Effective discharge recurrence intervals of Illinois streams

Geomorphology 64 (2005) 167 – 184 www.elsevier.com/locate/geomorph

Effective discharge recurrence intervals of Illinois streams David W. Crowder*, H. Vernon Knapp Illinois State Water Survey, Champaign, IL 61820, USA Received 8 January 2004; received in revised form 22 June 2004; accepted 26 June 2004 Available online 12 August 2004

Abstract Effective discharge values are routinely used to characterize the geomorphic impact/work that different flows have on streams and watersheds. Effective discharge values are also routinely employed in stream restoration design. Reliable flow frequency and sediment-rating curves are needed to compute effective discharge values, but are not available for many streams. In lieu of regional effective discharge data, researchers/engineers often assume that a stream’s effective discharge is the same as the 1.5-year flow event for that stream. However, research shows that this is not always true. Hence, demand exists for developing regional effective discharge values that can be applied at ungaged stream locations. A regional study of effective discharge values within Illinois is performed. Eighty-eight monitoring sites were identified as locations where effective discharge values could potentially be computed. Twenty of these sites were determined to have sufficient data to compute effective discharge values. For these stations, effective discharge values were computed using two different sediment–discharge relationships (a power curve and an alternative bmean approachQ). The mean approach estimates effective discharge values that are, on average, 1.9 times larger than those estimated using the power curve. The sediment load carried by flows less than or equal to the effective discharge value are, on average, 2.7 times larger when the mean approach is used. Both the power curve and mean approaches typically yield effective discharge values greater than the stream’s mean flow and less than its 1.1-year (annual maximum series) flow event. Some of the computed effective discharge values are less than the stream’s mean flow. Computing effective discharge values remains a subjective process with large uncertainties involved. Class interval size, sediment-rating curve fitting techniques, and available sediment data influence effective discharge results. A paucity of monitoring data for Illinois streams draining b518 km2 (where many geomorphic analyses and stream restoration projects take place) hinders the regional applicability of effective discharge results. The uncertainties associated with effective discharge values and the inability to validate effective discharge values as a stream’s dominant discharge makes channel design using effective and dominant discharge theory problematic. Suggestions for improving regional effective discharge studies and effective discharge estimates are provided. D 2004 Elsevier B.V. All rights reserved. Keywords: Effective discharge; Dominant discharge; Suspended sediment; Stream restoration; Morphology; Illinois

* Corresponding author. Fax: +1 217 333 2304. E-mail address: [email protected] (D.W. Crowder). 0169-555X/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2004.06.006

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1. Introduction Wolman and Miller (1960) wrote one of the preeminent discussions associated with effective discharge and dominant discharge theory. They argued that over long periods of time smaller more frequent flows, and not infrequent catastrophic flows (e.g., 100- and 500-year floods), carry the most sediment within streams. Specifically, Wolman and Miller (1960) concluded that for many rivers the discharge that carries the largest proportion of sediment over time (commonly referred to as the effective discharge) has a recurrence interval between 1 and 2 years. Through observations of natural channels, Wolman and Miller (1960) also argued that bankfull discharges often have recurrence intervals of 1–2 years and play an important role in determining a channel’s morphology. The argument that bankfull discharge ( Q b), effective discharge ( Q e), and 1.5-year flow events ( Q 1.5) have similar magnitudes and play an important (or ddominantT) role in forming and maintaining a stream’s morphology helped establish how dominant discharge theory is being debated and applied. In dominant discharge theory, it is often assumed that there is a single discharge which, if constantly maintained within a stream, will produce the same average channel dimensions/ morphology as those produced by a stable stream’s entire hydrologic regime (Inglis, 1949; Federal Interagency Stream Restoration Working Group (FISRWG), 1998). If such dominant discharges exist, and can be computed, one can theoretically design channels that will be in ddynamic equilibriumT (see Thorne et al., 1996; FISRWG, 1998), without addressing the influence a stream’s varying hydrologic conditions have on a channel. Effective discharge ( Q e), bankfull discharge ( Q b), and the 1.5-year flow event ( Q 1.5) have all been proposed as means of estimating dominant discharge (e.g., Rosgen and Silvey, 1996; Watson et al., 1999). Following Wolman and Miller’s (1960) paper, substantial research has been devoted to determining the differences (in magnitude and geomorphic function) between Q e and Q b , if recurrence intervals for Q e and Q b values are constant or vary between streams, and how Q e and Q b help form and maintain channels. Benson and Thomas

