the use of suspended sediment rating curves in malaysia ... papers/pert vol. 12 (3) dec... · kelok...

Pertanika 12(3), 367-376 (1989)

The Use of Suspended Sediment Rating Curves in Malaysia :Some Preliminary Considerations

G. BALAMURUGANInstitute of Advanced Studies

University MalayaLembah Pantai, 59100 Kuala Lumpur

Key words : Suspended sediment, rating curves, regression analysis

ABSTRAKKelok kadar endapan ampaian masih merupakan kaedah yang paling biasa digunakan untuk menentukanbeban endapan ampaian di sungai-sungai di Malaysia. Dengan demikian, walaupun kaedah ini kasar danmempunyai banyak kelemahan, prosedur-prosedur yang betul perlu dipatuhi. Tiga kesilapan yang selalu di-lakukan semasa menggunakan kaedah ini telah dikaji dan dibandingkan dengan keputusan yang diperokhidengan menggunakan kaedah yang betul. Hasil kajian menunjukkan bahaxva kaedah yang salah telah me-nyebabkan keputusan beban endapan ampaian iahunan disalahtajsirkan iaitu lebih daripada 50%. Faktorpembetulan untuk mengambil kira pengunaan data kadar alir tidak berterusan juga diperolehi dalam kajianini.

ABSTRACTThe suspended sediment rating curve technique is the most common method of estimating river suspendedsediment loads in Malaysia. Thus, despite its crudeness, adherence to the correct procedures of this techniqueis very important. Three of the most common incorrect procedures in the suspended sediment rating curvetechnique were studied and compared to the results obtained from using correct procedures. Results of analysisshowed that incorrect practices of this technique underestimate the annual suspended sediment load by morethan 50%. A correction factor to compensate for the use of non-continuous discharge data is also derived inthis study.

INTRODUCTIONAs soil erosion and conservation is currentlythe subject of a great research effort, methodsof estimating soil losses are being widely ex-plored. The most common method in Malaysiain this respect is the use of river suspendedsediment load to indicate the magnitude of soilerosion although such measures do not takeinto account the retention of eroded materialon land, but in turn do include channel ero-sion.

River suspended sediment load can bedetermined by a number of methods. The useof theoretical and semi-theoretical equations,although quite common in more developedcountries, is hardly used in Malaysia. The morepopular method here is determining the sus-pended sediment concentration and multiply-

ing it with river discharge value. The sedimentconcentration is obtained by laboratory analy-sis of water samples and river discharge valuesobtained by velocity-area measurements or byinterpretation of stage readings. This method,although adequate for measurements of instan-taneous sediment loads, is of little help in thecomputation of annual suspended sedimentloads. In the typical Malaysian situation ofinadequate manpower, funds and automaticsampling equipment, it is almost impossible toobtain daily sediment concentrations in therivers for long periods of time. Considering theabove mentioned factors, many researchers inMalaysia (Peh 1981; Lai & Samsuddin 1985;Mohammad Noor 1987; Balamurugan 1987;Wan Ruslan 1989) utilize the suspended sedi-ment rating curve technique to estimate long-

G. BALAMURUGAN

term suspended sediment loads of rivers vis-a-vis soil losses.

This paper investigates the use of thesuspended* sediment rating curve technique,some of the common mistakes made by usersand the correspending consequences on theprocess of estimating suspended sediment loadsin Malaysian rivers.

SUSPENDED SEDIMENT RATING CURVEThe suspended sediment rating curve (orequation) is basically a relationship betweenriver discharge and suspended sediment con-centration or load. Instantaneous suspendedsediment concentration or load and river dis-charge values are plotted together to derive aline of best-fit. The best-fit line equation is thenused together with river discharge data toestimate suspended sediment transport rates orto analyze other sediment-related processes.

In Malaysia, the most common form ofrelationship assumed to exist between riverdischarge and sediment concentration or load(Sieh & Sivapakianathan 1977; Peh 1981) is

or

c = a Qb

L = a Qb

(1)

(2)

where C = suspended sediment concentrationin mg/1

L = suspended sediment load in tonnes/day

Q= river discharge in cumecsa = coefficientb • exponent

Due to scarcity of relevant data, multipleregression analysis to derive different forms ofrelationships involving variables other than theabove two have seldom been tested in Malaysia.

