the conventional sediment rating curveanalysis.

Where, C=Sediment Concentration at Tekra in g/l,Q=Discharge at Tekra in cumecs, t represents the timestep in days

A back-propagation ANN (BPANN) with thegeneralized delta rule as the training algorithm has beenemployed in this study. The ANN package NeuralPower (NP), 2003, downloaded from the Internet hasbeen used for the ANN model development. Thestructure for all simulation models is three and four layerBPANN which utilizes a non-linear sigmoid activationfunction uniformly between the layers. Nodes in theinput layer are equal to number of input variables, nodesin hidden layer are varied from the default value by theNP package for various number of input nodes aboveto approximately double of input nodes (Zhu et al., 1994)and the nodes in the output layer is one as the modelsprovide single output. According to Hsu et al. (1995),three-layer feed forward ANNs can be used to modelreal-world functional relationships that may be ofunknown or poorly defined form and complexity.Therefore, three-layer networks were tried in this study.However, four layer ANN models were also tried forcomparison purpose.

The modeling of ANN initiated with thenormalization (re-scaling) of all inputs and output with

the maximum value of respective variable reducing thedata in the range 0 to 1 to avoid any saturation effectthat may be caused by the use of sigmoid function. Allinterconnecting links between nodes of successive layerswere assigned random values called weights. A constantvalue of 0.15 and 0.8 respectively has been consideredfor learning rate and momentum term selected afterhit and trials. The range tried for learning rate andmomentum term were 0.10-0.40 and 0.7-0.9respectively. The quick propagation (QP) learningalgorithm has been adopted for training of all the ANNmodels. QP is a heuristic modification of the standardback propagation and is very fast. The network weightswere updated after presenting each pattern from thelearning data set, rather than once per iteration. Thecriteria selected to avoid over training was generalizationof ANN through cross-validation (Haykin, 1994). Forthis purpose, the data were divided into training, testingand validation sets. Training data (730 patterns) wereused for estimation of weights of the ANN model andtesting data (365 patterns) for evaluation of theperformance of ANN model during training. Trainingwas stopped when the error for the testing datasetstarted increasing i.e., when R started decreasing forthe testing dataset as the software does not calculateRMSE automatically during testing/validation phase. In


43

this way, the training and testing datasets have beenused to assess the performance of various candidatemodel structures, and thereby choose the best one. Theparticular ANN model with the best performingparameter values was chosen and the generalizedperformance of the resulting network has been measuredon the validation data set (366 patterns) to which it hasnever before been exposed. The performance of allthe ANN models have been tested through threestatistical criterion, viz, root mean square error (RMSE),correlation coefficient (R) and Coefficient ofDetermination (DC).


The comparative performance of various ANNmodels and rating curve analysis in terms of RMSE, Rand DC are given in Table 2. It can be seen from Table2 that the RMSE values are generally low (less than

Table 2: Comparative Performance of Various ANN models and Rating Curve

Training Testing Validation ANN Model

Network Architecture RMSE

(g/l) R DC RMSE

(g/l) R DC RMSE

(g/l) R DC

ANN1 [1-2-1] 0.075 0.978 0.957 0.228 0.907 0.743 0.284 0.912 0.667 ANN2 [3-3-1] 0.067 0.983 0.966 0.170 0.951 0.856 0.217 0.946 0.805 ANN3 [5-4-1] 0.063 0.985 0.969 0.174 0.949 0.850 0.223 0.945 0.794 ANN4 [7-6-1] 0.060 0.986 0.973 0.168 0.953 0.860 0.217 0.943 0.806 ANN5 [9-7-1] 0.064 0.985 0.969 0.173 0.944 0.852 0.210 0.940 0.819 ANN6 [1-2-5-1] 0.071 0.980 0.962 0.229 0.903 0.740 0.284 0.919 0.675 ANN7 [3-3-5-1] 0.070 0.981 0.963 0.159 0.955 0.874 0.198 0.949 0.837 ANN8 [5-4-5-1] 0.067 0.984 0.966 0.151 0.953 0.887 0.217 0.920 0.805 ANN9 [7-6-5-1] 0.076 0.978 0.957 0.187 0.959 0.904 0.162 0.954 0.891 ANN10 [9-7-5-1] 0.056 0.987 0.974 0.169 0.950 0.859 0.210 0.948 0.818 SRC -- 0.472 0.923 0.680 0.359 0.870 0.870 0.424 0.867 0.668

0.075g/l) for all the ANN models except ANN-1 andANN-9 models, during training. However, RMSE islowest for ANN8 (0.151g/l) during testing and for ANN9(0.162g/l) during validation. Whereas, RMSE of therating curve model is very high, i.e., 0.472g/l, 0.359g/land 0.424g/l during training, testing and validationrespectively.

It can be seen from Table 2 that the correlation (R)values are high (more than 0.90) for all the ANN modelsduring all the three phases. The performance of ANN-9 model is the best in R statistic with R values of 0.959and 0.954 during testing and validation respectively. Theperformance of the rating curve model is the worst whencompared with the ANN models. The R values for ratingcurve model are 0.923, 0.870 and 0.867 during training,testing and validation respectively. These values areeven lower than the worst ANN model, i.e., ANN-1model.

In the coefficient of determination (DC) statistic,all the ANN models except ANN-1 perform well. InDC statistic also, ANN-9 model performs the best duringtesting and validation phases. The performance of ratingcurve model in DC statistic has gone down drasticallywith DC values as low as 0.68, 0.87 and 0.668 duringtraining, testing and validation respectively.

It can be seen that ANN-9 model is the bestperforming model in two statistical and hydrologicalcriteria during testing and validation phase. ANN( modelalso has low RMSE values of 0.076, 0.187 and 0.162during training, testing and validation respectively. Amodel which performs better in the validation phase isthe best model as it is the most generalized model.Therefore, ANN-9 model is the model representing

sediment-discharge relation of the Pranhita River at thegauging site Tekra. The performance of the rating curvemodel is average in the R criteria but drastically poor inother two criteria. It is because the estimated sedimentseries (from sediment rating curve model) follows agood general trend as that of the observed sedimentseries which gives high R values, but there is a significantdifference in the numeric values of observed andestimated sediment concentration due to which theRMSE and DC values are very poor. The performanceof the corresponding ANN model with only dischargeas input, i.e. ANN-1 is also better as compared withthe sediment rating curve technique.

The temporal variation of the observed sedimentconcentration and the estimate using the conventional


44

technique and ANN (ANN-9) for the training, testingas well as validation period is plotted in Fig 3. It is seenfrom the graph that ANN estimates very closely followthe observed curve, whereas conventional approach has

significant mismatch with the observed curve, especiallyduring the validation phase which conforms to the lowcoefficient of determination of rating curve technique.