(1966) and Pickup and Warner (1976) found that some stream’s bankfull and effective discharge values were significantly different. Andrews (1980) observed that bankfull and effective discharge values were nearly identical at 15 gaging stations in the Yampa River basin of Wyoming and Colorado. Williams (1978) analyzed the recurrence intervals of bankfull discharges at several US streams and concluded that recurrence intervals for bankfull discharges varied widely. Nash (1994) found that recurrence intervals of effective discharge values for streams throughout the USA also varied widely. Because of these varying results and the different methods used by researchers to estimate dominant discharge values, doubt exists whether a single recurrence interval (such as 1.5 years) could be used to adequately represent an arbitrary stream’s dominant discharge. Researchers argue that a single recurrence interval is not representative of every stream’s dominant discharge because morphology, watershed area, and hydrologic regime influence Q e and Q b values (e.g., Ashmore and Day, 1988; Castro and Jackson, 2001). Phillips (2002) found that a particular forested headwater stream essentially had two dominant discharges: a relatively frequent discharge responsible for maintaining the channel, and a second infrequent discharge responsible for shaping its banks. Wolman and Miller (1960) alluded to a similar case in which a stream’s effective discharge appeared to be a winter flow much less than the stream’s bankfull discharge, but that a larger, less frequent bankfull discharge was probably responsible for forming such features as the stream’s maximum bank height. Moreover, Magilligan (1992) argues that, for a given flow event (e.g., Q 1.5), geologic and morphologic controls cause shear stress and flood power to substantially vary along a river’s profile. Thus, the geomorphic function of a specific flow event (or dominant discharge estimate) may vary downstream. Such work demonstrates the continued need for quantifying and describing the geomorphic functions of effective, and bankfull, discharges within watersheds of various sizes, in different geographic regions, and in different hydrologic regimes. Illinois streams typically have low gradients and transport silts, clays, and sand material. In the past,

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many Illinois streams have been channelized in an attempt to reduce flooding, drain land for agricultural production and protect croplands from erosion. These agricultural and other anthropogenic activities are known to cause streams to undergo excessive aggradation and/or degradation (Waters, 1995). With increasing demand to reduce erosion from valuable croplands and to restore important stream habitat, fluvial geomorphologists and engineers are being asked to study and monitor morphologic processes in order to develop and implement better stream/ watershed restoration techniques. As described by Wolman and Miller (1960), magnitude–frequency computations (e.g., effective discharge computations) provide a means of characterizing the relative amount of geomorphic dworkT that is done within a watershed by a specific discharge (whether or not a stream is in dynamic equilibrium). Thus, effective discharge computations provide a basis for comparing the geomorphic work being done by specific flows in different watersheds. Moreover, effective discharge values representative of streams in dynamic equilibrium also provide one means of estimating dominant discharge values for stream restoration projects (Watson et al., 1999). Unfortunately, the discharge and sediment data needed to compute effective discharge values is not available at many locations. Hence, the development of regional effective discharge criteria that can be employed to estimate effective discharge values at ungaged locations will benefit stream morphology studies. A first step in developing regional effective discharge criteria is to compute effective discharge values for as many streams as possible. Suspended sediment records collected at or near 88 stream gages are analyzed to identify monitoring locations within Illinois that have sufficient sediment and discharge data to compute effective discharge values. For each location determined to have sufficient data, effective discharge values are computed using two different sediment-rating relationships. Recurrence intervals for both effective discharge estimates are made and compared. An extensive discussion describes these results and their sensitivity to various parameters. Issues that need further research before regional effective discharge criteria can be developed are also summarized.

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2. Available data Eighty-eight stream locations within Illinois were initially identified as having suspended sediment and discharge data that could potentially be used to estimate effective discharge values. Only some of these sites had active long-term suspended sediment and discharge monitoring programs. Most locations had an active long-term discharge monitoring program and a 1- to 3-year suspended sediment record collected sometime in the past. Fifty-eight stations had sediment records with b3 consecutive years of data, while 15 sites had sediment records covering 10 or more consecutive years. The remaining 15 stations had between 4 and 16 years of sediment data. The periods of record at 13 of these sites were discontinuous. The number of sediment samples collected at each of the 88 sites varied considerably. As described in Section 3.4, 20 sites were determined to have sufficient sediment and discharge data to compute effective discharge values. The names of these 20 gages are displayed in Table 1. The periods of time over which suspended sediment and discharge data were collected at these sites is also provided. The suspended sediment records shown in Table 1 were collected either by the United States Geological Survey (USGS) or the Illinois State Water Survey (ISWS). Sites at which the USGS collected suspended sediment data are tabulated first followed by sites at which the ISWS collected suspended sediment samples. As the ISWS and USGS both collected suspended sediment data at three locations, these sites are listed twice in Table 1. The first listing (under the USGS sites) shows the time period over which the USGS collected sediment data. The second listing (under the ISWS sites) shows the time period over which the ISWS sampled sediment. Showing these stations twice and not combining the sediment records is necessary because of differences in the USGS and ISWS suspended sediment records as described below. All of the discharge records in Table 1 were developed by the USGS and represent mean daily discharge records. 2.1. ISWS instantaneous suspended sediment records The ISWS suspended sediment records were collected using sampling protocols conforming with

