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Reliability of sediment rating curves for a deciduousforest watershed in IranSeyed Hamidreza Sadeghi a & Pari Saeidi aa Department of Watershed Management Engineering , College of Natural Resources andMarine Sciences, Tarbiat Modares University, International Campus , Noor, 46417-76489,Mazandaran, IranPublished online: 05 Jul 2010.

To cite this article: Seyed Hamidreza Sadeghi & Pari Saeidi (2010) Reliability of sediment rating curves for a deciduous forestwatershed in Iran, Hydrological Sciences Journal, 55:5, 821-831

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Reliability of sediment rating curves for a deciduous forestwatershed in Iran

Seyed Hamidreza Sadeghi & Pari Saeidi

Department of Watershed Management Engineering, College of Natural Resources and Marine Sciences, Tarbiat Modares University,International Campus, Noor 46417-76489, Mazandaran, Iransadeghi@modares.ac.ir; pari_saiedi@yahoo.com

Received 26 January 2009; accepted 22 March 2010; open for discussion until 1 January 2011

Citation Sadeghi, S. H. & Saeidi, P. (2010) Reliability of sediment rating curves for a deciduous forest watershed in Iran. Hydrol. Sci. J.55(5), 821–831.

Abstract The suspended sediment load in a river is an indicator of its health. Yet, the direct measurement ofsuspended sediment load is difficult. Most assessments of suspended sediment load are therefore estimated fromrelationships developed between suspended sediment concentration and discharge, as sediment rating curves.However, these are not able to provide sufficiently accurate results. The present study aimed to assess the accuracyof the sediment rating curve in the educational forest watershed of Tarbiat Modares University in northern Iran.Water and suspended sediment sampling were conducted via depth-integration at daily and storm-wise scales, andthe samples were analysed through decantation procedure. Towards this attempt, various sediment rating curveswere examined for different data sets classified on the basis of various governing conditions. The relationshipsbetween study variables were assessed by applying a bivariate regression method and descriptive statistics. Theresults of fitting rating curves on suspended sediment and water discharge, with respective averages of1.256 � 2.835 g L-1 and 0.556 � 0.444 m3 s-1, verified that no sediment rating curve could be reliably used forthe study area. Scrutinizing the results also verified that the data stratification and increasing time resolution led tobetter model performance, though their accuracy has not been statistically and logically tested.

Key words forest watershed; sediment rating curve; suspended sediment; regression model; Iran

Fiabilité des courbes de transport sédimentaire d’un bassin versant forestier feuillu en IranRésumé La charge de sédiments en suspension dans une rivière est un indicateur de sa santé. Pourtant, la mesuredirecte de la charge de sédiments en suspension est difficile. Les évaluations de la charge de sédiments ensuspension sont donc pour la plupart basées sur des relations développées entre la concentration de sédiments ensuspension et le débit, soit les courbes de transport sédimentaire. Toutefois, celles-ci ne donnent pas de résultatssuffisamment précis. La présente étude vise à évaluer la précision de transport solide dans le bassin versantforestier pédagogique de l’Université Tarbiat Modares dans le nord de l’Iran. Des prélèvements d’eau et desédiments en suspension ont été réalisés par intégration verticale, aux pas de temps journalier et événementiel, etles échantillons ont été analysés à travers une procédure de décantation. A l’occasion de cette étude, plusieurscourbes de transport sédimentaire ont été examinées vis-à-vis de différents jeux de données classifiées selonplusieurs règles. Les relations entre les variables de l’étude ont été évaluées en appliquant une méthode derégression bivariée et des statistiques descriptives. Les résultats de l’ajustement de courbes aux dynamiques dessédiments en suspension et des débits liquides, avec des moyennes respectives de 1.256 � 2.835 g L-1 et de0.556 � 0.444 m3 s-1, confirment qu’aucune courbe de transport sédimentaire ne peut être établie avec fiabilitédans la zone d’étude. L’examen des résultats permet également de vérifier que la stratification des données etl’augmentation de la résolution temporelle conduisent à de meilleures performances de modélisation, bien queleur précision n’ait pas été testée statistiquement et logiquement.