-2

0

2

4

6

8

10

12

14

1 101 201 301 401 501 601 701

Observed Estiamted (A NN )Estimated (SR C)

Se

dim

en

t c

on

ce

ntr

ati

on

(g

/l)

T im e (d ays )

(a) Training

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

1 101 201 301

ObservedEst imated (AN N)Est imated (SRC)

Se

dim

en

t c

on

ce

ntr

ati

on

(g

/l)

T im e (d ays )

(b) Testing

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

1 101 201 301

ObservedEst imated (AN N)Est imated (SR C)

Se

dim

en

t c

on

ce

ntr

ati

on

(g

/l)

T im e (d ays )

(c) Validation

Fig. 3. Comparative performance of observed, estimated (ANN) and estimated (rating curve) sediment concentration series


45

CONCLUSIONS

In the presented study ANN technique has beenutilized for modeling the sediment-discharge process ina river. The primary aim of the presented study is toillustrate the capability of ANN technique for modelingsediment load in rivers. To achieve the objectives, acase study has been done utilizing four years of dailydata of the Tekra gauging site of Pranhita river (a sub-basin of Godavari River) in India for analysis. Basedon the selected performance evaluation criteria, ANN-9, i.e., four layered back propagation ANN model with7 input variables, 6 nodes in the first hidden layer, 5nodes in the second hidden layer and one output variablerepresents the best model simulating the sediment-discharge relationship of the Pranhita River at Tekragauging site. The results of ANN have been comparedwith those of the conventional sediment rating curveapproach. ANN results have been found to be muchcloser to the observed values than the conventionaltechnique.

The study demonstrates that ANN technique canbe successfully applied for development of reliablerelationships between sediment and discharge in a riverwhen other approaches cannot succeed due to theuncertainty and the stochastic nature of the sedimentmovement. Moreover, ANN technique has preferenceover the conventional methods as ANNs can acceptany number of effective variables as input parameterswithout omission or simplification as commonly done inconventional methods. The presented ANN model isdesigned by using only field river data, and it has noboundary conditions in application. The only restrictionis that the model cannot estimate accurately thesediment load for data out of the range of training patterndata. Such a problem can easily be overcome by feedingthe training patterns with wide range data.

REFERENCES

1. ASCE. Task Committee on Application of ArtificialNeural Networks in Hydrology. 2000, “Artificial NeuralNetworks in Hydrology. II: Hydrologic Applications”,Journal of Hydrologic Engineering, ASCE, Vol.5 (2),pp.124-137.

2. Baruah, P. J., Tamura, M., Oki, K. and Nishimura, H.2001, “Neural Network Modelling of Lake SurfaceChlorophyll and Sediment Content from LandsatTM

Imagery”, Proc. 22nd Asian Conf. on Remote Sensing,5-9 November 2001, Singapore.

3. Ferugson, R.I. 1986, “River Loads Underestimated byRating Curves”, Water Resources Research, Vol. 22(1),pp.74-76.

4. Haykin, S. 1994, “Neural Networks - a ComprehensiveFoundation. Macmillan”, New York.

5. Hsu, K., Gupta, H.V., & Sorooshian, S. 1995, “ArtificialNeural Network Modelling of the Rainfall-Runoffprocess”, Water Resources Research, vol. 31(10),pp.2517-2530.

6. Jain, S.K. 2001, “Development of Integrated SedimentRating Curves using ANNs”, J. of HydraulicEngineering, ASCE, Vol. 127(1), pp.30-37.

7. Kerem, H., Cigizoglu, and Alp, M. 2006. Generalizedregression neural network in modeling river sedimentyield. Advances in Engineering Software archive,37(2), pp. 63-68.

8. Kisi, O. 2007. Development of streamflow-suspendedsediment rating curve using a range dependent neuralnetwork. International Journal of Science andTechnology.

9. Nagy, H.M., Watanabe, B., and Hirano, M., 2002,“Prediction of Sediment load Concentration in RiversUsing Artificial Neural Network Model”, J. ofHydraulic Engineering, ASCE, Vol. 128(6) pp.588-595.

10. Rawat J.S and Rawat M.S. 1994, “Accelerated erosionand denudation in the nana kosi watershed, CentralHimalaya, India, Part I: sediment load”, J. of MountainResearch and Development, Vol. 14(1), pp 25-38.

11. Raymo M.E. and Ruddiman W.F. 1992, “Tectonic forcingof Late Cainozoic Climate”, Nature, Vol. 359, pp. 117-122.

12. Rosenbaum, M 2000, “Harbours- Silting andEnvironmental Sedimentology (H-SENSE), FinalReport, Dept. of Civil & Structural Engineering”, TheNottingham Trent University, Nottingham, UK. http://hjs.geol.uib.no/HSense/

13. Sarkar, A., Kumar, R., Singh, R.D., Thakur, G and Jain,S.K. 2004, “Sediment-Discharge Modelling in a Riverusing Artificial Neural Networks”, Proc. Int. Conf.ICON-HERP, Oct 26-28, 2004, IIT, Roorkee, India.

14. Zhu, M., Fujita, M., and Hashimoto, N. 1994,“Application of Neural Networks to Runoff Prediction,“Stochastic and Statistical Method in Hydrology andEnvironmental Engineering, vol. 3, K.W. Hipel et al.,eds., Kluwer, , The Netherlands, pp.205-216.


46

SINGLE RESERVOIR OPTIMIZATION USING HYBRID GENETICALGORITHM

Nitin M Mohite1 and Sandeep Narulkar2

ABSTRACT

In this paper a hybrid genetic algorithm approach is proposed wherein the GA acts as abase level search algorithm that generates the initial values for solving the problem with a con-strained optimization algorithm. The adaptation of the hybrid approach has eliminated the prob-lems associated with the GA as well as the constrained optimization. The hybrid method proves tobe superior to the pure GA in finding a good solution quickly. The problem is applied to a singlereservoir planning problem for Parbati-Kalisindh-Chambal Interlinking Project in India.

Keywords : Optimization of single reservoir; Genetic algorithms, Hybrid methods

INTRODUCTION

The water resources planning and managementproblems, particularly the reservoir design and operationproblems are quite complex and dimensionally large.Simulation and optimization techniques play an importantrole in providing a good insight in solving these problems.Numerous researchers have solved the problems ofoptimum reservoir system planning and operation duringpast five decades using Simulation, Linear Programming(LP), Dynamic Programming (DP), NonlinearProgramming (NLP) etc. along with variants andcombination of these techniques. An exhaustive andextremely useful state-of-the-art review of simulationand optimization methods applied to reservoir systemsplanning and operation problems can be seen in theworks of Yeh (1985), Simonovic (1992), Wurbs (1993),and Labadie (2004) etc. In the recent era variousevolutionary search algorithms i.e. the GeneticAlgorithms (GA), Ant Colony Optimization (ACO),Particle Swarm Optimization (PSO) etc. have takenthe fore seat.

