Development, evaluation and interpretation of sediment rating curves for a Japanese small mountainous reforested watershed

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Geoderma 144 (2008) 198 – 211 www.elsevier.com/locate/geoderma

Development, evaluation and interpretation of sediment rating curves for a Japanese small mountainous reforested watershed S.H.R. Sadeghi a,⁎, T. Mizuyama b , S. Miyata b , T. Gomi c , K. Kosugi b , T. Fukushima d , S. Mizugaki d , Y. Onda d a

Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Mazandaran, Noor 46417, Iran b Laboratory of Erosion Control, SABO, Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan c Geo-hazards Division, Disaster Prevention Research Institute, Kyoto University, Gokasho Uji, Kyoto 611-0011, Japan d Laboratories of Environmental Modeling and Creation, and Geomorphology, Department of Integrative Environmental Science, Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan Received 17 January 2007; received in revised form 7 July 2007; accepted 5 November 2007 Available online 11 January 2008

Abstract Extensive reforestation using Cypress, Pinus and Cedar has widely taken place in Japan since early 1960s in attempt to fulfill national wood demands and control soil erosion. In 2004 one small headwater mountainous reforested watershed encompasses 4.8 ha was established in Mie Prefecture in Japan to monitor the hydrological response. Frequently and automatically collected values of discharge and suspended sediment concentration were examined for the main outlet and five other stations, spanning from June 2004 to July 2006. In the present study, the different functional linear and non-linear sediment rating curves analytical methods were employed in the investigation of ordinary and some transformed flow discharge–suspended sediment concentration relationships for the study watershed using from 48 to 162 simultaneous discharge–sediment records (2004–2006). According to the results of statistical analyses using different criteria and out of many types of regression functions and data transformation, power rating curve by least square regression on fourth root transformed flow discharge and suspended sediment concentration data performed well. Contrary to what is oft-reported, the best fitted rating curves to the entire data collected for each individual station excessively overestimated the suspended sediment concentration in the study area by 113–430%. The temporal and magnitude stratification of flow discharge and sediment data, flow components separation as well as employing bias correction factor did not improve the relationship, while better estimates were obtained when power regression was applied to the fourth root transformed data separated based on their locations on rising and falling limbs of hydrographs. Results also showed a complex suspended sediment concentration and flow discharge relationship in different subwatersheds of the study reforested watershed reflecting the effect of different physical local characteristics, sediment availability, contribution of various hydrologic cycle components, and subtle variation of soil hydrophobicity on runoff generation and consequently sediment supply. © 2007 Elsevier B.V. All rights reserved. Keywords: Suspended sediment; Sediment rating curve; Reforested watershed; Hydrophibicity; Subsurface runoff; Sediment availability

1. Introduction ⁎ Corresponding author. Tel.: +98 122 62 53101 3 (office); fax: +98 122 62 53499. E-mail addresses: [email protected], [email protected] (S.H.R. Sadeghi), [email protected] (T. Mizuyama), [email protected] (S. Miyata), [email protected] (T. Gomi), [email protected] (K. Kosugi), [email protected] (T. Fukushima), [email protected] (S. Mizugaki), [email protected] (Y. Onda). 0016-7061/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2007.11.008

Issues such as the transport of the pollutants adsorbed onto the fluvial sediments, water quality trends, reservoir sedimentation, channel and harbor silting, soil erosion and loss, as well as the ecological and recreational impacts of sediment management imply the need to understand the occurrence of suspended sediment transport (Walling, 1977b; Ferguson, 1986; Abrahams et al., 1988; Williams, 1989; Horowitz, 1995, 2003; Hasnain,

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1996; Lane et al., 1996, 1997;Sichingabula, 1998; Phillips et al., 1999; Asselman, 2000; Lenzi and Marchi, 2000; Horowitz et al., 2001; Alvarez and Jones, 2002; Alexandrov et al., 2003; Pathak et al., 2004; Kao et al., 2005; Rovira and Batalla, 2006). Moreover, as a ‘watershed wide’ measure of soil erosion, transport, and deposition, sediment yield reflects the characteristics of a watershed, its history, development, use, and management (Lane et al., 1997). In the absence of actual suspended sediment concentration measurements, hydrologists have used sediment rating curves to estimate suspended sediment concentrations (Horowitz, 2003). A sediment rating curve describes the average relation between discharge (Q) and suspended sediment concentration (SSC) for a certain location. Despite of increasing interest in suspended sediment dynamics over recent decades, it is still imperfectly understood, especially in forest watersheds. However, few studies of suspended sediment transport have been conducted in forest watersheds under strong complicated conditions (e.g., Thomas, 1988; Sorriso-Valvo et al., 1995; Terajima et al., 1996; Miura et al., 2002; Croke et al., 2005; Lai and Detphachanh, 2006). Although there are some 22 methods for developing sediment rating curves (Phillips et al., 1999), the most common is a power function (regression) that relates SSC to Q (e.g. Walling, 1977a,b; DeVries and Klavers, 1994; Gergov, 1996; Phillips et al., 1999; Asselman, 2000) but some other type of equations may also perform well (Asselman, 2000; Horowitz, 2003; Sadeghi et al., 2006; Schmidt and Morche, 2006). Linear or second-order polynomial sediment rating curves, with and without a ‘smearing’ correction could also calculate very good results for the annual or longer term suspended sediment fluxes (Horowitz et al., 2001). The multi-objective optimization through minimization of root mean square of error was also successfully applied by Sakai et al. (2005) for the development of SSC analysis model on two agricultural watersheds in Okinawa. These methods include extrapolation and interpolation, with potential additional modifications using various correction factors (Duan, 1983; Ferguson, 1986; Holtschlag, 2001; Kao et al., 2005). Further improvements have been noted when calibration data sets have been subdivided into seasonal (e.g. wet/dry) or hydrological (e.g. rising limb/falling limb) groupings (Walling, 1977b; Hansen and Bray, 1993; Sichingabula, 1998; Asselman, 2000; Sadeghi et al., 2006; Schmidt and Morche, 2006). Forests cover approximately 65% of all land in Japan, and cypress (Chamaecyparis obtuse, Hinoki) plantations occupy about 10% of this forest cover. In Mie Prefecture, where this study was conducted, cypress plantations comprise 50% of the reforested area. Most of the Japanese cypress trees were planted for commercial use in headwater areas, which also serve as source areas for groundwater recharge. The study has therefore been focused on the area where, despite of high density Japanese cypress, surface runoff and soil erosion are the serious concerns for forest management in Japan (Miura et al., 2002, 2003; Miyata et al., 2007). This study investigates the character of the relationships between suspended sediment concentration and discharge and factors controlling and influencing the variation in sediment behavior for the Mie watershed (4.8 ha) as