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Table 1 Summary of the suspended sediment and discharge data used to compute effective discharges USGS station Number

Suspended sediment data record Name

Sampling period(s)

Water years

Stations with USGS mean daily suspended sediment data 5466500 Edwards River near 1979–1981 New Boston 5525000 Iroquois River at Iroquois 1979–1980, 1993–1996 5526000 Iroquois River near 1979–1981, 1993–1996 Chebanse 5520500 Kankakee River 1979–1981, 1993–1996 at Momence 5527500 Kankakee 1979–1982, 1993–1996 River near Wilmington 5591200 Kaskaskia River 1979–1997 at Cooks Mills 5594100 Kaskaskia River 1980–1997 near Venedy Station 5585000 La Moine River 1981, 1995–1997 at Ripley 3378900 Little Wabash River 1977–1981 at Louisville 5568000 Mackinaw River near 1995–1997 Green Valley 5583000 Sangamon River 1981, 1983–1986, near Oakford 1995–1997

5570000

Spoon River at Seville

1981, 1995–1997

Stations with ISWS instantaneous suspended sediment data 3612000 Cache River at Forman 1981–2000 5520500 Kankakee River at Momence 1982–1985, 1993–2000 5527500 Kankakee River near 1983–2000 Wilmington 5592500 Kaskaskia River at Vandalia 1981–2000 5584500 La Moine River at Colmar 1981–1988, 5585000 La Moine River at Ripley 1983–1990, 3381500 Little Wabash River at Carmi 1981–1985, 5435500 Pecatonica River at Freeport 1981–2000 5437500 Rock River at Rockton 1981–2000

1988–1990,

1993–2000 1993–2000 1993–2000

Mean daily discharge Data points

Monitoring period(s)

Water years

3

1004

1935–2000

66

6 7

1826 2190

1945–2000 1923–1998, 2000

56 77

7

2191

88

8

2556

19

6848

1905–1906, 1915–2000 1915–1933, 1935–2000 1970–2000

31

18

6362

1970–2000

31

4

1461

1921–2000

80

5

1666

1965–1983

19

3

1096

49

8

2322

4

1461

1921–1956, 1988–2000 1910–1911, 1915–1919, 1922, 1929–1933, 1940–2000 1915–2000

20 15

1315 615

18

856

20 16 16 13 20 20

975 782 631 560 726 1052

5572000

Sangamon River at Monticello

1981–2000

20

646

5594800

Silver Creek near Freeburg

1981–2000

20

869

the ISWS’s Benchmark Sediment Monitoring Program (Allgire and Demissie, 1995). Specifically, single vertical depth-integrated samples are collected once every week. At the same time a

1923–2000 1905–1906, 1915–2000 1915–1933, 1935–2000 1908–2000 1945–2000 1921–2000 1940–2000 1914–2000 1903–1909, 1914–1919, 1940–2000 1908–1912, 1914–2000 1971–2000

85

74

86

78 88 85 93 56 80 61 87 74

92 30

suspended sediment sample is collected, stream stage at a nearby USGS continuous stream gage is also recorded. The discharge occurring at the time of sampling is then estimated from the USGS

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stage–discharge rating curve. The resulting binstantaneousQ suspended sediment concentration and discharge values are used here to develop the sediment load versus discharge relationships needed to compute effective discharges. No suspended sediment concentrations were estimated through interpolation or other means to predict sediment loads on days that no actual samples were taken. Consequently, a typical 1-year ISWS sediment record provides 52 data points from which to develop a sediment–discharge relationship. Because these records are collected on larger streams where instantaneous discharge and suspended sediment concentration are not expected to vary significantly from daily mean discharge and concentration values, these instantaneous concentrations and discharges are assumed to adequately represent mean daily values. 2.2. USGS daily mean suspended sediment records To accurately estimate daily mean suspended sediment concentrations/loads, collecting several instantaneous sediment samples in a day and averaging them in a way that yields an appropriate mean daily value is ideal. However, sampling several times each day is generally not economically feasible. Hence, to create continuous daily suspended sediment concentration records, it is common to estimate/interpolate sediment concentration values for the time periods in which samples are not available. The USGS estimated/interpolated many of the daily mean suspended sediment concentration/load values described in Table 1. Consequently, a 1-year USGS suspended sediment record will provide 365 points from which to estimate a sediment–discharge relationship, but many of the points are not actual data values and may lack the temporal variability found in sediment concentrations. However, bintegratingQ such bcontinuous concentration traceQ data is often the preferred method of computing stream loads (Robertson and Roerish, 1999). The bmean approachQ (discussed in Section 3.3) essentially incorporates the continuous concentration trace method of computing sediment loads into Q e computations when discharge and sediment concentration records at a gaging station have the same period of record.