Mots clefs bassin versant forestier; courbe de transport sédimentaire; sédiment en suspension; modèle régressif; Iran

INTRODUCTION

The design and implementation of improvedcatchment-based erosion control and sediment man-agement strategies are frequently hampered by thelack of data on both erosion rates and sediment yields,

and an understanding of the processes associated withthe delivery of fine sediment through the catchmentsystem (Walling et al., 2001). A detailed study of soilerosion and sediment yield processes at the watershedscale is therefore necessary for the designation and

Hydrological Sciences Journal – Journal des Sciences Hydrologiques, 55(5) 2010 821

ISSN 0262-6667 print/ISSN 2150-3435 online© 2010 IAHS Pressdoi: 10.1080/02626667.2010.489797http://www.informaworld.com

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implementation of proper soil erosion and sedimentyield control projects. Comprehensive studies aim at abetter understanding of the rainfall–soil erosion–run-off–sediment system as a whole, by integrating bothnatural and anthropogenic aspects at different tem-poral and spatial scales. This finally leads to a properestimate of sediment yield needed for water manage-ment and erosion control structures, and river morpho-logical and evaluation studies (Kothyari et al., 1997;Lana-Renault et al., 2007; Sadeghi et al., 2008b;Wilkinson et al., 2009).

Sediment yield provides an important index of landdegradation, severity and trends, and also reflects thecharacteristics of a watershed, its history, development,use and management (Lane et al., 1997; Verstraeten &Poesen, 2001; Lu et al., 2005; Sadeghi et al., 2006,2008a). It is dependent on many controlling variablesincluding: climate, drainage area, soils, geology, topo-graphy, vegetation and land use (Mingguo et al., 2008).Total sediment load has been subdivided into bed andsuspended load (Cigizoglu, 2004), with suspended loaddominating (Walling & Webb, 1983). Suspended sedi-ment concentration (SSC) is controlled by the interac-tions among the processes of flow generation, transportcapacity, sediment delivery from external sources, andthe amount of fine sediment mobilized from internalsources varying over awide range of temporal and spatialscales (Gomi et al., 2005). Variability in these processesin turn is associated with variability in affecting factorssuch as: precipitation characteristics, the connectivity ofsediment sources, changes in contributing areas, andhydraulic boundary conditions (Schmidt & Morche,2006; Nadal-Romero et al., 2008; Sadeghi et al., 2008b).

Several attempts have been made to relate the SSCto flow conditions, but it is usually related to the dis-charge (Q) (Kisi, 2007). However, none of the equationshave received universal acceptance (Sadeghi et al.,2008b). In the absence of SSC measurements, hydrolo-gists have used sediment rating curves (SRC) to estimateSSCs (Horowitz, 2003). Suspended sediment ratingcurves have accordingly been widely used to estimatesuspended sediment when measured data are not avail-able (Asselman, 2000; Horowitz, 2003). Although manydifferent SSC–Q relationships have been established byformer researchers (e.g. Phillips et al., 1999; Yang et al.,2007; Sadeghi et al., 2008b) for developing SRCs, theoft-reported type of equation is a power regression(e.g. Walling, 1977a, b; Crawford, 1991; Gergov, 1996;Phillips et al., 1999; Asselman, 2000; Sadeghi et al.,2008b). Nonetheless, the good performance of someother types of equations with and without a “smearing”

correction, and application through sundry method-ologies, has also been reported (Ferguson, 1986;Asselman, 2000; Holtschlag, 2001; Horowitz et al.,2001; Horowitz, 2003; Sakai et al., 2005; Kao et al.,2005; Sadeghi et al., 2006, 2008b; Schmidt & Morche,2006). The storm intra- and inter-variations in SSC–Qrelationships, withmarked hysteresis, were also verifiedin different land uses, even in assumed balanced forestwatersheds (e.g. Thomas, 1988; Nistor & Church,2005; Gomi et al., 2005; Sadeghi et al., 2008b).Further improvements have been noted throughsubdividing calibration data sets into seasonal or hydro-logical groupings (Walling, 1977b; Jansson, 1996;Sichingabula, 1998; Asselman, 2000; Sadeghi et al.,2006, 2008b; Schmidt & Morche, 2006).