199

an example for small and privately managed reforested watershed in Japan. The study objectives can be classified in two. The first objective is to develop, assess and interpret the best logically and statistically performed suspended sediment and discharge relationship for the study watershed. Secondly, the spatial differences in the relationship between discharge and suspended sediment concentration for the different subwatersheds will be determined. Questions that need to be answered in this regard include ‘Which rating curve produces the most accurate estimates of the suspended sediment concentration in the study stations?’, ‘What technique or procedure may help to reduce the scatter?, ‘How the developed rating curve is interpreted?’, and ‘How does the shape of fitted curves differ between subwatersheds and why?’ This study provides a welldocumented case that can be contrasted with studies of suspended sediment dynamics in other areas and under different land uses. 2. Materials and methods 2.1. Study area This study took place at the Hinotani-ike watershed (34°21′ N latitude, 136°25′ E longitude; 4.8 ha) located in Mie Prefecture, south central Japan (Fig. 1), and hereafter is briefly called as Mie. Elevation ranges from some 105 to 260 m above mean sea level. The watershed is deeply incised with a dominant hillslope gradient of 70 to 100%. The main riverbed is also composed mainly of consolidated bedrocks. Soils in the watershed are brown forest soils ranging in depth from 0.6 to 1.8 m. The soil is classified as Cambisol with light clay texture, and its density and organic matter content within top 10 cm is 0.78 g cm− 3 and 0.20 g g− 1, respectively. The soil has little or no A0-horizon, a 1-cm thick A-horizon, a 25-cm thick Bhorizon, and an approximately 35-cm thick C-horizon, overlying well-cleaved schist (Gomi et al., submitted for publication; Miyata et al., 2007). Average temperature, mean annual precipitation, average maximal daily rainfall, and average maximal hourly rainfall from 1979 to 2004 are 14.4 °C, 2094 mm, 211 mm, and 42.5 mm, respectively, at Kayumi meteorological station (34°27′ N, 136°23′ E), which is located approximately 9 km from the watershed. The region including the study site has a Cfa climate (i.e., humid subtropical with hot summers based on Köppen climate classification) typically with a rainy season from late June to mid-July (Baiu season) and a typhoon season from September to October. The forest is dominantly 40 years-old stand of Japanese cypress (Hinoki, C. obtusa) with a few small inclusions of Japanese cedar (Sugi, Cryptomeria japonica) and broad leaf forest (Gomi et al., submitted for publication). The stand density ranges from 1500 to 4000 stems ha−1. Japanese cedar was planted only along the stream channels and is not dominant in the watershed (Miyata et al., 2007). The average density cover was also estimated some 76 to 78% through image processing. In such dense cypress forest, sparse understory vegetation was found because of the limited light conditions produced by a dense cypress canopy. Dominant understory vegetation is coral fern (Gleichenia

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Fig. 1. Feature, topography and location of monitoring stations in Mie study watershed, Japan.

japonica). Moreover, the forest floor was poorly covered by litter because litter of Japanese cypress separates easily into small pieces, and, then, is likely carried away by surface runoff or mixed with surface soils (Miura et al., 2002, 2003). The denudation rate because of dominant interrill erosion in the upper stream was found between 0.6 and 1.1 mm y− 1 (i.e. 4.6 to 8.6 t ha− 1 y− 1) through measuring natural index of exposed trees roots. The rate of soil erosion almost corresponded to the value obtained through the calculation of sedimentation rate of some 7.5 t ha− 1 y− 1 in the

irrigation small earthen dam, and constructed immediately outside the watershed and receives the entire watershed output. A nested monitoring rainfall, runoff and sediment concentration network was installed in the study watershed since spring 2004. The maximum and average rainfall intensities for the study period were 108 mm h − 1 (January 2006) and 0.322 mm h− 1 for the 5 min intervals, respectively. The instrumented watershed was monitored only at outlets of seven subwatersheds besides the main watershed outlet, producing

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average runoff and sediment discharge at watershed scale. The suspended sediment concentration data were ultimately analyzed at the main outlet and five more stations, since no sediment samples were taken at other two stations. The location and some of important physical characteristics of study subwatersheds have been shown in Fig. 1 and Table 1. 2.2. Methods 2.2.1. Data collection The precise precipitation data was continuously collected for the entire period (June 2004 to July 2006) with the help of recorder instrument (Tipping bucket, Model 7852, Davis) nested adjacent to the main outlet. Continuous discharge data (with 5 min interval) were calculated from in situ stage recorder at the P1 to P8 stations (SE-TR/WT500, trutrack at P2, P4, P5, P7 and P8, and SE-TR/WT1000, trutrack for P1, P3 and P6) installed at the main outlets of the entire watershed and study subwatersheds (Fig. 1). Stage was then converted to discharge based on a flume stage–discharge relationship (Hann et al., 1996). Discharges resulted from the storm events were sampled in fixed interval (half an hour) by means of the Sigma 900 Standard Portable Automatic Sampler. The sampler and associated accessories were installed at the study points as per the procedure of instrument application. The sampler was submerged and fixed at the center of the river thalweg, approximately where the mean flow channel velocity is usually reached. Therefore samples were always collected at the same point in the cross-section. Some other suspended sediment concentration samples were also supplemented manually instream using milk bottles for base flows and small floods compatible with depth-integrating procedures (Edwards and Glysson, 1999). The SSC samples were collected for the same period with more intensive data during June to December 2004 because of less quantity of precipitation and subjecting the area to a partial deforestation in 2005 and 2006, respectively. Samples were transferred to the lab on a weekly or fortnightly basis and based on the frequency of rainfall occurrences. Standard Whatman 0.47 μm pore diameter glass microfiber filters were used to filter samples. The filter papers were first pre-burnt for 4 h at 450 °C. The SSC analytical method for determining

Table 1 Some of physical characteristics of Mie watershed and study subwatersheds Station

P1

P2

P3

P4

P5

P8

4.78 71 N.E.

1.12 92 1500

3.5 80 N.E.

0.23 82 3500

0.43 88 4500

0.18 87 N.E. Quercus glauca Thunb 100

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sediment concentration in water samples was applied to analyze sediment samples. The SSC analysis then proceeded with the help of the electric pump. The filter papers including sediment and the organic matter were dried in oven during 24 h at 110 °C and pre/post weighed by means of high precision scale. The organic burning was also performed through burning the dried filter papers for 4 h at 450 °C (Knott et al., 1993) and necessary calculations were consequently conducted. In order to understand the watershed soil erosion and sediment yield processes and validate the results and findings, the field surveying and investigations were extensively conducted during the summer 2006. 2.2.2. Data analysis The analysis of the suspended sediment was structured in order to (a) obtain the best performed statistical relationship (rating curve) between the SSC and the Q for the entire watershed and gauged subwatersheds and (b) to compare the developed rating curves and interpret them. In order to explore the best performed Q–SSC rating curve, different regression fitting procedures (linear, quadratic, inverse, cubic, power 2 and 3 parameters, logarithmic, compound, s-shape, growth, exponential 2 and 3 parameters, sigmoid, hill 3 and 4 parameters, and Chapman 3 and 4 parameters) were established for the entire data collected at study stations. Various methods viz. subdividing the suspended sediment dataset monthly, seasonally and periodically (Walling, 1977b; Walling and Webb, 1983; Hansen and Bray, 1993; Batalla and Sala, 1994; Lane et al., 1996; Mossa, 1996; Sichingabula, 1998; Phillips et al., 1999; Horowitz, 2003; Rovira and Batalla, 2006), flow components separation (i.e. base flow and direct runoff in case of station P1), rising and falling stages (Asselman, 2000; Hudson, 2003; Sadeghi et al., 2006) and mean and median discharge and SSC and optical evaluation of scatter cloud have been developed to improve the Q–SSC relationships. It was because of considering the fact that the concentration of suspended sediment and discharge generated during flood events are not normally homogenous, and the curve representing sediment concentration vs. discharge through time is often a hysteretic loop (Seeger et al., 2004). Ultimately, seventeen statistical models with the general form of Eq. (1) were applied for the study, including both the linear and the non-linear models using ordinary and transformed data, viz. square root, inverse square root, third to fifth root, and their combination as well. SSC ¼ f ðQÞ þ e

ð1Þ

Variable Area (ha) Slope (%) Stand density (Stem ha− 1) Dominant forest type Understory density (%) Understory vegetation

Japanese cypress N.E.