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3. Methodology 3.1. Effective discharge computation Effective discharge values are computed for each location described in Table 1 using a five-step process similar to that adopted by Benson and Thomas (1966), among others. First, the range of discharges is subdivided into a number of arithmetic and equal-size intervals, referred to as bintervals of stream dischargeQ (Benson and Thomas, 1966) or bclass intervalsQ (Biedenharn et al., 1999). Using the suspended sediment record, an estimate is made of the average sediment load that the discharges within each class interval carry. Next, the number of mean daily discharge values occurring within each class interval is obtained from the station’s mean daily discharge record. The total suspended sediment load transported by each class interval is then estimated by multiplying the class interval’s average suspended sediment load by the number of flow days occurring within that class interval. Finally, the effective discharge value is identified as the class interval that transports the largest amount of sediment. For simplicity, the effective discharge value is considered to be the midpoint of that interval. A plot of the suspended sediment load (or percentage of the total suspended load) carried by each class interval is often referred to as a bload histogramQ or simply the bhistogramQ and depicts the relative amounts of suspended sediment each class interval transports over time. The discharge corresponding to the histogram’s peak is the effective discharge. The sum of the loads transported by every class interval in a load histogram equals the total suspended sediment load transported by the stream over the period of record. By dividing the total suspended load transported within the stream by the total number of days in this discharge record, multiplying by 365 days and dividing by the watershed’s area, one obtains an estimate of the watershed’s annual suspended sediment yield (Y ss) (in T/year/km2). Here, bT Q refers to metric tons or tonnes. While effective discharge is simple to compute using the previously described approach, the procedure contains subjective elements. Determining how many class intervals to use in a computation, how to best estimate the average sediment load being

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transported within a class interval, and if a stream location has sufficient sediment and discharge data to compute Q e are decisions made based on a researcher’s best judgment. The following paragraphs briefly describe how these issues are addressed in this study. 3.2. Number of class intervals A primary criticism of the effective discharge procedure is that the number of class intervals used in a computation can significantly influence the effective discharge value (e.g., Sichingabula, 1999). Moreover, using a constant number of class intervals does not always yield a load histogram with a clear peak value (Biedenharn et al., 1999). Hence, researchers must determine the appropriate number/ size of class intervals to use based on their best judgment. Here, an iterative procedure similar to that proposed by Biedenharn et al. (1999) is adopted to determine the size/number of class intervals used in a computation. Initially, 25 class intervals are assigned. The number of flow events occurring within each class interval is then checked. If each class interval does not contain at least one flow event, then the number of class intervals is slowly reduced until each class interval contains at least one flow event. The location of the load histogram’s peak value is then identified. If the peak value occurs somewhere other than the first class interval, then the effective discharge is estimated based on the currently assigned number/size of discharge intervals. If the peak value occurs in the first class interval, the number of class intervals is slowly increased until the peak value occurs somewhere other than the first class interval. The minimum number of class intervals that creates a peak other than in the first class interval is accepted as the number of class intervals to use. A flow chart demonstrating this process is depicted (Fig. 1). 3.3. Assigning average sediment loads to class intervals Two methods are used to estimate the sediment load being transported within class intervals. In the first approach, a sediment-rating curve is established by computing the best linear regression fit for a log– log plot of suspended sediment load versus discharge.

Fig. 1. Flow chart for determining the number of class intervals to use in a computation (from Crowder and Knapp, 2002).

The resulting regression equation can be written in the form of a bpower curveQ as follows: Qss ¼ c1 Q c 2

ð1Þ

where Q ss is the suspended sediment load, Q represents discharge, and c 1 and c 2 are coefficients obtained from the regression analysis (see a basic statistics book such as that by Sandy, 1990). The suspended sediment load carried by each class interval is assigned by substituting the mean/midpoint discharge values of each class interval into Eq. (1) and computing Q ss (Fig. 2A,B). Using power curves to help establish suspended sediment-rating curves is a common practice (e.g., Andrews, 1980; Biedenharn et al., 1999). A drawback to this approach is that the regression technique creates a systematic bias that underestimates sediment loads (Ferguson, 1987). Despite this criticism, the approach is commonly adopted. Therefore, it is used here and compared to a second approach. The second approach used to assign average suspended sediment load values to each class