Based on past research on suspended sedimentdynamics over recent decades, it is understood that theapplicability of the SRC still needs further study, espe-cially in forest watersheds under strong complicatedconditions where a few studies (e.g. Thomas, 1988;Sorriso-Valvo et al., 1995; Terajima et al., 1996; Miuraet al., 2002; Croke et al., 2005; Lai & Detphachanh,2006; Sadeghi, 2008a,b) have been conducted. Theresults of all studies in forest ecosystems entirely verifiedthe complexities and variability governing the runoff–SSCrelationship. Thus, further site-specific endeavoursare required to understand the Q–SSC relationship inhyrcanian forests – the most commercial forest standin Iran (Sadeghi, 2005) – where the number of suchstudies is very limited and watershed sediment outputis still poorly understood. Therefore, the intention ofthis study is to remove the emphasis from the above-mentioned approaches, to discuss the state of knowl-edge and to assess the accuracy of the SRC in TarbiatModares University forest watershed, which is a repre-sentative hyrcanian forest in northern Iran. It washypothesized that no particular SRC could explainSSC variation in the study area owing to frequentand different human interference and uncertainties inthe hydrological behaviour of the study watershed.

MATERIALS AND METHODS

Study area

The present study took place in the educational forestwatershed of Tarbiat Modares University, brieflycalled Kojour watershed (36�3203300N latitude,50�4904000E longitude), encompassing 50 130 ha andlocated in Mazandaran Province, northern Iran(Fig. 1). Around 75% of the lower part of the

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watershed area is native deciduous forest, with thereminder developed mainly for livestock grazing inuplands. Land covers comprise Fagus orientalis,Alnus sp, Acer valetinum, Tillia begonifolia, Acercappadocicum, Parrotia persica and Diospyruslotus. The average density cover in forest stands andrangeland areas was about 75 and 50%, respectively(Nowshahr Natural Resources General Office, 2002).

Elevation ranges from some 150–2650 m a.m.s.l.More than 90% of geology formations belong to sec-ond geological era. The watershed is deeply incisedwith a dominant hillslope gradient of 25–60%. Soil inthe watershed is brown forest soil, which is classifiedas Pesdogelly with loamy sand texture, and its organic

matter content is about 0.089 g g-1. Maximum andminimum temperatures, mean annual precipitation,average maximal and minimal monthly rainfall are25 and 6.6�C, 1308.8, 280.4 and 37.4 mm, respectively,at Nowshahr plain meteorological station (36�2302400N,51�1800000E) and just 30 km away. The stated meteor-ological variables decrease as elevation increases, sothat the mean annual precipitation at Kojour station,located at the top-most point of the study watershed,declines to some 250 mm. The region including thestudy site has a humid subtropical climate with a dis-tinct dry season in winter at the lower part and semi-aridand cold at the upper areas of the watershed based onKöppen climate classification.

`

Sampling Site

Kojour Watershed

Sampling Point

Fig. 1 Location and general view of study forest watershed, northern Iran.

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Data collection In order to collect the mostreliable and comprehensive data, flow rate and SSCwere monitored at the main outlet, with the emphasison sampling major runoff events during a nine-monthperiod (24 October 2007 to 21 July 2008) at the dailyand storm event time scales. Discharge was calculatedby incorporating cross-section and flow velocitydata. The flow velocity was measured by means ofan OTT-1-170690 (MDs-Surfloat-LCD, SEBA)current meter. A calibrated surface-floating colouredtable-tennis ball, with reduction correction factor of0.79 obtained through comparison with velocity meterdata, was also applied at least three times for a traveldistance of some 25 m each time for the frequent andregular flow velocity measurements, particularlyduring storm events when the direct measurement ofthe flow velocity was impossible (Subramanya, 2001).Simultaneous scale readings were also made tocalculate flow discharge by using the flow cross-section, calculated based on water surface width anddepth at some fixed distance along the study cross-section, and considering trapezoidal-laws for thesection, and associated flow velocity. Calculation ofsuspended sediment load at the study site has beenbased on the analysis of depth-integrated water andsuspended sediment samples (Edwards & Glysson,1999; Rovira & Batalla, 2006) collected during thestudy period using 2-L capacity polyethylene containers(Das, 2000; Sadeghi et al., 2006). Samples wereregularly obtained during the flood at 1-hour intervalsand daily time scales within normal conditions. All dailysampling was carried out at 15 : 00 h local time(McConchie & Hawke, 2002; Sadeghi et al., 2006) toensure the probable runoff resulted from snow melting.Samples were transferred to the university laboratory toanalyse the SSC. The SSC values were then determinedthrough settling, decantation and drying processes (TVA,1941; Putjaroon & Pongboon, 1987; Walling et al.,2001). Towards this attempt, a sediment sample of 1-Lvolume was settled for 48 h and then the upper layerwater, over settled suspended sediment, was decanted.The settled sediment was then washed by distilled waterinto the pre-weighed aluminium foil dishes and ovendried at 105�C for 24 h. The dried sediment was thenweighed by means of high-precision scales (0.0001 g)and the SSCs were ultimately determined (Putjaroon &Pongboon, 1987; Sadeghi et al., 2006, 2008a).