100

N.E.

85

10

N.E.

Fern

N.E.

Fern

Spares

N.E. = Not evaluated.

Fern and Eurya japonica Thunb

Where SSC represents suspended sediment concentration (mg l− 1), Q is an indicator for corresponding water discharge (m3 s− 1), and ɛ is a residual error between observation and prediction. In this study, regression analysis was applied using other types of transformation rather log transformation of the sediment concentration and discharge data, since log transformation has been generally reported to lead to underestimation of river loads (Ferguson, 1986; Phillips et al., 1999; Kao et al., 2005; Rovira and Batalla, 2006). The transformed data were then back

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transformed to obtain the real SSC values for comparison purposes. To overcome the tendency of logarithmic transformation in which the ‘best fit’ curve is biased in favor of points close to the origin of the coordinate system (Pattyn and Van Huele, 1998), different correction factors in the detransformation (Jansson, 1985) were also introduced to reduce the bias. In the study watershed, where the suspended sediment concentrations were mainly overestimated and the assumption of lognormally distribution of residuals was not met, the alternative approach of additive error (Asselman, 2000; Kao et al., 2005) was considered for the study. The new smearing or bias correction factor of non-log transformed units (β) was then calculated using the following equation as the sum of residuals divided by the sum of total predictions (Kao et al., 2005): N P

b¼

i¼1 N P

ðei Þ ð2Þ

i¼ 1 to N f ðQÞ

i¼1

ficient of efficiency was supposed as a criterion that determines the efficiency of a model in comparison with the average value. The positive values of R2 (i.e. 0 to 1) in Eq. (5) indicates that the model estimate is better estimate than the averaged measured concentration value (Asselman, 2000). The AIC value also shows the reasonability of the regression model and embraces a penalty for increased complexity. It is seen from the Eq. (6) that the differences in model performance depend on differences in the residual sum of squares and the number of model parameters. The numbers of the model parameters only has a significant effect on the AIC when regression is carried out on a limited number of observations (Asselman, 2000). The better understanding of the governing conditions on and controlling factors of the sediment yield process in the study watershed was also achieved through comparing the best performed Q–SSC regression functions and hyetograph–hydrograph-sediment graph relationship at all hydrometric stations. The data bank development, statistical calculations and graphical presentations were supported by EXCEL, SPSS 14.01 and Sigma 9 software packages.

Where N is the number of observations in dataset. A corrected relationship between SSC and Q was then given by:

3. Results and discussions

Corrected SSC ¼ ð1 þ bÞSSC

3.1. Descriptive statistics

ð3Þ

Different statistical criteria viz. correlation coefficient (r), significant level of p-value, standard error of estimate, relative and absolute error of estimation, coefficient of efficiency, and Akaike's Information Criterion (AIC) were applied to evaluate the fitness, soundness and reasonability of the regression models. The following equations were used to calculate error of estimation (RE), coefficient of efficiency (R2 ) and AIC (Asselman, 2000; Horowitz, 2003). RE ¼

SSCo  SSCE  100 SSCo N P

R2 ¼

ðSSCo  SSCA Þ2 

i¼1

ð4Þ

N P

ðSSCo  SSCE Þ2

i¼1 N P

ð5Þ

ðSSCo  SSCA Þ2

i¼1

 AIC ¼

   2p N ln þ N þ 2 þ N ln Re þ 2M N

ð6Þ

Where SSCO, SSCE, SSCA, Re and M are observed SSC, estimated SSC, mean SSC, residual sum of squares and the number of model parameters, respectively. The model with higher correlation coefficient and smaller other criteria was supposed as best performed model (Walling, 1977b; Sichingabula, 1998; Asselman, 2000; Bozdogan, 2000; Horowitz, 2003; Wang and Liu, 2006). A minus sign in Eq. (4) indicates an overprediction, whereas a positive sign indicates an underprediction relative to the actual (measured) value. Coef-

The analysis of the suspended sediment concentrations collected in the Mie watershed provides insight into the characteristics of the suspended sediment load variability in a small reforested Japanese watershed. The descriptive statistics of flow discharge and suspended sediment concentration for the study stations (Fig. 1) have been summarized in Table 2. Despite the fairly persistent vegetation cover conditions, suspended sediment concentration response in this 4.8 ha watershed is highly variable. The mean value of SSC for the collected data is beyond 30 mg l− 1 for the entire watershed (Table 2) which is between the maximum values of some 4 and 84 mg l− 1, respectively on north (with good tree cover, little grass, but continuous leaf litter and runoff coefficients b 21%) and south (with little ground vegetation and runoff coefficients from 27 to 37%) facing slopes of plot size studies located in small logged Mediterranean Calabrian watershed (Sorriso-Valvo et al., 1995) where Pinus and Eucalyptus were afforested. This is also comparatively more than that reported by Lai and Detphachanh (2006) as about 9 mg l− 1 for a 2.6 km2, undisturbed headwater rainforest Sungai Pangsun catchment in Malaysia with the annual long-term mean precipitation of 2514 mm and average catchment slope of some 45%. 3.2. Fitting suspended sediment concentration and discharge relationship The initial results of application of different types of Q–SSC relationships in the Mie watershed showed that almost all different types of models and using various transformations established good Q–SSC relationships in view point of correlation coefficient criterion. But, as it has already been mentioned, many other criteria would also be satisfied for the

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Table 2 Descriptive statistics of flow discharge and suspended sediment concentration for the Mie Prefecture watershed (2004–2006) Station

P1

Variable

Q (m3 s− 1)

SSC (mg l− 1)

Q (m3 s− 1)

SSC (mg l− 1)

Q (m3 s− 1)

SSC (mg l− 1)

Q (m3 s− 1)

SSC (mg l− 1)

Q (m3 s− 1)

SSC (mg l− 1)

Q (m3 s− 1)

SSC (mg l− 1)

0.0744 0.0460 0.0134 0.0177 0.0068 0.0803 140 0.5041 0.0051

30.4756 5.1818 13.6661 0.0041 6.9118 81.7814 140 662.0909 0.2000

0.0203 0.0142 0.0031 0.0107 0.0016 0.0199 162 0.1180 0.0002

27.6806 8.0909 7.8972 0.0003 3.9989 50.8973 162 388.0400 0.3333

0.0364 0.0200 0.0081 0.0015 0.0041 0.0421 107 0.2116 0.0000

36.1839 6.7273 23.3208 0.0006 11.7625 121.6725 107 1102.6667 1.8182

0.0019 0.0017 0.0002 18.0525 0.0001 0.0010 72 0.0052 0.0007

13.4216 4.8182 5.4026 10.4278 2.7095 22.9906 72 124.3939 0.2424

0.0059 0.0039 0.0011 30.8553 0.0006 0.0053 85 0.0204 0.0005

79.8233 19.6250 25.8324 7.1721 12.9899 119.7614 85 524.6061 0.0667

0.0019 0.0013 0.0005 34.2391 0.0002 0.0016 48 0.0071 0.0008

10.2409 4.6667 5.1920 6.9288 2.5808 17.8804 48 106.2424 0.5152

Statistics Mean Median 95% Confidence 99% Confidence Standard error Standard Deviation Size of dataset Maximum Minimum