The most important assumption madewhen applying the suspended sediment ratingcurve technique is that the sediment concen-tration is dependent and only dependent onriver discharge, which is quite clearly untrue(Geary 1981; Walling 1974). Sediment trans-port rates have been shown to be governed bycomplex relationships involving sediment para-meters such as size, density and shape, flowparameters such as velocity, Reynolds numberand Froude number and channel parameters

like bed configuration channel slope andhydraulic radius (Lawson & O'Neil 1975; Ken-nedy & Brooks 1965). In addition to thesefactors, the availability of sediment, a majorfactor effecting the transport rates, is governedby many hydro-geomorphological factors suchas rainfall intensity-duration, topography andland use.

The assumption that suspended sedimentconcentration or load is dependent on riverdischarge or in other words the fact that sedi-ment content increases with discharge can beexplained by the following:

(a) Higher discharge means the occurrence ofheavier rainfall. Heavier rainfall normallyhas. higher erosivity, thus the cause forhigher sediment production.

(b) Higher discharge normally is accompaniedby higher velocity and increased sedimentcarrying capacity of the river.

Although discharge is not the only ex-planatory variable, it has been reported as themost important one in explaining sedimentconcentration variations (Imeson 1971). Stu-dies by Walling (1974) showed that dischargewas the most important single variable com-pared to many other variables including timerelation of sample to hydrograph peak, flowlevel preceding storm and index of flood inten-sity. However, several studies have shown thatother parameters such as rainfall, basin mor-phometry and land use may have a strongerinfluence on annual sediment yield (Jansen &Painter 1974; Dunne 1979; Ogunkoya &Jeje1987)

The above assumption also ignores thesediment exhaustion effect. In areas whereexhaustion effects constitute an importantcharacteristic of the suspended sediment res-ponse, the use of suspended sediment ratingcurves to calculate loads from streamflowrecords could give rise to significant errors(Golterman et al 1983). Furthermore, it is aninherent assumption of the rating curve thatthe water and sediment concentration peaksare synchronous (Walling 1978) which in realtime is usually not true, in that the suspendedsediment concentration - discharge curves usu-ally exhibit either clock-wise or anti clockwisehysterisis (Loughran et al 1986; Lootens &

368 PERTANIKA VOL. 12 NO. 3, 1989

THE USE OF SUSPENDED SEDIMENT RATING CURVES IN MALAYSIA

Lumbu 1986; Grimshaw & Lewin 1980; Klein1984). Furthermore, the lead or lag effects ofpeak sediment concentration with respect topeak discharge is also ignored (Grimshaw &Lewin 1980).

DATA LIMITATIONDue to the tediousness and large volume ofcomputations involved in using continuousdischarge data, it is common practice to incor-porate daily mean, weekly mean or monthlymean discharge values into the rating curves tocalculate annual sediment loads. These databeing averages of certain time series, have thepeakedness of flow smoothened. AT-day meanflow is the average flow over T-days. Thus it willonly represent the actual flow in a arithmeticrelationship, unlike in the case of the suspendedsediment rating curve where the relationship isgeometric. Thus the computed annual sedimentloads are actually underestimates when usedtogether with rating curves with 'b' values otherthan unity. In this respect, the use of any set ofdischarge data other than continuous valueswill underestimate the long-term sedimentloads. This effect will be more pronounced forsmall streams as compared to large rivers. Inlarge rivers, the maximum instantaneous flowon a particular day may only be slightly higherthan the daily mean flow but in small streams,the maximum values is usually very much higherthan the mean flow. Thus, the underestimationof sediment loads will be much greater in smallstreams compared to larger rivers.

In order to obtain the actual annualsediment loads, a correction factor K(b, T)corresponding to the values of 'b' and 'T' hasto be multiplied to the computed value. This isto compensate for the effect of smoothening offlow due to the use of mean flows. T representsthe number of days from which the mean flowvalues were computed; eg. for weekly mean flow,T « 7.