Data analysis The SRC performance wasevaluated using the monitoring data by calibrating aSRC model for the watershed, which was run under

different classifications and grouping, and differentregression fitting procedures, viz. linear, quadratic,inverse, cubic, power, logarithmic, compound,S-shape, growth and exponential, were establishedfor the entire data collected during the study period.Various methods of sub-dividing the suspendedsediment data set were taken into account with fiveperiods: whole duration; sand mining (before, during,after and without sand mining operation), monthly,seasonal and periodical bases (Walling, 1977b; Walling& Webb, 1983; Lane et al., 1997; Sichingabula, 1998;Phillips et al., 1999; Horowitz, 2003; Rovira & Batalla,2006), rising and falling stages of storm hydrographs(Asselman, 2000; Hudson, 2003; Sadeghi et al., 2006,2008b) and threshold discharge (Thomas, 1988;Schmidt & Morche, 2006) to improve the reliability ofQ–SSC relationships. Also, the fact that theconcentration of suspended sediment and dischargegenerated during different conditions are not related ina unique manner was considered (Seeger et al., 2004).

Ultimately, different statistical models wereapplied for the study, including both the linear andthe nonlinear models using ordinary and transformeddata, viz. square root, inverse square root and theircombination. This have the general expression(Sadeghi et al., 2008b):

SSC ¼ f ðQÞ+ � (1)

where SSC represents the suspended sediment con-centration (g L-1), Q is corresponding water discharge(m3 s-1), and � is a residual error between observationand prediction. Different statistical criteria, viz. correla-tion coefficient (r), significant level of p value, standarderror of estimate (SE), relative and absolute error ofestimation (RE), coefficient of efficiency (CE), rootmean of square error (RMSE) and Akaike InformationCriterion (AIC), were calculated using equations (2)–(7)to evaluate the fitness, soundness and reasonability ofthe regression models (Asselman, 2000; Horowitz,2003; Sadeghi et al., 2008b) where N is the numberof samples (i ¼ 1, 2, . . .):

r ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1�

PNi¼1

ðSSCO � SSCEÞ2

PNi¼1

ðSSCO � SSCAÞ2

vuuuuuut (2)

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SE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPNi¼1

SSCO � SSCAð Þ2

NðN � 1Þ

vuuut(3)

RE ¼ SSCO � SSCE

SSCO

� �� 100 (4)

CE ¼PNi¼1

ðSSCO � SSCAÞ2 �PNi¼1

ðSSCO � SSCEÞ2

PNi¼1

ðSSCO � SSCAÞ2

(5)

RMSE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPNi¼1

ðSSCO � SSCEÞ2s

N(6)

AIC ¼ N ln2pN

� �þ N þ 2þ N lnReþ 2M (7)

where SSCO, SSCE, SSCA, N, Re and M are observedSSC, estimated SSC, observed mean SSC, number ofobservations in data set, residual sum of squares andthe number of model parameters, respectively.

The model with higher r as well as CE and smallerother criteria (p value, SE, AE, RMSE and AIC) wassupposed as the best performing model (Walling,1977b; Sichingabula, 1998; Asselman, 2000;Bozdogan, 2000; Horowitz, 2003; Sadeghi et al.,2008b). Negative RE indicates over-prediction,whereas positive RE indicates under-prediction rela-tive to the measured value. Besides that, the RE valueswere calculated for two data sets of calibration andvalidation stages assigned to each study period at arespective ratio of two thirds and one third of the dataseries (Green & Stephenson, 1986; Das, 2000).Coefficient of efficiency was supposed as a criterionthat determines the efficiency of a model in compar-ison with the average value. The positive values of CE(i.e. 0 to 1) indicate that the model estimate is betterthan the average measured concentration value andvice versa (Asselman, 2000). The AIC value in equa-tion (7) also shows that the reasonability of the regres-sion model consists of errors resulting from increaseduncertainties. It is also seen from the AIC relationship(equation (7)) that differences in the Re andM controlthe model performance.

RESULTS AND DISCUSSION

Raw data and descriptive statistics

In order to picture the entire variation of SSC versus Qunder different conditions of normal, storms andsand mining periods, all collected data have beendepicted in Fig. 2. The descriptive statistics of flowdischarge and SSC for the study watershed have beensummarized in Table 1. The scatter plot of the studyvariable for the entire data has also been shown inFig. 3.