P2

P3

recognition of the best performed model. The logarithmic, quadratic, cubic and then exponential 2 and 3 parameters have been removed at the very early stages of analysis owing to bearing too large values of estimation errors, though some of which had got the maximum correlation coefficient at the former step. This may be because of either nonexistence of special pattern in the trend of the defined relationship or resulting biased estimates at time of data detransformation as experienced by Smith (2005). Very few former studies (e.g. Sichingabula, 1998; Horowitz, 2003; Sadeghi et al., 2006; Schmidt and Morche, 2006) have proved the capability of some types of aforesaid models in explaining Q–SSC relationship. It was also found that the some other transformation of both the Q and SSC data improved statistical criterion, since the variance of the data decreased. The results of the application of AIC to the models with comparatively better performance showed that sigmoid, hill 3 and 4 parameters, and Chapman 3 and 4 parameters could not also produce the reasonable results. The reason behind this finding could be referred to the shape of the graphs. The beginning parts of these functions were likely to be zero (in 3 parameter models) or a constant value (in 4 parameter models) near to zero (for the present dataset) whose difference with the very small frequent measured values and accordingly estimation error was low. The absolute estimation error of near to 100% also verified the foresaid explanation. The last parts of these functions were also almost constant which could not perfectly mimic natural conditions and based on AIC were ultimately evaluated as unreasonable models. According to AIC, it therefore verified that the power models could explain both types of errors caused by modeling, and done by estimation of the parameter vector (estimation error) as classified by Bozdogan (2000). This agrees Walling (1977a,b), DeVries and Klavers (1994), Gergov (1996), Cordova and Gonzalez (1997), Phillips et al. (1999), Asselman (2000) and Kao et al. (2005) who believed in power models as the most common function that relates SSC to water discharge. While it opposes Asselman (2000), Horowitz et al. (2001), Horowitz (2003), Sadeghi et al. (2006) and Schmidt and Morche (2006) who found that the other types of regression models (e.g. linear, quadratic, cubic, 3 parameters power, exponential and quadratic) have sometimes performed better than power type equation. Ultimately, among many types of functions, the power function of 4th root of all Q and SSC data with a very slight difference from

P4

P5

P8

the 5th root transformed data was chosen as the best approximating model for describing the entire data Q–SSC relationship for the Mie watershed, based on the overall performance evaluated using statistical criteria of r (52.54%), P-value (1% level of significance), standard error of estimation (0.701), RE (−152.455%), R2 (0.321) and AIC (230.893). The positive values of R2 also verified that the developed power models provide better estimates than application of the average values for estimation of SSC. The low positive values of R2 approved the initial high variance in measured SSC data as emphasized by Asselman (2000). The wide difference of one to four orders in SSC among ten stands (C.obtuse, C.japonica, Pinus densiflora and deciduous hardwood forests) has also been reported by Miura et al. (2002). The large variance of initial dataset could be minimized, at most, using 4th root transformation technique. The other power function of 3rd root, square root and ordinary data stood in next priorities which clearly affirmed the importance of data transformation in controlling dataset variance. The scatter plots of Q–SSC relationship has been shown in Fig. 2. The subclassification of Q and SSC data in monthly, seasonally and periodically (Baiu and typhoon) bases, according to flow components, the mean and median values of Q and SSC data as well as optical evaluation, and following the same procedure conducted for the entire data could not improve the statistical performances of the output functions. While the attempt made for subdividing datasets based on their hydrological flow conditions i.e. base flow (in case of station P1), rising and falling limbs of hydrographs for each individual station could explain the Q–SSC relationships better. The inapplicability of sediment rating curves without decomposition has been noticed by Walling (1977a and b), Walling and Webb (1982), Ferguson (1986, 1987), Thomas (1991) and Schmidt and Morche (2006). The smearing correction factor of β was then calculated and incorporated in the final power transformed models obtained during earlier stages. The β values were also found out very small, i.e. from − 0.00261 to +0.00762 respectively belong to rise P3 and all P5 data, and therefore the performance of the developed models have not been improved by considering bias values, since the estimated values have not been distributed normally around the regression line. This result is supported by the same statement given by Wang and Liu (2006). The

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Fig. 2. Scatter plot of Q–SSC data relationship for the entire data collected for stations P1 to P5 and P8 in Mie watershed, Japan.

following general form of power function was finally applied for all and categorized data. SSC ¼ ð1 þ bÞ  aQb

ð7Þ

The detailed statistical characteristics of the final models and corresponding figures have been shown in Table 3 and Fig. 3. 3.3. Interpretation and comparison of sediment rating curves Very interesting findings can be interpreted from the results reported in Table 3 and Fig. 3. Although the introduced sediment rating curve models are statistically, reasonably and

logically accepted, but they do not perfectly characterize suspended sediment transport in the study small mountainous reforested watershed. The inadequate applicability of sediment rating curve in prefect estimation of suspended sediment has also been reported by Walling (1988), Church and Slaymaker (1989), Hickin (1989), Lane et al. (1996), Sichingabula (1998), Asselman (2000) and Horowitz (2003). This is comparable with the results reported by Sorriso-Valvo et al. (1995) in connection with poor correlation between annual average discharge and suspended sediment concentration from the forested northfacing slopes in a small Calabrian watershed. It agrees Schmidt and Morche (2006) who found that the correlation between SSC and Q is generally poor (regression correlation of 30 to 80%) in

Table 3 Details and performance evaluation of sediment rating curve power models based on fourth root transformation of the data and introducing bias correction factor in Mie different subwatersheds, Japan Stations

Data

No. of data

r

a

b

Std. Error of estimation

PValue

P1

All Base flow Fall Rise All Fall Rise All Fall Rise All Fall Rise All Fall Rise All Fall Rise

140 31 66 43 162 75 87 107 47 60 72 34 38 85 45 40 48 16 32

52.51 33.59 65.21 39.87 56.62 61.27 54.82 41.98 28.50 62.69 24.19 33.94 10.32 48.51 53.88 13.58 35.45 90.10 51.70

3.543 0.316 2.711 3.730 5.775 4.960 5.461 3.278 2.734 4.176 5.176 5.395 2.773 8.052 5.882 2.509 5.897 21.155 7.484

0.938 − 1.167 0.908 0.566 1.086 1.106 0.905 0.583 0.611 0.674 0.742 0.914 0.239 0.894 0.880 − 0.201 0.844 1.908 0.899

0.701 0.390 0.291 0.819 0.616 0.452 0.632 0.710 0.554 0.677 0.597 0.336 0.623 0.960 0.570 0.792 0.494 0.160 0.408

0.0001 0.0647 0.0001 0.0081 0.0001 0.0001 0.0001 0.0001 0.0521 0.0001 0.0407 0.0496 0.5380 0.0001 0.0001 0.4036 0.0134 0.0001 0.0024

P2

P3

P4

P5

P8

β-Value

Error (%) Relative

Absolute

− 152.45 − 185.05 − 40.81 − 81.21 − 125.52 − 67.46 − 119.41 − 113.79 − 60.67 − 75.54 − 223.30 − 107.54 − 163.93 − 430.69 −189.74 − 103.46 − 148.12 − 19.19 − 38.98

193.67 243.97 79.97 123.22 170.71 100.66 173.12 156.75 93.82 116.72 270.22 159.19 214.88 476.13 228.75 148.39 188.15 53.39 72.44