Actual load = K (b,T) x Computed load (3)

LINEAR REGRESSIONThe suspended sediment rating curve is usuallya line fitted to the logarithmic transformationof a set of river discharge and sediment concen-tration measurements. Most researchers use the

classical (least-squares) regression to derive thebest-fit line for the rating curve which actuallymay not be the proper type of regression to beused in this case. The classical regressionrequires at least one of the variables to besubjected to no or minimal errors in its mea-surement (Poole & OTarrell 1971) where asin the case of suspended sediment rating curves,both the variables are prone to errors inmeasurement (Loughran 1976; Walling 1977).Probably due to the abundance of publicationsand computer software on the classical regres-sion, this fact has been frequently over-looked.

* The least-squares regression of Y on Xassumes that X is the independent variable,and Y, the dependent one. In a given set ofdata, one should be able to demonstrate a cleardependence of one variable on the other. Forexample, the weight of a chicken depends onits age and not vice-versa (Till 1973). In theexample above, the age of the chicken in dayscan be determined without errors, thus theclassical regression can be applied in this case.In the case of the suspended sediment ratingcurve, both variables are subjected to errors ofmeasurement, thus the classical regression isinapplicable. Till (1973) has commented thatdespite such implications, most researchers havepersisted in using the classical regression, whenthe reduced major axis line is in fact the correctsort of linear fit to apply to such data.

The reduced major axis line (R.M.A.L) isused to obtain best-fit line between two vari-ables in cases where both are known to be withmeasurement errors. The R.M.A.L. sums theareas of the triangles between the set of dataand the line to a minimum (CDE in Fig. 1)whereas the classical regression sums the verti-cal distances of a point from the line to aminimum (AB in Fig.l). The computationprocedures of both the classical and the re-duced major axis line are shown in Table 1.

Besides the above, many researches havealso evaluated the rating curve in terms ofrelationship between sediment load (L) andriver discharge (Q) when the C-Q relationshipwill be more meaningful (Walling 1977). A highcorrelation between sediment load and riverdischarge is inevitable due to the spuriouscorrelation effects, in that the relationship

PERTANIKA VOL. 12 NO. 3, 1989 369

G. BA1AMURUGAN

Yi

Fig. I Different ways of fitting a straight line to a set of data (Till, 1973)

TABLE 1Linear regression computation procedures

Equation

Slope, b

Intercept, a

Coefficient ofcorrelation, r

where

Sxy

SxSe-

N

I(y-N

Classicalregression

y = ax + b

" ~Sx~a = y - bx

Sxy

x ) - ( y - y )

N

R.M.A.L.

y = ax + b

^"Sx"a ^ y - bR

Sxy

V SxSy



involves one variable (discharge) and theproduct of that same variable and a secondvariable (discharge x concentration). This couldlead to an incorrect significance of correlation,in that the L-Q relationship could be signifi-cant when the proper C-Q one might not be so.

EFFECTS OF ASSUMPTIONS AND DATALIMITATIONS ON COMPUTED

SEDIMENT LOADAn analysis was performed to study the effectsof the use of incorrect regression analysis andnon-continuous discharge data on the annualsuspended sediment loads calculated usingrating curves. The main aim of this study wasto obtain a general idea on the above effects,and thus only a small set of data was used. Thefollowing three effects were studied:

(a) Use of non-continuous streamflow recordssuch as daily mean and weekly mean flows.

(b) Use of classical regression instead of re-duced major axis line.

(c) Use of Load (L) - Discharge (Q) regres-sion instead of Concentration (C) - Dis-charge regression.

Use of Non-continuous Discharge DataAs explained earlier, the use of daily mean orweekly mean discharge records will under-estimate the annual suspended sediment loads.Thus, a correction factor (Eq. 3) has to beincorporated.