The Genetic Algorithm is a global stochastic searchtechnique based on the Darwinian Survival-of-the-Fittest principle (Holland 1975). GA models have beenapplied successfully to a wide range of reservoir designand optimization problems of moderate size. East andHall (1994) and Fahmy et al. (1994) applied GA to the

1 Research Scholar, Dept of Civil & Applied Mechanics S.G.S.I.T.S,Indore2 Professor, Dept. of Civil & Applied Mechanics S.G.S.I.T.S, IndorePaper No. 1216

reservoir system operation and compared theperformance of the GA approach with that of DP. Boththe works showed the significant potential of GA inwater resources system optimization, and clearlydemonstrated the advantage of GA over standard DPin terms of computational requirement. Oliveira andLoucks (1997) used a GA model to evaluate operatingrules for multi reservoir systems, demonstrating that GAcan be used to identify effective operating policies.

The breakthrough research work on GA applicationsto reservoir planning and operation problems waspresented by Wardlaw and Sharif (1999). Theyevaluated GA technique through the solution of theclassic 4 reservoir problem and the 10 reservoir problemwith extended time horizons explaining various aspectsof the applicability of the technique. They evaluatedseveral combinations of GA parameters viz. the binarycoded and real coded representation, selection,crossover and mutation etc. Sharif and Wardlaw (2000)applied the technique to a multiple reservoir systemsfor a case study in Indonesia by considering the existingdevelopment situation in the basin and two future waterresources development scenarios. Results of GA werecompared with those obtained from discrete differentialdynamic programming. They concluded that geneticalgorithm results are closer to the optimum.

Many other works reported in the literature includingHilton and Culver (2000) comparing Additive PenaltyMethod (APM) and Multiplicative Penalty Method(MPM) for constraint handling in non linear problemswith GA technique, Ahmed and Sarma (2005) comparing



47

GA and Stochastic Dynamic Programming (SDP) toderive optimal operating policy, Nageshkumar et al.(2006) stating a GA model for obtaining an optimaloperating policy and optimal crop water allocation froman irrigation reservoir, Jothipraksh and Ganesh Shanti(2006 and 2009) demonstrating application of GA tomultipurpose reservoir system, Momtahen and Dariane(2007) comparing Real Coded Genetic Algorithm withthat of conventional model involving explicit stochasticoptimization (ESO) and implicit stochastic optimization(ISO) approach, Kangrang and Chaleeraktrakoon(2007) discussing a problem of deriving optimaloperating rule curves by using genetic algorithmsconnected simulation models, Chouhan and Shrivastava(2008) applying preference based approach using GAbased model for optimal operation of a reservoir systemmaximizing irrigation in the command area for twoseasons and inter reservoir transfers in one of theseasons, Hashemi et al. (2008) showing the applicabilityof GA model in MATLAB environment for operationof a multipurpose reservoir in stochastic frame worketc. can be considered to be the benchmark works inthe reservoir system planning and operation. Recentwork of Dariane and Momtahen (2009) demonstratingan application of direct search genetic algorithm (DSGS)for multireservoir system operation can also beconsidered as important from the view point of largescale reservoir system problems.

Despite the fact that the GA Technique is one ofthe most suitable technique for direct search and as anoptimization model, its applicability to large scaleproblems has certain limitations as pointed out by manyresearchers. Kapelan (2002) has thoroughlysummarized the advantages and limitations of GA. Hemainly points out that despite the GA Technique havinggood capability of an efficient exploration of large,complex, multi-modal search spaces with a less chanceto get trapped into the local optimum, their use in termsof CPU time is usually very expensive. He further addsthat there is no guarantee that the global optimum willbe found, even though good solutions are usually found.Moreover, in the constrained problems if a penaltyfunction is not chosen carefully, it may significantly affectGA search performance. He also noticed that GAsuffers from the slow finishing problem. Prematureconvergence is another GA disadvantage. One of thegreatest drawbacks of GAs is that they require a highnumber of function evaluations to achieve convergence.Each function evaluation entails a full extended-period

simulation of the system, which is a computationallyexpensive process. The net result is that GA optimizationis time consuming (Van Zyl et al., 2004). Similarconclusions were drawn by Chun et al. (2008).

The limitations and disadvantages of the GAtechniques can be alleviated through the use of HybridGenetic Algorithm (HGA). The basic concept of HGAis coupling of two models, the GA and a constrained oran unconstrained nonlinear optimization model. A largenumber of such hybrid algorithms have been reportedviz. a combination of GA and Sequential QuadraticProgramming SQP (Marco, 1996, 1998), GA andGeneralized Reduced Gradient (GRG) (Yen, 2005), GAcoupled with Chaos Optimization Algorithm (COA)(Chun, 2008). These combinations have made thecomplex optimization problems simpler and easilyapplicable. Kapelan (2002) narrates followingadvantages of HGA over GA as follows:

1. Increased computational efficiency typicallymanifested as reduced CPU search time necessaryto find an optimal solution, or to reach some pre-specified objective function value.

2. The optimal objective function value found by HGAis better or similar to the one found by GA, indicatingbetter (or similar) model fit. The variation of bestfitnesses from multiple HGA runs is usually smallerthan the variation of best fitnesses determined frommultiple GA runs.

3. More accurate (less uncertain) calibrationparameter values are typically determined withHGA than GA.

The applications of HGA to water resourcesproblems are of recent origin except a few studies.Marco Franchini (1996) and Marco Franchini et al.(1998) applied HGA for calibration of conceptual rainfallrunoff modeling. Hsiao and Chang (2002) solved anoptimum pumping problem with the use of HGA.Kapelan (2002) applied the HGA to a pipe networkcalibration problem. Van Zyl et al., (2004) applied GAand two popular search methodologies the Hooke andJeeves and Fibonacci methods to solve the problem ofwater distribution system network problem. Espinozaet al (2005) have developed new Self Adaptive HybridGenetic Algorithm (SAHGA) to solve optimal groundwater remediation problem. They compared the resultswith simple genetic algorithm and Non Adaptive HybridGenetic Algorithm (NAHGA). They concluded that theselection of the local search algorithm to be combined


48

with the simple genetic algorithm is critical to algorithmperformance.