0.00316 0.00051 0.00139 0.00047 0.00550 0.00362 0.00472 0.00526 0.00093 0.00762 − 0.00008 − 0.00041 − 0.00002 − 0.00261 − 0.00113 0.00003 0.00113 0.00068 0.00044

Error (%) after introducing β Relative

Absolute

− 155.66 − 185.63 − 41.59 − 81.55 − 126.76 − 68.07 − 120.44 − 114.91 − 60.82 − 76.88 − 223.28 − 107.45 − 163.09 − 429.30 − 189.41 − 103.47 − 147.40 − 19.27 − 39.04

194.42 244.43 80.50 123.52 171.76 101.08 170.61 157.66 93.93 117.84 270.20 159.12 214.87 474.83 228.47 148.39 188.36 53.41 72.49

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Fig. 3. Sediment rating curves for Mie watershed and its subwatersheds (P1 to P5 and P8) in Japan for all, fall and rise subset data.

two small mountainous Bavarian Alps watersheds with areas b 17.5 km2, slope b 88% and annual precipitation b 2000 mm in Germany. It also conforms to Abrahams et al. (1988) who reported a nonsignificant and negative (− 31 to − 69%) relationship between Q and SSC on six runoff plots ranging in gradient b 65% established on desert hillslopes in southern Arizona with annual precipitation of 288 mm. It is seen in Table 3, the separation of the entire data into two subsets representing the rising and falling limbs of the hydrographs improved the Q–SSC relationship and ultimately led to more reliable models using which the error of estimation was substantially decreased. The simple linear and non-linear regression analysis for collected data, showed that, overall,

sediment concentrations as a group was more related to discharge and hydrograph characteristics on the falling than rising stages. This finding is more valid for the falling limb because of more limited sediment availability (Walling and Webb, 1982) and approaching steady conditions. This finding is similar to that found out by Sichingabula (1998). Therefore, it is proposed that sediment concentrations on the rising and falling stages that are ostensibly similar cannot be described by an overall linear or non-linear single-valued curve. In the other word the composition of calibration sets can have a significant impact on suspended sediment rating curves in the study watershed. It is not axiomatic that long-term data sets from the same site, even when generated by consistent methods,

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represent a single statistical population. This assumption should be carefully evaluated prior to using all the data to produce a single sediment rating curve as advised by Horowitz (2003) for the estimation of long-term suspended sediment fluxes. Although, the preferred sediment-curve method tended to underpredict high, and overpredict low SSCs as mentioned by Horowitz (2003), but scrutinizing the relative error -SSC relationship showed that the best performed power models, like other acceptable models, significantly overestimated as an average with the error of estimation beyond − 113, − 19 and − 38% for all, fall and rise datasets, respectively, which opposes some former scientist (e.g., Jansson, 1985; Ferguson, 1986; Walling, 1988; Cohn et al., 1992; Sichingabula, 1998; Asselman, 2000; Holtschlag, 2001; Horowitz, 2003) who believed the sediment rating curves likely underestimate. It also ostensibly mismatched mathematical rule of smaller values of geometric means of dependent variable (SSC) compare to arithmetic means when they were transformed to logarithmic units (Smith, 2005), but it was mainly because of much more frequent small SSC as represented in median value (Table 2) resulting from the same flow discharges and then consequent overprediction estimates. The difference of the sediment yield and transport regime on the shape of the sediment rating curve (Asselman, 2000) was also checked through plotting the regression coefficients of best performed models for different data subsets in a graph. Since the relationship between regression coefficients (a and b) was not strong enough, it showed the dissimilarity of sediment yield process in the different flow conditions under consideration and also showed that the subcalssified data did not have the same mean values of both values (SSC and Q) to which a line be perfectly fitted as mentioned by Thomas (1988). It opposes the similarity of sediment yield process oft-reported by former researches e.g. Walling (1977b), Fenn et al. (1985), Thomas (1988) and Assleman (2000) for the data set analyzed for Creedy, Exe and Dart in England, proglacial stream of de Tsidjiore Nouve in Switzerland, North Fork of the Mad River in California and river Rhine and its main tributaries, respectively. The imperfect regression coefficients relationship obtained for Mie reforested watershed also disagrees Thomas' (1988) finding who also worked in forest watershed, since he sub-sampled a data record of discharge and suspended sediment concentrations at a single gauging station and fitted rating curves through the simulated data sets. However, as all rating curves are based on the same period of record it can be expected that the simulated data sets will have similar discharge and suspended sediment concentration values. Hence, the rating curves fitted through the data sets will have one point in common and the slope/intercept parameters will plot on a straight line. In overall, the steepness of the fitted rating curves is relatively low which is related to the availability of suspended sediment in the study area, in combination with the erosive power of the runoff to transport this material. This finding can be attributed to the progressive exhaustion of fine sediment loosened by natural reasons (weathering and raindrop impacts) as mentioned by Abrahams et al. (1988) and anthropogenic (frequent traffic and soil disturbance) prior to runoff. It showed that most limited available eroded sediment washed out from the watershed during

initial runoff phase stage. Whereas the steep rating curves are expected to be characteristic for rivers where most sediment is transported at high discharge as reported by Asselman (2000). The overall pattern of data scatter seems to emphasize that the suspended sediment load is a non-capacity load and is mainly controlled by supply, rather than transport capacity. The importance of sediment availability in controlling Q–SSC relationship has also been emphatically noticed by Walling and Webb (1982). The variable behavior of the Mie watershed in wash load production is therefore controlled by suspended sediment concentration and runoff interaction (Sorriso-Valvo et al., 1995; Hasnain, 1996) while both runoff and sediment yield are strongly influenced by rainfall, sediment availability and soil hydrophobicity conditions in the study watershed. The major influence of forest species on runoff has been well demonstrated by MataixSolera and Doerr (2004) in Spain and Miyata et al. (2007) in the study area. Very high eroded sediments may contribute at the beginning of the incident when the soil is was mainly water repellent and sufficient sediment resources are available to the surface water. The negative relationship between time and surface runoff flow established for the study area (Miyata et al., 2007) is also another evident proving the effects of soil hydrophibicity on controlling runoff generation which ultimately affects on sediment transportation process and then sediment rating curve efficiency. During initial flushing, concentration is very high, but thereafter it declined steadily through the event as discharge increased (Sorriso-Valvo et al., 1995, and Rovira and Batalla, 2006). The contribution of three important components of hydrologic cycle viz. interception, throughfall and interflow (subsurface flow or pipe flow) may be also supposed as other controlling factors for Q–SSC relationship in the Mie watershed. The field studies verified that the throughfall, which initially abstracts some 40% of rainfall as reported for similar forest stands (Nanko et al., 2004), attains contribution 20 to 30 min after the onset of rain. The contribution of interflow through soil macropores and pipes along the river bank in a short distance of about 30 m between P1 and P3 (Fig. 1) was also proved by field studies and hyetograph–hydrograph relationship analysis. Very well distinguished exits of the pipes were seen at the left river bank. The inclined bedding of schist formations towards main thalweg, apparently observed in the field, could also direct subsurface runoff. A view of exist holes and geologic bedding has been shown in Fig. 4. An average value of less than 79% of instantaneous total runoff was obtained for interflow contribution based on comparing instantaneous discharge data recorded for stations P1 and P3. These obviously supported the aforesaid statements which have also been approved by Noguchi et al. (1997) who reported the ratio of pipe flow to total flow from a negligible amount up to 99.5% varied with rainfall intensity and antecedent moisture conditions for Hitachi Ohta forest experimental watershed in Japan. In other words, the lower SSCs during the recession flow are partially the result of dilution by increased contributions to the flow by throughfall and throughflow and less availability of sediment. The advanced sediment graphs compare to hydrograph also seems to be another evident for the special governing conditions in the Mie watershed. To clarify the given explanations, hyetograph–hydrograph-sediment graphs