These results shown in Table 1 and Figs 2 and 3indicated rather high variations in SSC (1.059 �2.662 g L-1) and discharge (0.556 � 0.444 m3 s-1).The value of SSC in the entire period ranged from0.03 to 16.672 g L-1 with coefficient of variation (CV)of 251.18%, whereas discharge variability was in therange of 0.06–1.62 m3 s-1 with CV of 79.86%. Itclearly verified high variation in both the study vari-ables with the higher rate for the SSC. This agrees withthe results of: Sichingabula (1998), from the FraserRiver, British Columbia, Canada; Kao et al. (2005)who studied mountainous rivers in Taiwan; Nadal-Romero et al. (2008) for a humid Mediterranean bad-land area; Sadeghi et al. (2008b) for a Japanese refor-ested watershed; and Sadeghi et al. (2009) for a longerstudy period in the same Iranian study watershed asthis study. It can be inferred from Table 1 that the highrelative variance of suspended sediment data in com-parison with discharge, demonstrates the significantand complex effects of changeful factors such as dis-charge on SSC, as reported by Lana-Renault et al.(2007). The analysis of grouped data sets, as shownin Table 1, resulted in a meaningful change in CV ofthe order of 13.1–94.8% and 28.6–414.8% for dis-charge and SSC, respectively.

Fitting the SSC–Q relationship

Scrutinizing Fig. 3 shows that no unique or individualrelationship can be easily fitted to the entire SSC–Qdata set due to high scattering of data over a large area.The results of application of different types of regres-sion fitting procedures established for the entire datacollected at study area are summarized in Table 2.

The results of the SSC–Q relationship analysis(Table 2) show that almost all of them were statisticallysignificant. However, the correlation coefficientbetween SSC and Q data was low. Furthermore, theestablished relationships had large values of estimation

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error and RMSE, and low and (in some cases) negativevalue of CE. High variability of regression coefficientsand parameters clearly indicated high intra- and inter-variability of SSC–Q relationships because of com-bined effects of variability of affecting factors of sedi-ment availability, rainfall characteristics and human

interferes during categorized data sets. Similar findingshave also been reported by Sadeghi et al. (2008a,b)through conducting a cause-and-effect analytical studyin a reforested watershed in Japan. The relative betterperformance of cubic regression model for entire timeseries and the period without sand mining activity as

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Fig. 2 Temporal variation of SSC and Q: (a) under different conditions of storm events; and (b) normal and sand miningperiods, for the study duration in educational forest watershed of Tarbiat Modares University, Iran.

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Table 1 Descriptive statistics for flow discharge (Q, m3 s-1) and suspended sediment concentration (SSC, g L-1) in theeducational forest watershed of Trabiat Modares University, Iran.

Descriptive statistic Variable Number ofdata

Minimum Maximum Mean SD Skewness Kurtosis

Study period Statistics StandardError

Statistics StandardError

Entire period(a) Q 257 0.060 1.620 0.556 0.444 0.871 0.152 -0.302 0.303SSC 257 0.030 16.672 1.059 2.661 4.068 0.152 17.128 0.303

Sand mining activityWithout Q 226 0.060 1.620 0.570 0.500 0.832 0.526 -0.322 0.322

SSC 226 0.030 14.690 0.431 1.104 0.162 10.352 127.350 0.322Before Q 38 0.200 1.140 0.797 0.288 -0.772 0.383 -1.004 0.750

SSC 38 0.260 14.690 0.952 2.322 5.897 0.383 35.640 0.750During Q 31 0.350 1.560 0.574 0.251 2.231 0.421 7.090 0.821

SSC 31 0.520 16.672 5.634 5.151 0.769 0.420 -0.532 0.821After Q 188 0.060 1.620 0.503 0.477 1.129 0.177 -0.045 0.352

SSC 188 0.150 6.194 0.325 0.291 6.193 0.177 57.088 0.352

Seasons (b)

Autumn Q 55 0.200 1.140 0.695 0.291 -0.003 0.322 -1.667 0.634SSC 55 0.260 14.690 2.420 3.610 1.789 0.322 2.826 0.634

Winter Q 82 0.360 1.620 1.006 0.366 0.494 0.266 -1.084 0.526SSC 82 0.150 16.672 1.496 3.247 4.051 0.266 15.765 0.526