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Fig. 2. Example effective discharge computations: (A) sediment-rating data divided into 25 class intervals; (B) flow frequency curve and sediment load curves (mean and power curve approaches); (C) load histogram generated using the mean approach and 25 class intervals; (D) load histogram generated using the power curve approach and 25 class intervals; (E) load histogram generated using the power curve approach and 53 class intervals; and (F) load histogram generated using the mean approach and 53 class intervals.

interval also relies upon a suspended sediment load versus discharge plot. Specifically, the x-axis (discharge) of the load–discharge plot is subdivided into the same discharge intervals being used to compute flow frequencies (Fig. 2A). The suspended sediment loads of all sample points falling within a class interval are then averaged and assigned to represent the class interval’s average sediment transport rate (Fig. 2B). This approach to assigning sediment loads

to class intervals will be referred to as the bmean approach.Q The primary advantage to using the mean approach is that it avoids systematically under- or overestimating sediment loads. Moreover, if the discharge and sediment records have the same period of record and mean daily load estimates can be associated with every daily mean discharge value, this approach becomes similar (if not identical) to the

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method that Ashmore and Day (1988) used to compute effective discharge values. A limitation of the mean approach is that it requires substantial sediment data. Ideally, one needs enough sediment data to obtain an accurate average sediment load for each class interval. Naturally, as the number of available sediment samples increases, the number of data points falling within a class interval tends to increase and improve the average sediment load estimate for that class interval. Conversely, the smaller the suspended sediment data set becomes, the less accurate average sediment loads become, and the more likely it becomes that sediment data will not have been collected at any of the discharges within one or more class intervals. Here, if no samples were collected at any of the discharges within a class interval, the sediment transport rate for that class interval is assigned to be 0 T/day to reflect the lack of sediment data to describe sediment transport rates for that class interval (even though, in reality, the class interval carries a load). Consequently, using the mean approach with small amounts of sediment data will yield a bnoisyQ load histogram with several class intervals having no sediment load estimates and no clear peak (effective discharge value). Such load histograms indicate that insufficient sediment data is available to compute an effective discharge value using this approach. To avoid the bnoisyQ histograms created with limited data, sediment record sampling over a long period of time and over a wide range of discharges needs to be used. Under these circumstances, we can expect at least a few samples to be collected within each class interval. The resulting load histogram will typically be a comparatively smooth plot with a distinct peak (effective discharge value). 3.4. Data sufficiency Before computing an effective discharge, a preliminary analysis was performed to determine which stations appeared to have enough data to develop adequate flow frequency curves. Andrews (1980) developed flow frequency curves for gaging stations with at least five or more years of discharge data, while Biedenharn et al. (1999)

recommend using stations with between 10 and 20 years of data. Here, stations with 10 or more years of discharge data were considered to have sufficient data to derive a flow frequency curve. For stations that met this criterion, watershed characteristics were assumed to remain constant over the entire period of record so that flow frequency curves are representative of natural variations within the watershed. No analyses were performed to determine if the channels at these stations were in dynamic equilibrium. If a station had 10 or more years of discharge data, an analysis was then performed to determine if that station’s sediment data was sufficient to quantify the sediment transport rates occurring over the stream’s entire range of discharges. A station had sufficient sediment data if sediment samples had been collected over a range of flows in which at least 90% of the stream’s daily mean discharges occur, had sediment samples collected at discharges at least up to the 2.0-year recurrence interval, and the resulting load histogram (using the mean approach), created a fairly smooth curve with a clearly identifiable effective discharge value. Twenty locations met this criterion. 3.5. Effective discharge and associated parameters For stations with sufficient sediment and discharge data, effective discharge values were computed using both the mean and power curve approaches. Corresponding flood recurrence intervals were computed for each effective discharge value at each station using Log Pearson Type III flood frequency distributions and the procedure outlined by the Interagency Advisory Committee on Water Data (1982). The percentage of flows exceeding each effective discharge value (Exc.), the annual sediment yield ( Y ss), and the annual sediment load carried by discharges less than or equal to each effective discharge value ( Y ssVQ e) are also computed. The recurrence interval (R.I.), exceedance, and sediment yield estimates for the mean and power curve approaches are then compared. To differentiate between parameters computed using the mean approach from those that were computed using the power curve approach, Q e, Y ss, and Y ssVQ e values computed using the mean approach are denoted Q em,

D.W. Crowder, H.V. Knapp / Geomorphology 64 (2005) 167–184

Y ssm, and Y ssVQ em, respectively. Q e, Y ss, and Y ssVQ e values computed using the power curve approach are denoted Q ep, Y ssp, and Y ssVQ ep, respectively.

175

the load histogram generated using the mean approach and 53 class intervals is also shown (Fig. 2F).