In order to obtain a preliminary set ofcorrection factors K(b,T), 3 years of dischargedata from the Drainage and IrrigationDepartment's (DID) river stations in the Kelangriver basin were analyzed. The stations and yearsused were

(a) Sg. Kelang @ Jambatan Sulaiman 1978(b) Sg. Kelang @ Lrg. Yap Kwan Seng 1982(c) Sg. Batu @ Sentul 1982

Hypothetical rating curve equations(Equation 2) with exponent 'b' values of 1.5,2.0, 2.5 and 3.0 were used to evaluate thereduction in annual suspended sediment loadscomputed with daily mean, 2-day mean, 7-day,10-day, 30-day, 50-day and annual mean dis-charge. The 'b' values were chosen to representthe typical exponent values, which usually rangefrom 1.0 - 3.0. The 'a' value was taken fb be 1.0because being the linear component of theequation, it will not effect the relative sedimentloads computed using the rating curves.

As expected, the computed annual sus-pended sediment loads decreased with theincreases in *b' and T values. Computationusing weekly mean discharge resulted in an-nual loads being 3% - 47% lower compared tothose computed using daily mean discharge.The use of monthly mean discharge producedannual loads which were 6% - 62% lower whilethe use of annual mean discharge resulted inannual loads being 11 % - 80% lower than thosecomputed using daily mean discharge records.The computed annual suspended sedimentloads are shown in Table 2.

As neither the continuous nor hourly datawere available to the author, the above findingswere used to derive a relationship to enablesediment load reductions to be predicted basedon *b' and 'T1 values. There was poor correla-tion between T and reduction factors (RF), soregression analysis were performed between thelogarithmic transformation of T and RF. Theresulting equations are as shown below. L(T) isannual sediment load derived using T-day meandischarge data.

Using the regression equations above, itwas possible to calculate the reductions insuspended sediment loads due to changes in'b' values and discharge data interval. Thecorrection factor K(b,T), which is the ratio

Equation Significance

1.52.02.53.0

RF = 1.0046 - 0.0568 log TRF= 1.0020-0.1340 log TRF = 0.9863-0.2112 log TRF = 0.9577 - 0.2740 log T

0.9150.9220.9180.908

0.010.010.010.01

RF = L(T) K(b,T) = RF( 1-hour) / RF(T)


G. BALAMURUGAN

TABLE 2Computed annual sediment loads

Dischargedata

Sg. Kelang @ Jambatan Sulaiman

Daily2-DayWeekly10-DayMonthly50-DayAnnual

Sg. Kelang @ Lrg. Yap Kwan Seng


Sg. Batu @ Sentul


1.5

16378161771580215782153611528714629

6140610159565872570956544873

6949679665146423629662536003

2.0

67692653326138561152570065627150062

20976206651947918765172911687311559

23165216481916118373173491701115266

'b' values

2.5

300272279989269615247628218215212763171322

79388774337009265447557805342527421

88092766066020755253493264745338823

3.0

1418363126410410570701042133859794823973586295

32329631189227048124318518770017609965050

37921029932820134017412014432013538898737

between actual sediment load and the com-puted value may be obtained through interpo-lation for various values of b and T. However,from the above equations, it is obvious thatthere will not be a K value for T = 0 (continuousdischarge data) and the values for t —»0 willbe very high. In this respect, T = 1 hour isassumed to represent continuous discharge. TheK values are shown in Table 3.

The K(b,T) values shown in Table 3 werederived using just 3 years of data and are thustentative and by no means universal. Further-more, no efforts were made to distinguish thedifferences in catchment sizes. More detailedstudies, involving catchments of varying sizesare required in order to establish applicablevalues. However, these results show that sus-pended sediment loads computed using daily

TABLE 3Correction factor K (b, T)

Discharge Data

Daily Weekly Monthly Annual

1.52.02.53.0

1.0781.1851.3001.395

1.1321.3351.5821.840

1.1761.4761.8952.416

1.2611.8022.8715.226



mean and weekly mean discharge data can be40% and 84% respectively less than the actualloads. This is comparab le with studies byLoughran (1976), where loads calculated usinghourly discharge data were 28% - 47% higherthan those calculated with daily mean data.