Spring Q 89 0.060 0.600 0.187 0.088 2.588 0.254 9.677 0.506SSC 89 0.030 6.194 0.162 0.651 9.032 0.254 89.870 0.506

MonthsOctober/November

Q 25 0.490 1.060 0.947 0.124 -2.286 0.464 7.243 0.902SSC 25 0.260 1.980 0.556 0.373 2.757 0.441 8.371 0.858

December Q 30 0.200 1.140 0.485 0.213 1.985 0.427 4.193 0.833SSC 30 0.320 14.690 3.948 4.336 0.877 0.427 -0.624 0.833

January Q 30 0.360 1.560 0.853 0.342 0.922 0.427 0.234 0.833SSC 30 0.260 16.672 3.015 5.035 2.192 0.427 3.372 0.833

February Q 23 0.720 1.620 1.087 0.330 0.451 0.481 -1.465 0.935SSC 23 0.340 1.880 0.912 0.379 1.089 0.481 1.453 0.935

March Q 29 0.700 1.620 1.100 0.375 0.378 0.434 -1.791 0.845SSC 29 0.150 1.620 1.100 0.375 3.024 0.434 -1.791 0.845

April Q 27 0.060 0.540 0.175 0.122 2.054 0.441 3.612 0.858SSC 27 0.040 0.180 0.113 0.034 2.054 0.441 3.612 0.858

May Q 31 0.070 0.600 0.205 0.092 2.888 0.421 11.342 0.821SSC 31 0.030 0.120 0.069 0.220 2.888 0.421 11.342 0.821

June Q 31 0.130 0.250 0.181 0.029 0.910 0.421 0.543 0.821SSC 31 0.030 6.194 0.299 1.097 5.514 0.421 30.574 0.821

July Q 31 0.140 0.250 0.180 0.026 0.941 0.421 1.052 0.821SSC 31 0.030 0.090 0.056 0.016 0.328 0.421 -0.960 0.821

DischargeQ � 0.30 Q 120 0.060 0.600 0.184 0.076 3.090 0.221 13.427 0.438

SSC 120 0.030 6.190 0.135 0.560 10.807 0.221 117.875 0.438Q > 0.30 Q 137 0.200 1.620 0.881 0.370 0.437 0.207 -0.494 0.411

SSC 137 0.150 16.672 1.834 3.385 2.956 0.205 8.826 0.407

Storm eventsWhole data Q 75 0.020 1.440 0.636 0.386 -0.008 0.277 -1.233 0.548

SSC 75 0.050 47.630 5.150 9.106 -0.008 0.277 -1.233 0.548Rising limb Q 32 0.040 1.440 0.786 0.387 -0.482 0.414 0.813 0.809

SSC 32 0.050 33.380 5.014 7.928 2.468 0.414 6.119 0.809Falling limb Q 43 0.020 1.120 0.525 0.348 0.207 0.361 -1.302 0.709

SSC 43 0.080 47.630(c) 5.521 9.985 3.441 0.361 12.203 0.709

(a) These figures have only been obtained as per data recorded on daily basis at 15:00 h local time. (b) Season does not include data forsummer, since data collection was only done for July, with 31 SSC and Q data.(c) The maximum SSC has been recorded for the falling limb,since the study hydrograph preceded the associated sediment graph.

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well as quadratic relationship for July data set can beattributed to nonexistence of special pattern in the trendof the SSC–Q relationship as partially reported bySadeghi et al. (2008b).

The low positive values of CE approved the initialhigh variance in measured SSC data as emphasized by

Asselman (2000). This agrees with the results reportedby Sorriso-Valvo et al. (1995) in connection with poorcorrelation between annual average discharge andSSC from the forested north facing slopes in a smallCalabrian watershed. It is comparable with Abrahamset al. (1988) who reported a nonsignificant and nega-tive Q–SSC relationship on six runoff plots on deserthillslopes in southern Arizona with annual precipita-tion of 288 mm. This finding also conforms to those ofSchmidt & Morche (2006), that the correlationbetween SSC and Q is generally poor (regressioncorrelation of 30–80%) in two small mountainousBavarian Alps watersheds with areas <17.5 km2,slope <85% and annual precipitation <2000 mm inGermany, and of Sadeghi et al. (2008b), who foundsimilar results in theMie watershed, Japan (area<5 ha,slope approx. 85% and annual precipitation approx.2094 mm). However, few studies (e.g. Sichingabula,1998; Horowitz, 2003; Sadeghi et al., 2006, 2008b;

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Fig. 3 Scatter plot ofQ–SSC data relationship for the entiredata collected in the educational forest watershed of TarbiatModares University, Iran.