Reis et al. (2005) have applied hybrid geneticalgorithm (GA-LP) technique for multi-reservoiroperation planning problem. The results were comparedwith the stochastic dual dynamic programming (SDDP).Reis et al. (2006) have applied hybrid genetic algorithmmethod using genetic (GA) and linear programming (LP)to determine operational decisions for a reservoir systemover the optimization period. Ebrahimi et al. (2007) usedthe G.A approach and four combined models of G.Aand wavelet transforms to optimize the operation ofthe Vanyard dam reservoir. Chung et al. (2008) proposeda novel Chaos Genetic Algorithm (CGA) and applied itto optimal operation of hydropower station reservoirsystem in China. They combined chaos optimizationalgorithm (COA) and genetic algorithm (GA) toovercome premature local optimum and increase theconvergence speed of genetic algorithm. Li and Wei(2008) developed a GA – Simulated Annealing Methodfor operation planning of a multiple reservoir system.The objective was to maximize generation output fromthe 3-reservoir systems over each 12-month operatingperiods. The developed algorithm was stated to be fasterand better in comparison to GA. Yuan et al. (2008) haveproposed a new real value encoding self-adaptive chaoticgenetic algorithm to solve hydrogenation schedulingproblem. The new crossover operation with probabilitydistribution function and a self-adaptive chaotic mutationoperator combined chaotic dynamic character withartificial neural network theory are used. The resultshows that the model gives better quality solution.

THE MODEL

Though Hybrid GA model is considered to performbetter than the GA model, proper selection of the search/optimization algorithm to supplement GA is still aquestion. In most of the applications the local searchpart of the algorithm is problem specific. However, thereservoir planning and operation problems pose aconstrained optimization problem and one should use aconstrained optimization model for the search of theoptimum solution. The best constrained non linear modelis the Quadratic Programming (QP) model if theobjective function is strictly quadratic and the constraintsare linear. The convergence of the QP to a satisfactorylocal optimum shall be fast and improved if this modelis coupled with a GA model which provides a good initialpoint for the QP model.

In the present study the GA is applied first to producethe proper starting point. Then Quadratic Programming(QP) technique is used through optimization tool box inMATLAB Environment. The MATLAB software isextremely useful and user friendly mathematical tool tosolve various problems. In early seventies MATLABsoftware was written to solve the problem in the fieldof matrices, linear algebra and numerical analysis inStanford and New Mexico University. The importantfeature of this software is a matrix that doesn’t need tospecify its dimensions. So the solution of the problem isas easy as writing them. There are several differenttoolboxes available in MATLAB. One of the toolbox isfor the applications of the Genetic Algorithms. Theinvocation of the GA is through GA tool which acceptsthe objective functions and the constraints in the formof various matrices. The GA tool also specifies a widerange of parameters to be chosen with the GAapplication and also the convergence criteria. Thegenetic algorithm terminates when one of the followingexit conditions are met: (1) No improvement in the searchdirection (2) no improvement in the solution for morethan pre defined maximum number of iterations(generations) (3) maximum time in seconds the geneticalgorithm runs before stopping. The GA results are thenconsidered as an initial starting point for QP. The QPtoolbox is accessible form: Start/Toolboxes/Optimization/Optimization tool (optimtool). More detailsof the use of these functions can be found in MATLABUser’s guide (2007b). A flow diagram of the HGAmethod is given in Fig. 1

(GA Toolbox using GA and QUADPROG optionsof MATLAB 7.0).

The proposed model is applied to a reservoirplanning model. The reservoir is a component of aninter basin water transfer system in west central India.The details of the system and the model etc. are asfollows.

MODEL FORMULATION

The objective of the model is to optimize the capacityof a single reservoir that serves multiple purposes suchas Municipal and Industrial water supply, Irrigation andTransfer of water to other reservoir. The presentobjective is to minimize the capacity of a reservoir whilemaximizing the other objectives. The objective functionis:


49

Evaluation of Objective function (Fitness function)

Selection

Crossover

Mutation

Elitism

If any: 1. Meet specific value

for objective function 2. Meet stall generation

limits

Start QP: Use GA final solution as QP required initial point

Solve the problem with QP

Converge to desired

solution?

Final solution

Initial Population Generation no (G=1)

Specification of Parameters

Fig.1 Flow chart of hybrid genetic algorithm

Minimize

Y

tttt

Y

ttcp TTTRTBIDIRBirrCBCA

1

22

1

22 )(*)(*)(*)(* (1)

Whereu = Total no of years of planningT = Total No. of time steps used for planning in eachyearY = Total No. of time steps in the time horizon (= u x T)A = Relative weight indicating the unit cost of capacityof reservoir.

pC = Live storage capacity of the reservoir in MCMB = Relative weight indicating the unit cost of capacityof canal.

cC = The canal Capacity in Cumec.Birr = Relative weight indicating the benefit coefficientof unit volume of water supplied to irrigation in Millions/MCM

tIR = Monthly irrigation released at the reservoir inperiod t in MCM

tID = Monthly target irrigation demand at the reservoirin period t in MCM

TB = Relative weight indicating the benefit coefficientof unit volume of water transfer in Millions/MCM

tTR = Monthly transfer of water from the reservoir toother reservoir in period t in MCM

tTT = Monthly transfer target of water from the reservoirto other reservoir in period t in MCM

The objective function is subjected to followingconstraints

The reservoir continuity equation

The continuity equation for reservoir storage at the

end of time t , 1tS is calculated as

ttttttttt LSPTRIRISaSa )1()1( 1 (2)

Where

tS = Reservoir storage at the beginning of time period ttI = Inflow in the reservoir in time period t

tSP = Spillage in time period ttL = the fixed evaporation loss at site = ,* ot Ae te is

the evaporation rate in period t , oA = water surface areaat the top of the dead storage level, ,2/tt aea anda is the surface area per unit active storage.

The tttt STIrrS ,,, are in units of Million cubic meter(x106 m3) and te is in units of millimeters (mm). Themethod of computation of evaporation is as per theprocedure stated in Loucks et al. (1981).

Bounds on Capacity Constraints

The reservoir capacity is constrained by upperbound

pup CC (3)

puC = The upper bound on the capacity in MCM

cuc CC (4)

cuC = The upper bound on the Canal capacity in Cumec.

Reservoir capacity constraint01 pt CS (3)

The storage in the reservoir under consideration inany time period t cannot exceed the live storagecapacity.