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Fig. 4. Distinguished exits of flow pipes (sides) and geologic bedding (middle) of schist formation in bank of downstream part of Mie main river, Japan.

relationship for storm event June 21, 2004 and concurrently recorded at the main outlet (P1) and important subwatersheds (P2, P3 and P5) have been presented in Fig. 5 for instance. It is seen in Fig. 5 that the sediment graph centeriod in P1 precedes the centeroid of the hyetograph by 13 min which can be implied that the transportation of limited available sediment immediately starts when excess runoff (rainfall excess) generates after satisfying interception storage and reaching hydrophobic surface soil. The sediment graph centeriod and even peak are then followed by those of hydrograph with almost half an hour delay. The sediment graphs are in advance to hydrographs in figures presented for P2, P3 and P5. A highly variable runoff generation is seen in P5 hydrograph which is strictly influenced by excess rainfall variations and verifying very prompt response of the small subwatersheds. Therefore the real peak coordinates of sediment graph obtained for P5 may not be exactly similar to those shown in the figure, since the sampling interval is comparatively very large and may not reflect the prompt response and associated variations of the very small study subwatershed. The reduction in SSCs at P1 compare to those recorded at P3 as the result of dilution by increased contribution to the flow by throughflow is also validated through comparing the second y-axes values of concerned graphs. A minute examination of the results (Tables 2, 3 and Fig. 3) also shows that the process of sediment production in different subwatersheds (shown and pictured in Fig. 1) is not alike. Hence, it is initially can be judged that the smaller size of the afforested subwatershed (i.e. P4 and P5) the more complexity and uncertainties in sediment concentration process and less reliable sediment rating equations. In other words, the Q–SSC relationship is comparatively more stable for the entire watershed and larger subwatersheds of P2 and P3, and natural forest subwatershed P8. It is clearly attributed to the very prompt hydrologic response of the small subwatersheds to any slight natural and anthropogenic changes in input variables and delivering of most of the suspended sediment load during a very small portion of the time (Thomas, 1991). While in larger areas sediment may be available not only from hillslopes but there may be a small and continuous contribution of river bank erosion (not from stony river bed) in supplying sediment loads which is directly controlled by generated runoff. For example, patch erosion may be supposed as case reason controlling the SSC particularly in very small-sized subwatersheds P4 and P5

where frequent traffic was taking place and very intensive experiments were also being conducted during data collection period. It is verified by a negative significant linear relationship (r = 79.16%) between coefficient “a” values for all data (Table 3) and respective areas of subwatersheds, since high a-coefficients indicate easy transportation of intensively weathered materials as reported by Phillips et al. (1999) and Asselman (2000). In contrary to Phillips et al. (1999) in LOIS study area, the nonsignificant relationship between “b” values for all data (Table 3) and subwatersheds areas as well as convergency of all, fall and rise curves at higher discharges proved the limited sediment sources when discharge increases, though not all the correlations are statistically significant (Fig. 3). For better understanding and interpretation of the erosional condition in the study subwatersheds and owing to closeness of b-values to unit for all data (Table 3), the concerned b-values was fixed as one and corresponding a-values were then determined. The calculated a-values for the developed linear and still statistically acceptable equations varied from 3.688 to 9.228 belong to P1 and P5, respectively. It simply showed that intensity of soil erosion and easiness to transport of eroded materials is almost 3 times more in P5 compare to that of the entire watershed of P1. The other values of 5.315, 4.642, 7.762 and 7.533 were also obtained for P2, P3, P4 and P8, respectively. It verified the earlier explanations regarding the difference in soil erosion conditions in the study subwatersheds. The two stations of P1 and P3 locating on the main river and respectively representing the entire and a large part of the watershed comparatively have almost the lowest mean and median values and lower sediment availability and power index coefficient of “a” as explained earlier. Despite of low stand density of 1500 stems ha− 1 of Japanese cypress (C. obtuse, Hinoki) compare to other subwatersheds, the average and median values as well as regression coefficients of the developed sediment rating curve for P2 is not so high by virtue of full existence of dense understory vegetation cover of fern and evergreen species (Gomi et al., submitted for publication). The same complete understory conditions with shorter deciduous species exit in P8, because of which the rate of soil erosion and consequent sediment yield is also very low. Whereas in subwatershed P4 and P5 with respective stand density of 3500 and 4500 stems ha− 1, the soil erosion and sediment transportation induced by raindrop splash erosion with the higher rate in P5, since almost 85 and 10%

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Fig. 5. Hyetograph–hydrograph-sediment graph relationship and associated components for storm event June 21, 2004 at main outlet and important subwatersheds P2, P3 and P5 of Mie watershed, Japan.

(Gomi et al., submitted for publication) of the surface soil is covered by understory vegetation cover. The fewer number and larger size of throughfall raindrops than open rainfall drops and consequent total impact energy of throughfall over twice that of open rainfall (Nanko et al., 2004) can be postulated as main reason on higher soil erosion rates in two aforesaid subwatersheds. The protective effect of floor cover against soil erosion on steep slopes forested with C. obtuse and some other species has also been approved by Miura et al. (2002 and 2003) in Japan and Sorriso-Valvo et al. (1995) for two slope aspects afforested by Pinus and Eucalyptus in small logged Mediterranean Calabrian watershed. The impact of fern understory coverage on reduction

of surface runoff to the level of 50% of that in bare plots has also been reported by Miyata et al. (2007) for the same study watershed. The parallel studies in plots with and without fern (Miyata et al., 2007) also verify that the annual erosion rate is almost 5 times more in bare plots than in fern plots. Since the average slope (Table 1) of area covered by each station is also sufficiently high (Mutreja, 1990) to play almost the same affective role on soil erosion, and even varying within a small range of 71 to 92% in all subwatersheds, its effects has not been incorporated minutely. From the results of the present study, it is inferred that the sediment production in the Mie small mountainous reforested