Classical Regression vs. Reduced Major Axis LineSediment concentration data from 2 DID

stations for the period 1980- 1984 were usedto analyze the discrepancies between sedimentloads computed with rating curves derived usingthe classical regression and the reduced majoraxis line. The two stations were Sg.Kelang @Lrg. Yap Kwan Seng and Sg. Batu @ Sentul. Therating curves are shown in Fig. 2 and Fig. 3. Thesuspended sediment rating curve equations thatwere obtained are as follows:

Sg. Batu Sg. Kelang

Classical C = 164.63 Q0834

regression

R.M.A.L. C = 87.48 Q1211

Coefficient

of correlation 0.705

Significance 0.01

C = 64.34 Q°-894

C = 31.79 Q1W5

0.641

0.01

The above equations were substituted intoequations 4 to derive equations to compute thesuspended sediment loads,

L = 0.0864 x C x Q (4)

where 0.0864 is the factor for converting theconcentration and discharge units into tonnes/day. The subsequent equations to computesediment loads and the computed annual sedi-ment loads are as follows :

Classical Lregression

Sediment load(tonnes)

R.M.A.L. L

Sediment load(tonnes)

Sg. Batu

= 14.22 Q18M

228698

= 7.56 <£•*"

302778

Sg.Kelang

L = 5.56 Q1 m4

89006

L = 2.75 Cp95

163759

In both cases, it was observed that thesediment loads computed using R.M.A.L. rat-ing curves were higher than those computedusing classical regression rating curves. At Sg.Batu, the sediment load calculated usingR.M.A.L. was 32.8% higher while at Sg. Kelang,

4 0

3-5

3 0

25

2 0

R-M-A-L-

Classical regression

-

0

1

0-2

l

04

i

0-

Log

\6 0 8

discharge (m

I

10

3 / s )

n

r

P

: 19: 0-= 0-

I

1-2

70501

[

1-4

I16

10

Fig. 2 Suspended sediment rating curve for Sg. Batu at Sentul


G. BALAMURUGAN

40

- 3 5

3 0

2-5 -

2 0 -

100 0 0 2 0 4 0 6 0 8 10

Log discharge (m 3 /s )

1-2 14

Fig. 3 Suspended sediment rating curve for Sg. Kelang at Lrg, Yap Kwan Seng

the increase was 84.0%. The R.M.A.L. producedhigher V values in the best-fit line, thus ex-plaining the increase in the computed annualloads. The higher 'a' values in the classicalregression equations do not compensate forthe lower 'b' values, thus resulting in lowerannual sediment loads.

C-Q regression vs. L-Q regressionRegression analyses using R.M.A.L. were per-formed between sediment load (L) and riverdischarge (Q) for both the stations. R.M.A.L.was used because the classical regression willproduce equations similar to those derived usingEq. 4. The regression equations obtained fromthe above exercise as compared to those de-rived using Eq. 4 and the resulting annualsediment loads are as follows :

As expected, the coefficients of correla-tion were higher for the L - Q regression, in-creasing from 0.705 and 0.641 to 0.907 and0.871 respectively. Although in this study, allregression were significant at p = 0.01, in othercases, the use of L - G regression may overlookthe fact that the proper C - Q regression mightnot be significant. At Sg. Batu, the use ofsediment load rating curves derived from L - Qregression produced an annual load of 263009tonnes compared to 302778 tonnes obtainedfrom using equations derived from C - Q re-gression or a reduction of 13%, At Sg. Kelang,it resulted in a reduction from 163759 tonnesto 124046 tonnes only, or 24%. Thus the useof L - Q regression instead of C - Q regressionunder-estimated the annual sediment load atboth stations.

Sg.Batu Sg.Kelang

C - Q regression

L - Q Equation 4

Coefficient of correlation

Sediment load (tonnes)

C = 87.48 Q1211

L = 7.56 Q2211

0.705

302778

C = 31.70 Q1396

L = 2.75 Q 2 : w

0.641

163759

L - Q regression

Coefficient of correlation

Sediment load (tonnes)

L = 10.17 Q

0.907

263009

L = 3.74 Q*

0,871

124046



Cumulative ErrorsThe study of three aspects of the suspendedsediment rating curve, namely the use of clas-sical regression instead of R.M.A.L., the use ofnon-continuous discharge data without correc-tion and the use of L-Q regression instead ofC-Q regression has shown that all three prac-tices underestimate the annual sediment loads.In order to evaluate the cumulative errors dueto the combined effects of the three practices,annual loads were calculated for both the sta-tions using both correct and incorrect proce-dures. The results are as shown below:

Incorrect method(no correction, use

of classical regression

Correct method

Load computed usingC-Q 8c R.M.A.L.Correction factor K(b,T)Corrected load

% differenceCorrect/Incorrect

Sg. Batu(tonnes)

228698

302778

1.229372114

62.7%

Sg. Kelang(tonnes)

89006

163759

1.271208138

133.8%

As expected, the combination of all threefactors produced annual sediment loads thatgrossly underestimated the actual sediment load.The use of R.A.M.L. and C-Q regression re-sulted in the computed annual sediment loadsat Sg. Batu and Sg. Kelang being 32.3% and84.0% higher respectively. The incorporationof the correction factors resulted in furtherincreases of 22.9% and 27.1%. The overallincrease in the computed sediment loads forSg. Batu and Sg, Kelang due to the use of correctprocedures were 62.7% and 133.8% respectively.

CONCLUSIONThe analyses using 3 years of discharge dataand sediment concentration data from 2 sta-tions permit several conclusions. The use ofdaily mean discharge or weekly mean dischargecan underestimate the annual sediment loadsby as much as 40% to 84% respectively. Thedifference is expected to be greater with the

increase in V values and decrease in catch-ment area. The use of rating curves derivedusing the improper classical regression canunderestimate the annual loads by 33% to 84%while the use of rating curves derived from L-Q regression instead of C-Q regression canunderestimate the annual loads by 13% - 24%.When all the three incorrect practices arecombined, the actual sediment load can be62.7% -133.8% higher than the computed ones.

Among the three incorrect proceduresstudied, the use of classical regression and theuse of L-Q regression can be easily rectified.However, the use of daily or weekly mean dis-charge data poses a bigger problem. To obtaincontinuous data is almost impossible and hourlydata difficult for an average practitioner inMalaysia, and therefore the use of daily meandischarge data will continue in the future. Thus,it is important that the calculated sediment loadsbe corrected in this respect. Much more re-search is required before the correction factorsderived in this study can be used in real-time.Although this study was based on only twostations, the findings require serious considera-tions as they indicate that previous publicationsof sediment yield data may be only half theactual value.

Despite all the above improvements, itmust be reiterated here that the suspendedsediment rating curve is still a crude method ofestimating sediment loads. Scarce samplingduring flood events and inadequate overallsampling will only produce rating curves whichare suspicious in terms of reliability of thecomputed loads. Furthermore, laboratoryprocedures and methods of sampling can alsoinfluence the final results. However, it must beemphasized that the rating curve is still themost important tool in Malaysia in terms ofestimating sediment yield. Until better me-thods are adopted, hydrologists and engineersshould adhere to the correct methods whenusing the suspended sediment rating curvetechnique.

ACKNOWLEDGEMENTThe author wishes to thank Prof. Ian Douglasfrom University of Manchester and Assoc. Prof.Low Kwai Sim from University of Malaya forcorrecting and commenting on an earlier draft


G. BALAMURUGAN

of this paper . T h e valuable comments offeredby two anonymous referees are also sincerelyappreciated.

REFERENCES

BALAMURUGAN, G. 1987. Kajian Endapan diLembangan Hulu Sg. Kelang. Unpubl B. Eng.thesis, Univ. Tech. Malaysia, Kuala Lumpur.

DUNNE, T. 1979. Sediment Yield and Land Use inTropical Catchments. J.HydroL, 42: 281-300.

GEARY, P.M. 1981. Sediment and Solute Yield ina Representative Basin. Australian Geogr. Studies,19(2): 161-176.

GOLTERMAN, H.L., P.G. SLY, 8c THOMAS. 1983.

Study of the Relationship between Water Qualityand Sediment Transport. Technical Papers in Hy-drology, 26, UNESCO, Paris.

GRIMSHAW, D.L. &J. LEWIN. 1980. Source Identi-

fication for Suspended Sediments. J. HydroL 47:151-162.