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Canal capacity constraint

The water released from reservoir for irrigation andtransfer form the reservoir during t time cannot exceedthe canal capacity.

ctt CTRIR )(* (4)

Tt ,..,2,1 ; = factor of conversion of MCM tocumec (m3/s)

THE CASE STUDY

The model formulated above is applied to thePatanpur reservoir, which is first proposed reservoir inParbati-Kalisindh-Chambal (P-K-C) link project of themost ambitious river interlinking project envisaged bythe Government of India. The reservoir is a multipurposereservoir having municipal and industrial water supply,irrigation and water transfers to other reservoir for thepurpose of inter-basin water transfer as the objectives.The proposed reservoir is located between the Northlatitude 230 42’ and 240 2’and East longitudes of 770 9’

and 770 15’. The climate of the area is subtropical andsemi-arid. The maximum and minimum annual rainfallin the basin varies from 356 mm to 1270 mm. Theproposed active storage capacity of the reservoir is 110MCM and catchment area 5312 Km2. In the presentstudy the main objective is to optimize the live storagecapacity of the reservoir and the link canal capacitymaximizing the fulfillment of the objectives of the inter-basin transfer.

MODEL APPLICATION

The model formulated above was applied to thePatanpur reservoir which is one of the reservoirs ofParbati-Kalisindh-Chambal (P-K-C) link project. Thepresent values of cost parameter of dams and canalsand annual benefit of irrigation and transfer of waterhave been computed form the available data (Pre-Feasibility Report, 2004). The unit cost of structure wascalculated by dividing its design project cost by the designcapacity as per NWDA. The rate of annual interestwas taken as 5% of the respective capital cost. Theannual operation and maintenance cost is assumed tobe 10% of the total cost of project. The cost parameter(including all costs i.e. Construction cost + Operationand Maintenance cost) of the reservoir, canal and thebenefit parameters of irrigation and transfers shown inTable 1. The water demands in various reservoirs and

transfer targets are shown in Table 2 and 3 respectively.The target annual irrigation yield was at 124 million cubicmeters (MCM) and municipal requirement at 3 MCM.An inflow record of 20 years was used to demonstratethe application. The transfer targets were varied fromthe NWDA specified values and are specified as D1,D2, D3 and are mentioned in Table 3. As per the prioritiesfor water demand satisfaction set by the NWDA thehighest priority municipal and industrial demand wasfulfilled in all the seasons by subtracting it from thenatural inflows. The irrigation demands as well as thetransfer demands were weighed according to theexpected benefits. The upper bound on the reservoircapacities were varied from the NWDA specifiedvalues and many trial runs were taken to generateminimum capacity of the reservoir fulfilling the monthlyirrigation targets. The upper bound on the canal capacitywas also varied in similar manner.

For model computations, the MATLAB softwarewas used since it is easy to use and can accommodatelarge scale optimization problems. The GA componentof the model had many options due to various GAParameters viz. the population size, number ofgenerations, the crossover and mutation probabilities etc.In general a population size of 500 and a generationnumber of about 1000 was sufficient for a good degreeof optimum result as an initial value for QP algorithm.The crossover probabilities were taken in the range of0.7 to 1 and the mutation probabilities are taken in arange .001 to 0.3. Better results are obtained at acrossover probability at 0.75 and the mutation probabilityat 0.002. Further the problem was solved through QPusing optimization toolbox in the MATLAB Environment.


The results of application of the Hybrid Modelproposed in the present study for various target transfervalues are presented in Table 4. It is observed that in all20 years the M & I demands along with the irrigationtargets are fulfilled. In comparison to the plannedcapacity of the reservoir and canal, based on simulationstudies carried out by NWDA for a given set ofhydrologic record, present model generated bettervalues. With an increase in the target transfer values,the canal capacities are increased in proportion to thehighest volume of transfer chosen in a specific month.The reliability of the transfer target met is around 80%for all the cases. Also there is an increase in the netbenefits from the proposed system.


51

CONCLUSIONS

The present study demonstrates a successfulapplication of hybrid GA approach for solving a problemof optimal planning for a single reservoir. The GA modelapplied to the problem terminated to suboptimal solutionsin all the solutions employing various combinations ofthe GA parameters viz. the population size, number ofgenerations, the crossover and mutation probabilities etc.The direct application of the quadratic programming tothe same formulation though generated similar resultsas the hybrid model; the starting point to be chosen wasa tricky aspect. Moreover, the number of iterationsrequired was more in such cases. A major conclusionwas thus drawn that though, GA model as an

Table 1 Cost and benefit Parameter values of Patanpurreservoir

Parameters Patanpur Cost of Dam 7.594 Cost of canal 27.4

Benefits from Irrigation 5.06 Benefits from Transfer 1.478

The cost and benefits of all the variables are in

Million Rs. / MCM. MCM indicates Million CubicMeters and Rs. Specify the Indian Rupee. The cost ofcanal is in Million Rs. / cumec. Cumec indicates CubicMeter per Second.

evolutionary algorithm is good for solving complexproblems, its application to a large dimensionalconstrained problem has some limitations. The worthof quadratic programming method to purely quadraticformulations such as presented in the study, has beenestablished since early stages of research in optimization.But with the use of a hybrid approach, the computationalefficiency of the QP has increased considerably. Theresults obtained in the present study indicate that thereis a scope of optimization of the proposed capacities ofthe reservoirs in the Inter basin Transfer Project toachieve better water management as well as betterenvironmental conditions. In the present study theoptimization has been achieved through trials of differentoperating policies.

Table 2 Water demands and transfer targets for Patanpurreservoirs (MCM)

Month Water Supply Irrigation June 0.25 8.68 July 0.25 19.84 August 0.25 13.64 September 0.25 8.68 October 0.25 6.2 November 0.25 8.68 December 0.25 13.64 January 0.25 18.6 February 0.25 17.36 March 0.25 3.72 April 0.25 2.48 May 0.25 2.48 Annual 3 124

Table 3 Transfer targets for Patanpur reservoirs (MCM)

Month NWDA D1 D2 D3 June 6 6 6 6 July 67 80 100 100 August 131 400 450 475 September 24 250 230 205 October 57 5 5 5 November 4 5 5 5 December 4 5 5 5 January 4 1 1 1 February 4 1 1 1 March 4 1 1 1 April 4 1 1 1 May 4 1 1 1 Total 313 756 806 806


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1. Ahamed, J.A., and Sarma, 2005. Genetic algorithms foroptimal operating policy of a multi-purpose reservoir.J. Water Resor. Manage., 19(2), 145-161.

2. Chauhan, S., and Shrivastava, R.K., 2008. Optimaloperation of multipurpose reservoir using geneticalgorithms. J. of Indian Water Resources Society., 28(4),9-17.

3. Chun, C. T., Wang, W. C., Mei, W.D., and Chau, K. W.2008. Optimizing hydropower reservoir operationusing genetic algorithm and chaos. J. Water ResourcesManage., 22 (7), 895-909.

4. Dariane, A., and Momtahen, S. 2009. Optimization ofmultireservoir systems operation using modified directsearch genetic algorithm. J. Water Resour. Plan.Manage. ASCE 135(3), 141-148.