Table 2 Final regression models in relationships between suspended sediment concentration (SSC, g L-1) and discharge(discharge, m3 s-1) in the educational forest watershed of Tarbiat Modares University, Iran.

Study period Model r p value SE Absolute RE (%) RMSE CE AIC

Validation Estimation

Entire period SSC ¼ 20.40Q – 21.54Q2 + 7Q3 – 2.20 0.33 0.00 2.84 455.41 537.32 2.81 0.11 861.95

Sand mining activityWithout SSC ¼ 6.38Q + 11.28Q2 – 4.56Q3 + 1.23 0.17 0.22 2.06 295.91 251.42 2.03 0.03 661.19Before SSC ¼ 1.93Q – 0.48 0.20 0.30 2.69 162.10 145.34 2.59 0.04 138.85During SSC ¼ 2.77Q + 7.90 0.19 0.40 5.731 303.48 313.09 5.45 0.04 136.81After SSC ¼ 0.25Q + 0.32 0.07 0.45 1.90 318.14 310.49 1.87 0.01 525.86

SeasonalAutumn SSC ¼ –4.49Q + 6.10 0.33 0.03 3.76 311.82 436.62 3.66 0.12 217.92Winter SSC ¼ –2.30Q + 4.40 0.23 0.09 3.82 294.06 101.15 3.75 0.05 307.38Spring SSC ¼ –0.65Q – 0.68 0.10 0.46 2.72 554.51 483.63 2.68 0.01 294.38

MonthsOctober/Nov. SSC ¼ 0.50Q-0.38 0.13 0.59 0.53 32.73 26.43 0.39 -0.01 33.31December SSC ¼ 3.072Q0.477 0.14 0.56 1.48 130.71 60.40 5.63 1.00 76.29January SSC ¼ 2.944exp(–0.688Q) 0.20 0.41 1.36 131.76 201.10 6.21 1.00 73.07February SSC ¼ 0.194 ln(Q) + 0.912 0.14 0.61 0.37 42.01 26.12 0.41 0.02 22.91March SSC ¼ 0.303Q0.538 0.26 0.24 0.46 49.99 44.06 0.42 0.80 46.20April SSC ¼ –0.022 ln(Q) + 0.072 0.34 0.15 0.02 29.20 17.85 0.30 -10.82 -73.50May SSC ¼ – 0.050Q + 0.062 0.23 0.33 0.05 29.91 34.67 0.02 0.05 -77.57June SSC ¼ 2.811/Q – 14.393 0.55 0.01 0.98 2532.08 4308.90 3.37 0.30 124.80July SSC ¼ –1.540Q + 3.959Q2 + 0.203 0.25 0.01 0.02 25.10 23.72 0.02 0.06 -109.62

Threshold dischargeQ � 0.30 SSC ¼ 6.92Q – 4.03Q2 – 0.63 0.26 0.04 3.76 297.82 390.83 3.70 0.07 851.94Q > 0.30 SSC ¼ –0.01 ln(Q) ¼ 0.05 0.18 0.45 0.06 42.85 54.54 0.06 0.03 -76.01

Storm eventsWhole data SSC ¼ 1.65Q 0.59 0.31 0.03 0.92 97.45 112.90 0.90 0.53 136.98Rising limb SSC ¼ 2.80Q1.51 0.65 0.00 0.39 340.65 137.59 1.54 -0.12 87.31Falling limb SSC ¼ –1.33Q + 4.72 0.12 0.55 2.19 1942.67 1364.44 1.70 0.22 223.87

828 Seyed Hamidreza Sadeghi & Pari Saeidi

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Schmidt & Morche; 2006) have proved the capabilityof other types of regression models, rather than thepower equation, in explaining theQ–SSC relationship.This may be because of the complexity of governingconditions, such as sand mining activities in the vici-nity of the sampling site (outlet), as is verified by thedescriptive statistics in Table 1. The differences in AICvalues reported in Table 2 can also be ascribed toerrors resulting from increased uncertainties in eachstudy period. These are mainly controlled by numberof data (N) and residual sum of squares (Re), since thenumber of parameters (M) had a very small variation.This is in full agreement with Asselman (2000), whostated that the number of model parameters only has asignificant effect on the AIC when regression is car-ried out on a limited number of observations.