3.6. Example computation 4. Results The overall procedure used for computing effective discharge values using the ISWS instantaneous data collected for the Cache River at Forman is shown graphically (Fig. 2). The suspended sediment data is plotted, divided into 25 class intervals, and fitted with a power curve (Fig. 2A). The average load values assigned to the 25 class intervals by the mean and power curve approaches are then plotted beside the number of days in the discharge record corresponding to each class interval (Fig. 2B). Multiplying the average load carried by each class interval (mean approach) by the number of days occurring within that class interval generates the load histogram shown in Fig. 2C. The fourth class interval in the resulting load histogram carries the most sediment load and represents the effective discharge (35 m3 s1). Note that in this computation only a relatively small number of suspended sediment samples were collected at flows exceeding 79.5 m3 s1 (Fig. 2A), and average sediment load values for class intervals larger than 180 m3 s1 could not be estimated using the mean approach (Fig. 2B). However, as the load histogram at smaller discharges is relatively smooth and has a distinct peak (Fig. 2C), the site is considered to have sufficient data. Multiplying the average load carried by each class interval (using the power curve approach) by the number of flow events occurring within that class interval generates the load histogram shown in Fig. 2D. However, the first class interval in this histogram carries the most load, and more class intervals must be used in order to compute the effective discharge. Following the flow chart in Fig. 1, one finds that 53 class intervals are needed to generate a load histogram where the first class interval does not carry the most load. The fifth class interval (21.09 m3 s1) in the resulting histogram (Fig. 2E) carries the most load and becomes the effective discharge under the power curve approach. To help demonstrate the influence that the number of class intervals has on Q e results,

4.1. Sites with sufficient data Using the mean approach, 12 of the USGS sediment sites and 11 of the ISWS sediment sites had sufficient discharge and sediment data and yielded load histograms with distinct peaks. The watershed area upstream of each of these sites and the correlation coefficient (R 2) of the power curve fitting each site’s suspended sediment data are listed in Table 2. Each site’s channel slope, mean discharge ( Q avg), and minimum and maximum discharges ( Q min and Q max) are tabulated (Table 2). Estimates for each site’s 1.25-, 1.5-, and 2.0-year flow events ( Q 1.25, Q 1.5, and Q 2.0, respectively) are also presented (Table 2). Three of these stations (USGS# 05520500, 05527500, and 05585000) had both ISWS and USGS suspended data from which an effective discharge value could be computed. At these three stations, effective discharge values were computed using the mean and power curve approaches for both data sets. Altogether, 20 different stream locations throughout Illinois are listed in Table 2. These sites drain between 632 and 16,480 km2 and have channel slopes between 0.00013 and 0.00070. The correlation coefficients (R 2) for power curve regressions of suspended sediment versus discharge at these sites ranged from 0.32 to 0.92. On average, power curve regression analyses yielded a correlation coefficient of 0.78. 4.2. Effective discharge and associated parameters Results for the effective discharge computations are summarized in Tables 3 and 4. The Q e, R.I., Exc. Y ss, and Y ssVQ e values from the mean and power curve approaches are presented in Table 3 along with the class interval size used in each computation. Each site’s Q avg (mean discharge), Q 1.50, and Q 2.0 are then divided by each site’s Q em and Q ep estimates and listed in Table 4. Ratios comparing Q em, Y ssm, and Y ssVQ em values to Q ep, Y ssp, and Y ssVQ ep values are also shown (Table 4). The following paragraphs discuss the magnitudes/recur-

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Table 2 Hydrologic parameters associated with each station for which effective discharge was computed Monitoring site (USGS#) Stations with USGS suspended Edwards River near New Boston (05466500) Iroquois River at Iroquois (05525000) Iroquois River near Chebanse (05526000) Kankakee River at Momence (05520500) Kankakee River near Wilmington (05527500) Kaskaskia River at Cooks Mills (05591200) Kaskaskia River near Venedy Station (05594100) La Moine River at Ripley (05585000) Little Wabash River at Louisville (03378900) Mackinaw River near Green Valley (05568000) Sangamon River near Oakford (05583000) Spoon River at Seville (05570000)

R2

Slopea

Q min (m3 s1)

Q avg (m3 s1)

Q 1.25 (m3 s1)

Q 1.5 (m3 s1)

Q 2.0 (m3 s1)