5. East, V., and Hall, M.J. 1994. Water resources systemoptimization using genetic algorithms.” Proc., 1st Int.Conf. on Hydroinformatics, Baikema, Rotteram,Netherlands, 220-231.

6. Ebrahimi, L., Barani, G. A., and Ebrahimi, H. 2007.Optimization of reservoir operation using combinedgenetic algorithm and wavelet transform methods.American J. of Agricultural and Biological sciences, 2(3), 153-158.

7. Espinoza, F.P., Minsker, B. and Goldberg, D. 2005.Adaptive hybrid genetic algorithm for groundwaterremediation design. J. Water Resour. Plan. Manage.ASCE 131(1), 14-24.

8. Fahmy, H.S. King, J.P, Wentzel, M.W. and Seton J.A. 1994.Economic optimization of river management usinggenetic algorithms. Paper no. 943034, ASCE Int.Summer meeting, Am. Soc. Of Agricultural Engrs., St.Joseph, Mich.

9. Hashemi, M. S., Barani, G.A., and Ebrahimi, H. 2008.Optimization of reservoir operation by geneticalgorithm considering inflow probabilities (Casestudy: The Jiroft Dam Reservoir). J. of Applied Sciences8(11), 2173-2177.

10. Hilton, A.B. and Culver, T.B. 2000. Constraint handlingfor genetic algorithm in optimal remediation design. J.Water Resour. Plan. Manage. ASCE 126(3), 128-137.

Table 4 Summary of results of optimization for Patanpur reservoir Reservoir Storage

Capacity (MCM) Patanpur % Reduction

Adopted in planning 110 NWDA Policy 110 0 D1 101.5 7.73 D2 97 11.81 D3 97 11.81 Canal capacity (cumec) Adopted in planning 199.32 % Reduction NWDA Policy 59.5475 70.12467 D1 171.9895 13.71187 D2 192.8895 3.226219 D3 203.3395 -2.01661 Total Transfer From Patanpur for 20 Years (MCM) Maximum Value Actual

Transfers Reliability of Target Met

NWDA Policy 6260 5253.951 83.9 D1 15120 12019.6 79.41 D2 16120 12625.99 83.5 D3 16120 12675.44 83.84 Net Benefit from Proposed System (Mill. Rs.) NWDA Policy 17748.35 % Increase D1 24738.09 39.38 D2 25076.21 41.28 D3 24862.63 40.08


53

11. Holland, J.H. 1975. Adaptation in natural and artificialsystems. MIT Press, Cambridge, Mass.

12. Jothipraksah and Ganesh Shanti 2006. Singlereservoir operation using genetic algorithm.” J. WaterResources Manage. 20 (6), 917-929.

13. Jothipraksah and Ganesh Shanti 2009. Comparison ofpolicies derived from stochastic dynamic programmingand genetic algorithm models. J. Water ResourcesManage. 23 (8), 1563-1580.

14. Kangrang, A. and Chaleeraktrakoon, C. 2007. Geneticalgorithm connected simulation with smoothingfunction for searching rule curves. American J. ofApplied Sciences. 4(2), 73-79

15. Kapelan, Z. 2002. Calibration of water distributionsystem hydraulic models. Ph D Thesis

16. School of Engineering and science, University of Exeter,Exeter, UK

17. Labadie, J. W., 2004. Optimal operation ofmultireservoir systems: state-of –the-art- Review. J.Water Resour. Plan. Manage. ASCE 130(2), 93-111.

18. Li, X., and Wei, X. 2008. An improved genetic algorithm-simulated annealing hybrid algorithm for theoptimization of multiple reservoirs. J. Water ResourcesManage. 22 (8), 1031-1049.

19. Momtahen, Sh., and Dariane, A.B. 2007. Direct searchapproaches using genetic algorithms for optimizationof water reservoir operating policies. . J. Water. Resour.Plan. Manage. ASCE 133 (3), 202-209.

20. Nagesh Kumar, D., K. Srinivasa Raju, and Ashok, B.2006. Optimal reservoir operation for irrigation ofmultiple crop using genetic algorithms. J. Water. Resour.Plan. Manage. ASCE 132 (2), 123-129.

21. Marco, F. 1996. Use of a genetic algorithm combinedwith a local search method for the automatic calibrationof conceptual rainfall-runoff model. HydrologicalScience Journal, 41 (1), 21-39.

22. Marco, F. Galeati, G. and Berra, S. 1998. Globaloptimization techniques for the calibration ofconceptual rainfall-runoff models. HydrologicalScience Journal, 43 (3), 443-458.

23. Oliveira, R., and Loucks, D.P. 1997. Operating rules formultireservoir systems. Water Resour. Res., 33 (4), 839-852.

24. Preliminary study of Parbati-Kalisindh Link 2004.National Water Development Agency, New Delhi, F.R.P/9/04 vol., I & II.

25. Reis, F.R., Walters, G.A., Savic, D. and Chaudhry,F.H.2005 .Multi-reservoir operation planning usinghybrid genetic algorithm and linear programming (GA-LP): an alternative stochastic approach. J. WaterResources Manage. 19 (6), 831-848.

26. Reis, L. F. R., Bessler, F. T., Walters, G.A. and Savic, D.2006. Water supply reservoir operation by combinedgenetic algorithm-linear programming (GA-LP)approach. J. Water Resources Manage. 20 (2), 227-255.

27. Simonovic, S. P. 1992. Reservoir system analysis:Closing gap between theory and practice. J. Water.Resour. Plan. Manage. ASCE 118(3), 262-280.

28. Sharif, M., and Wardlaw, R. 2000. Evaluation of geneticalgorithms for optimal reservoir system operation. J.Water Resour. Plan.Manage. ASCE 15(2), 25-33.

29. Van, Z., Savic, D.A., and Walters, G. 2004. Operationaloptimization of water distribution systems using hybridgenetic algorithm. J. Water Resour. Plan.Manage. ASCE130(2), 160-170.

30. Wardlaw, R., and Sharif, M. 1999. Multireservoir systemoptimization using genetic algorithm: case study. J.Water Resour. Plan.Manage. ASCE 14(4), 255-263.

31. Wurbs, R. A. 1993. Reservoir-system simulation andoptimization models. J. Water

32. Resour. Plan. Manage. ASCE 119(4), 455-472.

33. Yeh, W. W.G. 1985. Reservoir management and operationmodels: A state-of-the-art review. J. Water Resour. Res.,21(12), 1797-1818.

34. Yen, M. T., Frank, T.C., Tsai, A.M., and Yeh W. G. 2005.Optimization of water distribution and water qualityby hybrid genetic algorithm. J. Water Resour. Plan.Manage. ASCE 131(6), 431-440.