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watershed is significantly complex and variable as same as sediment yield response of watersheds located in arid zones. The variable response of the former watershed is because of spatial and temporal variation of contribution of different hydrological cycle components on soil erosion process while in the latter, it is mainly owing to non-consistency of rainfall characteristics and very sparse vegetation cover. It therefore verifies that the concurrent discharge values cannot be supposed as an excellent indicator for SSC prediction in the study area. 4. Conclusion remarks During the present study, different types of sediment rating curves were developed for the Mie small mountainous reforested watershed in Japan and their efficacies were evaluated using various and rarely applied statistical criteria and the corresponding results were then interpreted. Although a sediment rating curve is considered a ‘black box’ type of model, but some physical interpretation is often ascribed to those developed for the study subwatersheds. It also could be referred from the results of the study that the sediment rating curves without decomposition of data based on appropriate criteria, commonly used to estimate suspended sediment loads, as the logarithm of sediment response as a linear function of the logarithm of the simultaneous water discharge gave highly biased estimates for the small study watershed. Contrary to what is oft-reported, the best fitted rating curves to the entire data collected for each individual station excessively overestimated the suspended sediment concentration in the study area. The temporal and magnitude stratification of flow discharge and sediment data as well as employing bias correction factor did not improve the relationship, while better estimates were obtained when power regression was applied to the fourth root transformed data separated based on their locations on rising and falling limbs of hydrographs. In this region, where any type of sediment rating curves does not adequately and perfectly characterize suspended sediment transport and the close interplay of spatial and temporal factors is one of the major problems faced in trying to explain variations in suspended sediment concentrations, the study of individual hydrological events (e.g. sediment graphs), dynamic modeling and multivariate analysis of the runoff amount and rate, hydraulic properties of the flow, sediment supply from the watershed or its contingency on soil erosion processes, the temporal rainfall intensity distribution, the travel rates and distances of floodwaters in the main channel, and pedological factors is important and should greatly improve prediction of sediment transport in the study watershed, though it masks the simplicity of sediment rating curve application. An understanding of these adjustments demands a deep investigation of the relationships between aforesaid factors at appropriate spatial and temporal scales. Although all factors are important, supply limitations must, however, also exert an important control on suspended sediment transport rates in the study area. In the other words, scatter about the regression line caused by variations in sediment supply due to, for instance, hydrophobicity, antecedent conditions in the watershed, and differ-

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ences in sediment availability at the beginning or the ending of a flood which was not accounted for by the rating curve. The advantage of the sediment rating curve technique is that once a rating curve has been developed, it can be applied to past streamflow data to reconstruct long-term sediment transport records or to fill gaps in existing sediment transport records. However, one of the major shortcomings of the application of rating curves in the extrapolation of sediment transport time series is that the requisite assumption of stationarity is often questionable. To judge the validity of the stationarity assumption, more knowledge about the factors that determine the shape of the sediment-rating curve is needed. The same merit and demerit can also be recognized for the developed models for the Mie study watershed which require deeper and longer study for better understanding of governing fluvial circumstances. Although the evidence presented above is not definitive, it can be suggested that the major factors controlling the sediment rating curves in the Mie watershed are hydrological, vegetation and soil conditions prevailing during water and eroded sediment supply during hydrological events. Study on the effects of changing in vegetation cover density from very dense to clear cutting and under different silvicultural techniques such as thinning, clearing and mixed plantation in the study area will be greatly important to forest manager and authorities to draw final conclusions for appropriate forest management. The analysis of more lengthy collected data covering different climatological conditions and more consideration on regular, persistent and concurrent recording of flow discharge and sediment in different substations as well as identification of sediment source are recommended for forthcoming studies. Substitution or equipping stations with other supplementary direct or indirect sampling devises in order to collect more frequent suspended sediment samples especially in the small subwatersheds may also provide some more details about the governing hydrological processes in the study watershed which ultimately lead to better management of forest watershed in Japan. Acknowledgments This study was supported by a Post Doctoral Fellowship from the Matsumae International Foundation (MIF) provided to the first author. Additional support and funding was provided by the Erosion Control Lab (SABO) at Kyoto University and Japan Science and Technology Agency, CREST project. The authors also wish especially thank Prof. J. Laronne for his constructive suggestions and encouragements. The leave allowance given to the first author by the Tarbiat Modares University, Iran, is also thanked. References Abrahams, A.D., Parsons, A.J., Luk, S.H., 1988. Hydrologic and sediment responses to simulated rainfall on desert hillslopes in southern Arizona. Catena 15, 103–117. Alexandrov, Y., Laronne, J.B., Reid, I., 2003. Suspended sediment concentration and its variation with water discharge in a dryland ephemeral channel, northern Negev, Israel. Journal of Arid Environment 53, 73–84. Alvarez, L.G., Jones, S.E., 2002. Factors influencing suspended sediment flux in the upper Gulf of California. Estuarine, Coastal and Shelf Science 54, 747–759.

210

S.H.R. Sadeghi et al. / Geoderma 144 (2008) 198–211

Asselman, N.E.M., 2000. Fitting and interpretation of sediment rating curves. Journal of Hydrology 234, 228–248. Batalla, R.J., Sala, M., 1994. Temporal variability of suspended sediment transport in a Mediterranean sandy gravel-bed river. IAHS Publication 224, 299–305. Bozdogan, H., 2000. Akaike's information criterion and recent developments in information complexity. Journal of Mathematical Psychology 44, 62–91. Church, M., Slaymaker, O., 1989. Regional clastic sediment yield in British Columbia. Canadian Journal of Earth Sciences 26, 31–45. Cohn, T.A., Caulder, D.L., Gilroy, E.J., Zynjuk, L.D., Summers, R.M., 1992. The validity of a simple statistical model for estimating fluvial constituent loads: an empirical study involving nutrient loads entering Chesapeake Bay. Water Resources Research 28, 2353–2363. Cordova, J.R., Gonzalez, M., 1997. Sediment yield estimation in small watersheds based on streamflow and suspended sediment discharge measurements. Soil Technology 11, 57–65. Croke, J.C., Mockler, S.P., Fogarty, P., Takken, I., 2005. Sediment concentration changes in runoff pathways from a forest road network and the resultant spatial pattern of watershed connectivity. Geomorphology 68, 257–268. DeVries, A., Klavers, H.C., 1994. Riverine fluxes of pollutants: monitoring strategy first calculation methods second. European Journal of Water Pollution Control 4, 12–17. Duan, N., 1983. Smearing estimate: a nonparametric retransformation method. Journal of the American Statistical Society 78, 605–610. Edwards, T.K., Glysson, G.D., 1999. Field methods for measurement of fluvial sediment. USGS Open-file Report 1–97. http://water.usgs.gov/ osw/techniques/Edwards-TWRI.pdf1999. Fenn, C.R., Gurnell, A.M., Beecroft, I.R., 1985. An evaluation of the use of suspended sediment rating curves for the prediction of suspended sediment concentration in a proglacial stream. Geografiska Annaler 71–82 67A. Ferguson, R.I., 1986. River loads underestimated by rating curves. Water Resources Research 21, 74–76. Ferguson, R.I., 1987. Accuracy and precision of methods for estimating river loads. Earth Surface Processes and Landforms 12, 95–104. Gergov, G., 1996. Suspended sediment load of Bulgarian rivers. Geojournal 40 (4), 387–396. Gomi, T., Sidle, R.C., Ueno, M., Miyata, S., Kosugi, K., submitted for publication. Characteristics of overland flow generation on steep forested hillslopes of central Japan. Journal of Hydrology. Hann, C.T., Barfield, B.J., Hayes, J.C., 1996. Design Hydrology and Sedimentology for Small Watersheds. Academic Press, San Diego. 487p. Hansen, D., Bray, D.I., 1993. Single-station estimates of suspended sediment loads using sediment rating curves. Canadian Journal of Civil Engineering 20, 133–143. Hasnain, S.I., 1996. Factors controlling suspended sediment transport in Himalayan glacier meltwaters. Journal of Hydrology 181, 49–62. Hickin, E.J., 1989. Contemporary squamish river sediment flux to Howe Sound British Columbia. Canadian Journal of Earth Sciences 26, 2172–2176. Holtschlag, D.J., 2001. Optimal estimation of suspended-sediment concentrations in streams. Hydrological Processes 15, 1133–1156. Horowitz, A.J., 1995. The use of suspended sediment and associated trace elements in water quality studies. IAHS Special 4. Horowitz, A.J., 2003. An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations. Hydrological Processes 17, 3387–3409. Horowitz, A.J., Elrick, K.A., Smith, J., 2001. Estimating suspended sediment and trace element fluxes in large river basins: methodological considerations as applied to the NASQAN programme. Hydrological Processes 15, 1107–1132. Hudson, P.F., 2003. Event sequence and sediment exhaustion in the lower Panuco Basin México. Catena 52, 57–76. Jansson, M., 1985. A comparison of detransformed logarithmic regressions and power function regressions. Geografiska Annaler 61–70 67A. Kao, S.J., Lee, T.Y., Milliman, J.D., 2005. Calculating highly fluctuated suspended sediment fluxes from mountainous rivers in Taiwan. Terrestrial, Oceanic and Atmospheric Sciences Journal (TAO) 16 (3), 653–675. Knott, J.M., Glysson, G.D., Malo, B.A., Schroeder, L.J., 1993. Quality assurance plan for the collection and processing of sediment data by the U.S.