IMESON, A.C. 1971. Hydrological Factors In-fluencing Sediment Concentration Fluctuationsin Small Drainage Basins. Earth Set. J. 5: 71-78.

JANSEN, J.M.L. & R.B. PAINTER. 1974. PredictingSediment Yield from Climate and Topography./HydroL 21: 371-380

KENNEDY, J.F. & H.N. BROOKS. 1965. Laboratory

Study of an Alluvial Stream at Constant Discharge.In Proc. Federal. Inter-Agency Sedimentation Conf,USDA-ARS Misc. Publ. No. 970. pp. 320-330.

KLEIN, M. 1984. Ami Clockwise Hytheresis inSuspended Sediment Concentration during Indi-vidual Storms : Holbeck catchment, Yorkshire,Englang. Catena 11: 251-257.

LAI, F.S. & M. SAMSUDDIN. 1985. Suspended andDissolved Sediment Concentrations of two Dis-turbed Lowland Forested Watersheds in Air HitamForest Reserve, Selangor. Pertanika, 8(1): 112-115.

LAWSON, J.D. & I.C. O'NEILL. 1975. Transport of

Material in Streams. In Prediction in CatchmentHydrology (Eds. Chapman & Dunin), AustralianAcademy of Science, Canberra, pp. 165-202.

LOOTENS, M. 8c LUMBU, S. 1986. Suspended Sedi-ment Production in a Suburban Tropical Basin,Lubumbashi, Zaire. HydroL Sd. J. 31(1): 39-49,

LOUGHRAN, RJ. 1976. The Calculation of Sus-pended Sediment Transport from Concentration

v. Discharge Curves : Chandler River, N.S.W..CatenaS: 45-61.

LOUGHRAN, R.J., B.L. CAMPBELL and G.L. ELLIOTT.

1986, Sediment Dynamics in a Partially Culti-vated Catchment in New South Wales, Australia./ HydroL 83: 285-297.

MOHAMMAD NOOR, S. 1987. Sediment RatingCurves in Water Resources Assessments. Paperpresented at UNESCO/IHP Workshop on Impact ofOperations in Natural and Plantation Forests onConservation of Soil and Water Resources, Univ.Pertanian Malaysia, Serdang, Selangor, June 23-26.

OGUNKOYA, O.O. & L.K-JEJE. 1987. Sediment Yieldfrom Some Third Order Basins on the BasementComplex Rocks in Southwestern Nigeria. Catena14: 383-397.

PEH, C.H. 1987. The Suspended and DissolvedSediment Load of Three Small Forested Drain-age Basins in Peninsular Malaysia. Malay. Forester,44(4): 438-452.

POOLE, M.A. & P.N. O'FARRELL. 1971. The As-sumptions of the Linear Regression Model. TransInst. BriL Geogr. 52: 145-157.

SiEH, K.C. 8c K.SrVAPAKiANATHAN. 1977. The De-termination of Suspended Sediment Discharge.Hydrological Procedure No. 19, Drainage 8c Irri-gation Dept., Kuala Lumpur.

TILL, R. 1973. The Use of Linear Regression inGeomorphology. Area 5(4): 303-308.

WALLING, D.E. 1974. Suspended Sediment andSolute Yield from a Small Catchment Prior toUrbanization. In Fluvial Processes in InstrumentedWatersheds, Inst. Brit. Geogr. Special PublicationNo. 6, pp. 169-162.

WALLING, D.E. 1977, Assessing the Accuracy ofSuspended Sediment Ratting Curve for a SmallBasin. Water Resources Research, 13(3): pp. 531-538.

WALLING, D.E. 1978. Suspended Sediment andSolute Response Characteristics of the River Exe,Devon, England. In Research in Fluvial Systems (Eds.Arnott & Nickling), Geo Abstracts Ltd., Univ. EastAnglia, Norwich.

WAN RUSLAN, I. 1989. Hydrological Characteris-tics of Muda River Basin : A Focus on SedimentDischarge. Paper presented at Regional Seminar onTropical Forest Hydrology. Kuala Lumpur, Sept. 4-9.

(Received 21 August, 1988)


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