35. Yuan Xiaohui., Yongchuan, Z. and Yanbin, Y.2008.Improved self-adaptive chaotic genetic algorithm forhydrogenation scheduling. J. J. Water Resour. Plan.Manage. ASCE 134(4), 319-325.


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REVIEWERS OF IWRS JOURNAL FOR THE YEAR - 2008 - 2009

A timely and critical review of the manuscripts that are submitted is very important for publication of the journal. Sucha review also provides useful and necessary feedback to the author. The Editor and Joint Editor of IWRS Journal are thankfulto the following experts who have reviewed the various manuscripts that were submitted for possible publication in the IWRSJournal during the year 2008-2009.

Dr. A.A.Kazmi, Deptt. of Civil Engg., IIT RoorkeeDr. A.K.Rastogi, DEC, IIT BombayDr. A.K.Lohani, Sci. NIH RoorkeeDr. A.K.Vashisht, GBPUT, PantnagarDr. Anupama Sharma, Sci. NIH RoorkeeMs. Archana Sarkar, Sci. NIH RoorkeeDr. Ashok Mishra, Deptt. of Agril. & Food Engg., IIT, KharagpurDr. Ashish Pandey, Deptt. of WRD&M, IIT RoorkeeDr. Arnab Bandyopadhyay.NIH, GuwahatiProf. B.C.Mal HOD, Deptt. of Agril. & Food Engg., IIT, KaragpurDr. D.C.Singal, Deptt. of Hydrology, IIT, Roorkee.Prof. Deepak Khare, Deptt. of WRD&M, IIT, RoorkeeMr. Deepak Jhajaria, Deptt. of Hydrology, IIT RoorkeeMr. Dilip Durbudhe, Deptt. of Hydrology, IIT RoorkeeProf. G.S.Rajput, College of Agri. Engg., JNKVV, JabalpurProf. G.C.Mishra, Deptt. of WRD&M, IIT, RoorkeeDr. Himanshu Joshi, Deptt. of Hydrology, IIT RoorkeeDr. J.V.Tyagi, Sci. NIH RoorkeeProf. K.L. Mishra, College of Agril. Engg., JNKVV, JabalpurProf. K.C.Patra, HOD, Deptt. of Civil Engg., NIT RourkelaDr. K.D.Sharma, NRAA, New DelhiProf. K.N.Tiwari, Deptt. of Agril. & Food Engg., IIT, KharagpurDr. K.P.Tripathi, CSWCRTIDr. M.J.Kaladhorkar, CSWCRTI, KarnalDr. M K Jain, Deptt. of Hydrology, Roorkee.Dr. M.P.Tripathi, IGAU, RaipurProf. M.K.Hardha, College of Agri. Engg., JNKVV, JabalpurDr. M.K.Goel, Sci. NIH Roorkee

Dr. M.L.Kansal, Deptt. of WRD&M, IIT, RoorkeeProf. M.Perumal, Deptt. of Hydrology, IIT, RoorkeeProf. M.P.Sharma, AHEC, IIT RoorkeeDr. N.C.Ghosh, Sci. NIH RoorkeeDr. N..K.Gontia, Junagadh Agri. University, JunagarhDr. N. Panighahi, NIH, RoorkeeDr. Nayan Sharma, Head, Deptt. of WRD&M, IIT RoorkeeDr. Omkar Singh, NIH, RoorkeeDr. P.K.Gupta, SAC, AhamadabadDr. Pramod Kumar, Deptt. of Civil Engg., IIT RoorkeeDr. P.K.Bhunya, Sci. NIH RoorkeeDr. P.K.Garg, Civil Engg. Deptt., IIT RoorkeeDr. P.P.Dabral, Deptt. of Agril. Engg., NERIST, NirjuliProf. R.K.Nema, College of Agri. Engg., JNKVV, JabalpurDr. R.D.Garg, Deptt. of Civil Engg., IIT RoorkeeDr. Ravi Galkati Deptt. of Agril. Engg., NERIST, NirjuliDr. Rakesh Kumar, NIH, RoorkeeDr. R.P.Pandey, Sci. El, NIH RoorkeeEr. R.K.Jain, CWC, New DelhiDr. Surjeet Singh, NIH, RoorkeeDr. Sanjay K.Jain, NIH, RoorkeeDr. S.K.Mishra, Deptt. of WRD&M, IIT RoorkeeDr. S.K. Tripathi, Professor , Deptt. of WRD&M, IIT, Roorkee.Dr. S.R.Bhakar, Deptt. of SAWE, CTAE, UdaipurDr. S. Moulik, Deptt. of Agril. & Food Engg., IIT, KharagpurDr. S.K.Jain , Deptt. of WRD&M, IIT, RoorkeeDr. U. C. Kothayari, Deptt. of Civil Engineering, IIT Roorkee.Prof. V.K.Pandey, Agril. Engg., IGAU, RaipurDr. Virendra K. Chaubey, Sci. NIH RoorkeeDr. Vijay Kumar, Sci, NIH Roorkee


55

Journal ofIndian Water Resources Society

Volume 30 Number 1 January, 2010

CONTENTS

Page

1. Protection of Bridge Abutments from Scour 1V. K. Sarda

2. Rainfall Pattern in Northern Kerala 10P. A. Lisha, P. K. Pradeep Kumar and K. V. Jayakumar

3. Sustainable Management of the Water Stressed 21Aquifers in Sabarmati Basin Gujarat, IndiaR. C. Jain And A. K. Sinha

4. Runoff Estimation by Distributed Curve Number Technique 31Using Remote Sensing And GISSusanta Kumar Jena, Kamlesh Narayan Tiwari and Ashish Pandey

5. Sediment Runoff Modeling using Artificial Neural Networks 39Archana Sarkar, M. Mohan Raju and Anil Kumar

6. Single Reservoir Optimization using Hybrid Genetic Algorithm 46Nitin M Mohite and Sandeep Narulkar


56


Journal ofIndian Water Resources Society

ISSN O970-6984

President

Er. A. K BajajChairman, CWC, New Delhi

Editor

Prof. Sharad Kumar JainDeptt. of WRD & MIIT, Roorkee - 247 667

Joint Editor

Dr. Ashish PandeyAssistant ProfessorDeptt. of WRD & MIIT, Roorkee - 247 667Er. Avinash Agarwal

IWRS as a body accepts no resoponsibility forthe statements made by the individuals/authors.

Further. views expressed by authors need notnecessary be the views of the organisation towhich they belong.

Reprints of any portion of this publication may bemade, provided that reference thereto is quoted.

This Journal is for private circulation only.