The subdivision of the data set based on miningactivities also led to non-significant models with lowgeneral performance. The reason behind this findingcould be associated with the wide scatter of data withinthe sub-data sets. The sub-classification of Q and SSCdata on monthly and seasonal bases, despite increasingthe precision of SRCs, could not improve the statisticalperformances of the output functions. This conformedto the results of Nadal-Romero et al. (2008), whobelieved that, at the monthly scale, there was no sig-nificant linear relationship between runoff/precipitationdepths and suspended sediment transport. Other subdi-viding of the data set according to flow components andfollowing the same procedure conducted for the entiredata could just improve the statistical performances ofthe functions developed for whole storm data and risinglimb data sets. The negative value of CE and highrelative errors in falling limb showed that increase ofsediment transport trend was not steady through thedischarge increase. The subdivision of data set basedon threshold discharge of below and more than0.3 m3 s-1 resulted in the respective quadratic andlogarithmic models with the correlation coefficient of0.262 (p < 0.040) and 0.184 (p < 0.448), respectively.These findings also verified that even the sub-classification of data set on the basis of flow dischargecould explain fairlywell theQ–SSC relationship, whichdisagrees with Sadeghi et al. (2008b) who found noimprovement in SSC–Q relationship after subdividinga data set on discharge basis in theMie watershed, Japan.According to equations and rating curves in Table 2, itis also found that, in most cases, other types of modelsthan the power function gave a better performance.This opposes the findings of Walling (1977a,b),Gergov (1996), Cordova & Gonzalez (1997), Phillips

et al. (1999), Asselman (2000), Kao et al. (2005) andSadeghi et al. (2008a), who believed power models tobe the most common function that relates SSC to waterdischarge. However, it agrees with Asselman (2000),Horowitz et al. (2001), Horowitz (2003), Sadeghiet al. (2006), and Schmidt & Morche (2006), whofound that the other types of regression models(e.g. linear, quadratic, cubic, three-parameter power,exponential and quadratic) sometimes performed bet-ter than simple power type equations.

The results of this study clearly show the inadequateapplicability of the SRC in estimation of suspendedsediment concentration in the study forest watershed.Similar findings have also been reported for other loca-tions by Walling (1988), Church & Slaymaker (1989),Lane et al. (1997), Terajima et al. (1996), Sichingabula(1998), Asselman (2000), Horowitz (2003), Sadeghiet al. (2008a) and Wilkinson et al. (2009). The poorcorrelation between SSC and Q data can be attributedto: variation in the temporal rainfall intensity distribution;suspended sediment supply from internal sources(i.e. material stored within the channel system fromperennial and ephemeral reaches) and external sources(i.e. bank erosion, mass movement, excavation, road andtrail disturbances, and surface erosion on slopes)(e.g. Gomi et al., 2005; Sadeghi et al., 2008b); antece-dent moisture conditions in the watershed; and differ-ences in sediment availability at the beginning or end of aflood (e.g. Walling & Webb, 1983; Sadeghi et al.,2008b).

CONCLUSION

The objective of this paper was to assess the accuracy inestablishing suspended sediment rating curves. Theresults of analysis of 257 suspended sediment and dis-charge paired data collected in a 9-month study periodshowed that, in the area where flow conditions changecontinually, data have high scatter and the sedimentrating curves are highly scattered around the regressionline. Neither the rating function types nor their classifi-cation based on different criteria was able to adequatelydescribe the SSC–Q relationship, which clearly provesthe hypothesis considered for the present study. It cantherefore be concluded that the developed models arejust statistically and fairly reliable whenever relativelyconstant conditions govern the study watershed. It isalso understood that a proper assessment of sedimentyield in forest ecosystems, despite the general low rateof soil erosion and comparatively sustainable governingconditions, needs collection of high-resolution data in

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such a way to be sure that all different circumstances areprecisely considered and governing conditions are wellformulated. Further extensive studies with longer andcomprehensive hydrological and climatological datasets for the same study area and other forest watershedsare accordingly advised, before drawing a final conclu-sion on the study watershed as a representative ofhyrcanian forest in Iran.

Acknowledgements The authors gratefully acknowl-edge Engrs M. Kiani Harchegani, M. Moatamedniaand S. Boor for their valuable assistance during fieldsampling, data collection and laboratory work. Thevaluable institutional support of Mazandaran RegionalCenter forWater Resources is also greatly appreciated.

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