Q max (m3 s1)

sediment 0.8621

1153

0.00051

0.04

9

74

93

118

396

0.7004

1777

0.00021

0.16

17

76

92

111

289

0.8287

5416

0.00013

0.28

49

251

307

374

764

0.6239

5941

0.00017

7.02

59

139

163

191

419

0.7938

13,339

0.00024

7.08

126

440

552

699

1560

0.7812

1225

0.00029

0.00

13

102

124

151

274

0.8693

11,378

–

1.59

106

371

487

640

1379

0.8976

3349

0.00035

0.03

24

163

209

270

759

0.8861

1930

0.00050

0.01

16

195

246

317

595

0.8800

2779

0.00047

0.48

20

137

178

236

665

0.8685

13,191

0.00024

1.27

98

354

472

637

3398

0.9065

4237

0.00038

0.17

31

227

284

357

929

632

0.00051

0.00

8

63

80

103

249

5941

0.00017

7.02

59

139

163

191

419

13,339

0.00024

7.08

126

440

552

699

1560

4931

0.00026

0.10

43

217

278

360

1529

1696

0.00070

0.00

13

123

172

240

1008

3349

0.00035

0.03

24

163

209

270

759

8034

0.00022

0.00

78

293

359

442

1302

3434

0.00030

3.34

27

96

121

155

481

16,480

0.00016

14.18

122

289

347

416

906

1425

0.00052

0.00

12

90

117

153

529

1202

0.00044

0.00

10

76

106

148

425

Stations with ISWS suspended sediment Cache River at 0.9178 Forman (03612000) Kankakee River at 0.5195 Momence (05520500) Kankakee River near 0.7600 Wilmington (05527500) Kaskaskia River at 0.8603 Vandalia (05592500) La Moine River at 0.8345 Colmar (05584500) La Moine River at 0.7760 Ripley (05585000) Little Wabash River at 0.9133 Carmi (03381500) Pecatonica River at 0.4566 Freeport (05435500) Rock River at 0.3179 Rockton (05437500) Sangamon River at 0.8642 Monticello (05572000) Silver Creek near 0.8152 Freeburg (05594800) a

Basin area (km2)

Slope computed by Curtis (1987).

D.W. Crowder, H.V. Knapp / Geomorphology 64 (2005) 167–184

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Table 3 Effective discharge parameters computed using the mean and power curve approach Station name

Results obtained using the mean approach

Results obtained using the power curve approach

Class Qe R.I. Exc. Y ss Y ssVQ interval (m3d s1) (years) (days/ (T/year/ (T/year/ size year) km2) km2) 3 1 (m d s )

Q ep R.I. Exc. Y ss Class Y ssVQ e interval (m3 s1) (years) (days/ (T/year/ (T/year/ year) km2) km2) size 3 1 (m s )

Stations with USGS suspended sediment Edwards River 18.02 27.07 near New Boston Iroquois River 12.55 18.97 at Iroquois Iroquois River 30.57 76.70 near Chebanse Kankakee River 27.47 48.22 at Momence Kankakee River 62.12 224.49 near Wilmington Kaskaskia River 12.47 18.71 at Cooks Mills Kaskaskia River 55.09 139.30 near Venedy Station La Moine River at Ripley 30.35 106.26 Little Wabash River 54.05 81.09 at Louisville Mackinaw River 47.49 119.20 near Green Valley Sangamon River 226.42 340.91 near Oakford Spoon River at Seville 46.42 162.66 Stations with ISWS suspended sediment Cache River 9.94 34.80 at Forman Kankakee River 27.47 48.22 at Momence Kankakee River 62.12 100.26 near Wilmington Kaskaskia River 47.77 167.30 at Vandalia La Moine River 22.40 33.61 at Colmar La Moine River 30.35 106.26 at Ripley Little Wabash River 100.18 150.28 at Carmi Pecatonica River 19.12 32.02 at Freeport Rock River 35.67 67.70 at Rockton Sangamon River 11.03 16.53 at Monticello Silver Creek 23.59 35.39 near Freeburg