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Deptt. of Water ResourcesDevelopment & ManagementIndian Institute of Technology RoorkeeROORKEE - 247 667Uttarakhand, India

EDITORIALDear Friends,

These days, one frequently hears about climatechange in a wide range of forums: conferences,seminars, meetings, newspapers, television, etc.Any weather event which may have slight deviationfrom the general trend is attributed to climatechange. Thus, climate change is blamed for an intense rainfall event andso is drought in an area. Hot weather is certainly the result of globalwarming but big snow storms are also due to changes in climate. Certainlythis way of thinking is not correct and we need to address the issue witha rational approach that is based upon proven theory and is in conformitywith the observed data.

Climate change is also the subject of a large number of conferenceswherein this topic is being discussed from different perspectives. Althoughmuch noise is being generated in some of these events, hopefully therewill be many useful ideas and outcomes. These conferences also providean opportunity for interactions between people from academics, researchinstitutes, government organization, and political leaders. Suchinteractions should eventually lead to water management that is basedon sound scientific principles and is sustainable.

The National Action Plan on climate change launched by the Governmentof India in the year 2008 has proposed comprehensive steps to tacklethis problem through eight national missions. National Water Mission isone of these. Besides, water sector has important role to play in othermissions: Enhanced Energy Efficiency, Sustainable Habitat, Sustainingthe Himalayan Ecosystem, Sustainable Agriculture, and Green India. Agood aspect of these missions is that many programs and activities inthe water sector in India which should have been initiated two-three decadesearlier are proposed under these. In that sense, whether the climate changetakes place or not, proposed actions will be good for the nation. Theaction plan was launched with much fanfare but typical to the Indian wayof working, the actual progress so far is not very encouraging. One canonly hope that there will be actions on the ground which will change (forgood) the way water is managed in this country.

The topic of climate change was also under focus due to some alarmingstatements in the Fourth Assessment Report of IPCC which had predictedthat the Himalayan glaciers will disappear by the year 2035. This errorwas an aberration in the report which continues to be a valuable source ofinformation for scientists and policy makers.

(SKJain)


57

Registered with the Registrar News Paper of India, under Registration of Books Act. 1867 No. 61811/95

INDIAN WATER RESOURCES SOCIETYPAST PRESIDENTS

Sri R. Ghosh, Former Chairman, CWC New Delhi 1980-82,1990-92Sri Pritam Singh, Former Chairman, CWC New Delhi 1982-84Sri J. F. Mistry, Former Secretary, Gujarat Irrigation Departmem, 1984-86Dr. Mahesh Verma, Former Professors, Head, WRDTC, UOR, Roorkee, 1986-88Dr. M. A. Chitale, Former Secretary, MOWR, GDI, New Delhi, 1988-90Sri A. B. Joshi, Former Chairman, CWC New Delhi 1993-95Dr. M. S. Reddy, Former Secretary, MOWR, GOI, New Delhi, 1995-97, 1997-99SriZ. Hasan, Former Secretary, MOWR, GOI, New Delhi, 1999-2001Sri A. K. Goswami, Former Secretary, MOWR, GOI, New Delhi, 2001-05Er. M. Gopalakrishnan,Secretary General, ICID (2004 - till date) New Delhi, 2005-2009

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Printed &. Published by Dr. R. P. Pandey, Secretary, IWRS on behalf of the Indian Water Resources Society.Department of Water Resources Development & Management, Indian Institute of Technology, Roorkee-247 667, (UK)India Telephone : Office PBX (01332-276220, 286690)Printed at : Jain Printing Press, Main Bazar, Roorkee, Ph. : 262722, 269999Secretary : E-mail : [email protected] : E-mail : [email protected]

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President - Er. A.K.BajajExecutive Vice-President (HQ) - Dr. Ram Pal SinghVice President (Executive Office) - Er. R.C.JhaVice President - Er. S.K.KumarSecretary - Dr. R.P.PandeyJoint Secretary - Mr O.P.KhandaTreasurer - Dr. R.D.GargJoint Treasurer - Mr. Amrinder KumarEditor - Dr. S. K. JainJoint Editor - Dr. Ashish Pandey

MembersProf. Brijesh ChandraEr. A.S.DhingraEr. S.S.DoifodeDr. Arun GoelSri. Som Dutt GuptaEr. Madhu Chandra JainEr. N.K.JainDr. R. N. KhareProf. D.T.SheteEr. Ravindra K.Sinha

Co-opted MembersDr. Z. AhmadProf. Gopal ChauhanEr. M. L.GargEr. V. K.KanjiliaSri R. N.KalraEr. R. K.KhannaEr. M.S. MenonProf. S. K.MazumderEr. Jagdish MohanDr. R. D.SinghProf. C.P.SinhaMs. Jaya Sood


58


Journal ofIndian Water

Resources Society

ISSN O970-6984


59

Journal Article :Rao, S. S., 1979. Flood Control Regulation of Reservoirs. Journal of Indian Water Resources Society, 1 1(2), 19-26.

Chapter in Book or Paper in a Proceedings :Singhal, S. P. and Kasliwal, R., 1991. Calibration of Conceptual Catchment Models , in Applied Water Resources Planning,

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JOURNAL OF INDIAN WATER RESOURCES SOCIETYGUIDELINES FOR AUTHORS

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The Indian Water Resources Society (IWRS) is a multi-disciplinary organization dedicated to the advancement of thescience and technology of water resources development and management. The IWRS Journal publishes original papers onscientific, engineering and socio-economic aspect that are clear, concise and presented in a style readily understood by aninternational audience. Before publication, the papers are reviewed by the Editor/ reviewers. Detailed instructions for preparingmanuscripts are included in the following. A review of previous issues will be helpful in organizing the manuscript.

The text of the paper should be well organized and presented in a logical manner. The appropriate use of subheadings,especially in long sections, enhances the readability and quality of paper. Repetition of data given in tables and figures should beavoided. The paper should be written so that it will be of interest to persons in the wide variety of disciplines represented byIWRS’s membership. Complex sentences and the excessive use of highly technical jargon are prime offenders in detracting thereaders. The articles should have well-defined objectives, discussions, applications and conclusions that are easily understood.The papers not complying w these guidelines will be returned to the authors for improvement or publication elsewhere.

The acceptance or rejection of a paper is based on appraisal by the reviewers who examine the paper for its originality,relevance, adequacy and conciseness of the presentation. Depending on the results of reviewer, a manuscript may be returned tothe authors for revision. If the authors suitably revise the manuscript, it is accepted for publication. If the review comments areextensive, the authors should include a reply to the reviewer, in addition to making changes to the manuscript.

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Books :Gupta, S.K., 1999. Engineering Hydrology. Tata Mc Graw-Hill Publishers, New Delhi.

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