Geological Survey Water Resources Division: USGS Open-File Report 92499. available on-line at: http://pubs.er.usgs.gov/pubs/ofr/ofr92499. Lai, F.S., Detphachanh, S., 2006. Sediment production during an unusually dry year in the steep forested Sungai Pangsun watershed Selangor Peninsular Malaysia. Forest Ecology and Management 224, 157–165. Lane, S.N., Richards, K.S., Chandler, J.H., 1996. Discharge and sediment supply controls on erosion and deposition in a dynamic alluvial channel. Geomorphology 15, 1–15. Lane, L.J., Hernandez, M., Nichols, M., 1997. Processes controlling sediment yield from watersheds as functions of spatial scale. Environmental Modelling & Software 12 (4), 355–369. Lenzi, M.A., Marchi, L., 2000. Suspended sediment load during floods in a small stream of the Dolomites_northeastern Italy. Catena 39, 267–282. Mataix–Solera, J., Doerr, S.H., 2004. Hydrophobicity and aggregate stability in calcareous topsoils from fire-affected pine forests in southeastern Spain. Geoderma 118, 77–88. Miura, M., Hirai, K., Yamada, T., 2002. Transport rates of surface materials on steep forested slope induced by raindrop splash erosion. Journal of Forest Research 7, 201–211. Miura, M., Yoshinaga, S., Yamada, T., 2003. Protective effect of floor cover against soil erosion on steep slopes forested with Chamaecyparis obtusa (hinoki) and other species. Journal of Forest Research 8, 27–35. Miyata, S., Kosugi, K., Gomi, T., Onda, Y., Mizuyama, T., 2007. Surface runoff as affected by soil water repellency in a Japanese cypress forest. Hydrological Processes 21 (17), 2365–2376. Mossa, J., 1996. Sediment dynamics in the lowermost Mississippi River. Engineering Geology 45, 457–479. Mutreja, K.N., 1990. Applied Hydrology. TATA McGraw-Hill Publishing Company Limited. 959p. Nanko, K., Hotta, N., Suzuki, M., 2004. Assessing raindrop impact energy at the forest floor in a mature Japanese cypress plantation using continuous raindrop-sizing instruments. Journal of Forest Research 9, 157–164. Noguchi, S., Tsuboyama, Y., Sidle, R.C., Hosoda, I., 1997. Spatially distributed morphological characteristics of macropores in forest soils of Hitachi Ohta experimental watershed Japan. Journal of Forest Research 2, 207–215. Pathak, P., Wani, S.P., Singh, P., Sudi, R., 2004. Sediment flow behaviour from small agricultural watersheds. Agricultural Water Management 67, 105–117. Pattyn, F., Van Huele, W., 1998. Power law or power flaw? Earth Surface Processes and Landforms 23, 761–767. Phillips, J.M., Webb, B.W., Walling, D.E., Leeks, G.J.L., 1999. Estimating the suspended sediment loads of rivers in the LOIS study area using infrequent samples. Hydrological Processes 13, 1335–1350. Rovira, A., Batalla, R.J., 2006. Temporal distribution of suspended sediment transport in a Mediterranean basin: the Lower Tordera (NE SPAIN). Geomorphology 79 (1–2), 58–71. Sadeghi, S.H.R., Tofighi, B., Mahdavi, M., 2006. Sediment estimation modeling in Zarrinderakht Watershed. Iranian Journal of Natural Resources 58 (4), 1–9 (In Persian Abstract in English). Sakai, K., Osawa, K., Yoshinaga, A., 2005. Development of suspended sediment concentration (SSC) analysis model and its application with multiobjective optimization. Paddy Water Environment 3, 201–209. Schmidt, K.H., Morche, D., 2006. Sediment output and effective discharge in two small high mountain catchments in the Bavarian Alps Germany. Geomorphology 80 (1–2), 131–145. Seeger, M., Errea, M.P., Beguerı, S., Arnaez, J., Martı, C., Garcıa-Ruiz, J.M., 2004. Watershed soil moisture and rainfall characteristics as determinant factors for discharge/suspended sediment hysteretic loops in a small headwater watershed in the Spanish Pyrenees. Journal of Hydrology 288, 299–311. Sichingabula, H.M., 1998. Factors controlling variations in suspended sediment concentration for single-valued sediment rating curves Fraser River British Columbia Canada. Hydrological Processes 12, 1869–1894. Smith, R.J., 2005. Logarithmic transformation bias in allometry. American Journal of Physical Anthropology 90 (2), 215–228. Sorriso-Valvo, M., Bryan, R.B., Fair, A., Iovino, F., Antronico, L., 1995. Impact of afforestation on hydrological response and sediment production in a small Calabrian watershed. Catena 25, 89–104.

S.H.R. Sadeghi et al. / Geoderma 144 (2008) 198–211 Terajima, T., Sakamoto, T., Nakai, Y., Kitamura, K., 1996. Subsurface discharge and suspended sediment yield interactions in a valley head of a small forested watershed. Journal of Forest Research 1, 131–137. Thomas, R.B., 1988. Monitoring baseline suspended sediment in forested basins: the effects of sampling on suspended sediment rating curves. Hydrological Sciences Journal 33, 499–514. Thomas, R.B., 1991. Systematic sampling for suspended sediment. In: Fan, S.S., Kuo, Y.H. (Eds.), Proceedings of the 5th Federal Interagency Sedimentation Conference. Las Vegas Neveda March 18–21 1991 2-17-2-24. Walling, D.E., 1977a. Assessing the accuracy of suspended sediment rating curves for a small basin. Water Resources Research 13, 531–538. Walling, D.E., 1977b. Limitations of the rating curve technique for estimating suspended sediment loads with particular reference to British Rivers. IAHS Publication.

211

Walling, D.E., 1988. Erosion and sediment yield research—some recent perspectives. Journal of Hydrology 100, 113–141. Walling, D.E., Webb, B.W., 1982. Sediment availability and the prediction of storm-period sediment yields. Recent developments in the explanation and prediction of erosion and sediment yield. IAHS Publication 137, 327–337. Walling, D.E., Webb, B.W., 1983. Patterns of sediment yield. In: Gregory, K.J. (Ed.), Background to Paleohydrology. John Wiley and Sons, NY, pp. 69–100. Wang, Y., Liu, Q., 2006. Comparison of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in selection of stock–recruitment relationships. Fisheries Research 77, 220–225. Williams, G.P., 1989. Sediment concentration versus water discharge. Journal of Hydrology 111, 89–106.