1.01

16

289.515 128.326

11.01

16.55

b1.01

31

b1.01

76

22.290

b1.01

60

b1.01

222.218 49.437

7.403

7.60

11.55

b1.01

115

13.949

3.337

39.364

9.764

30.57

46.13

b1.01

87

24.590

5.217

129

19.110

5.325

27.47

75.69

1.01

75

13.398

6.114

1.02

47

29.504

9.627

62.12

224.49

1.02

47

18.976

7.462

b1.01

53

33.799

8.933

6.53

9.80

b1.01

95

18.124

3.916

1.02

74

43.777

17.525

55.09

139.31

1.01

74

36.941 12.232

1.07 b1.01

17 15

165.693 86.281

99.068 52.090

18.97 5.26

123.32 160.49

1.11 1.13

15 7

113.441 46.862 72.040 43.499

1.17

6

103.443

65.715

18.47

28.18

b1.01

54

60.413 13.255

1.23

12

84.090

60.283 147.66

222.77

1.08

28

84.012 32.023

1.08

10

213.501 135.368

38.69

58.20

b1.01

33

171.688 40.912

1.04

16

124.080

69.706

4.69

21.11

b1.01

38

77.645 23.708

b1.01

129

24.207

8.273

27.47

48.22

b1.01

129

18.910

6.446

b1.01

117

22.614

5.549

62.12

162.37

b1.01

74

17.912

6.290

1.11

14

85.677

68.471

38.22

57.43

b1.01

58

71.801 22.974

1.01

23

193.334

69.651

7.64

11.45

b1.01

68

111.273 13.044

1.07

17

97.032

56.657

8.07

12.13

b1.01

109

1.02

52

52.976

26.408

65.12

227.92

1.1

34

49.348 24.666

b1.01

50

49.530

33.153

19.12

32.02

b1.01

50

41.247 24.298

b1.01

194

23.211

5.703

35.67

67.69

b1.01

194

17.417

4.041

b1.01

53

37.723

14.209

5.88

8.82

b1.01

99

30.886

6.116

1.05

20

124.631

89.274

4.52

29.37

1.04

34

88.984 21.859

39.476

4.806

178

D.W. Crowder, H.V. Knapp / Geomorphology 64 (2005) 167–184

Table 4 Ratios of parameters computed from the mean and power curve approaches Monitoring site (USGS#)

Q avg / Q em

Q avg / Q ep

Q 1.5/ Q em

Q 1.5/ Q ep

Q 2.0/ Q em

Q 2.0/ Q ep

Q em/ Q ep

Yssm/ Y ssp

Y ssVQ em/ Y ssVQ ep

Stations with USGS suspended sediment Edwards River near New Boston (05466500) Iroquois River at Iroquois (05525000) Iroquois River near Chebanse (05526000) Kankakee River at Momence (05520500) Kankakee River near Wilmington (05527500) Kaskaskia River at Cooks Mills (05591200) Kaskaskia River near Venedy Station (05594100) La Moine River at Ripley (05585000) Little Wabash River at Louisville (03378900) Mackinaw River near Green Valley (05568000) Sangamon River near Oakford (05583000) Spoon River at Seville (05570000)

0.32 0.88 0.64 1.22 0.56 0.69 0.76 0.22 0.20 0.17 0.29 0.19

0.52 1.44 1.07 0.78 0.56 1.32 0.76 0.19 0.10 0.72 0.44 0.54

3.43 4.83 4.00 3.38 2.46 6.64 3.49 1.97 3.04 1.49 1.38 1.75

5.62 7.93 6.65 2.16 2.46 12.67 3.49 1.69 1.54 6.30 2.12 4.88

4.36 5.84 4.87 3.96 3.12 8.09 4.59 2.54 3.91 1.98 1.87 2.19

7.13 9.59 8.10 2.52 3.12 15.46 4.59 2.19 1.98 8.37 2.86 6.13

1.64 1.64 1.66 0.64 1.00 1.91 1.00 0.86 0.51 4.23 1.53 2.79

1.30 1.60 1.60 1.43 1.55 1.86 1.19 1.46 1.20 1.71 1.00 1.24

2.60 2.22 1.87 0.87 1.29 2.28 1.43 2.11 1.20 4.96 1.88 3.31

Stations with ISWS suspended sediment Cache River at Forman (03612000) Kankakee River at Momence (05520500) Kankakee River near Wilmington (05527500) Kaskaskia River at Vandalia (05592500) La Moine River at Colmar (05584500) La Moine River at Ripley (05585000) Little Wabash River at Carmi (03381500) Pecatonica River at Freeport (05435500) Rock River at Rockton (05437500) Sangamon River at Monticello (05572000) Silver Creek near Freeburg (05594800)

0.24 1.22 1.26 0.26 0.39 0.22 0.52 0.84 1.81 0.72 0.29

0.40 1.22 0.78 0.75 1.14 1.96 0.34 0.84 1.81 1.35 0.35

2.30 3.38 5.51 1.66 5.11 1.97 2.39 3.79 5.13 7.09 3.00

3.79 3.38 3.40 4.83 15.00 17.22 1.57 3.79 5.13 13.28 3.62

2.96 3.96 6.98 2.15 7.15 2.54 2.94 4.84 6.15 9.25 4.18

4.88 3.96 4.31 6.26 20.99 22.23 1.94 4.84 6.15 17.33 5.03

1.65 1.00 0.62 2.91 2.93 8.76 0.66 1.00 1.00 1.87 1.21

1.60 1.28 1.26 1.19 1.74 2.46 1.07 1.20 1.33 1.22 1.40

2.94 1.28 0.88 2.98 5.34 11.79 1.07 1.36 1.41 2.32 4.08

All stations Average Median Minimum Maximum

0.60 0.52 0.17 1.81

0.84 0.76 0.10 1.96

3.44 3.38 1.38 7.09

5.76 3.79 1.54 17.22

4.37 3.96 1.87 9.25

7.39 5.03 1.94 22.23

1.87 1.53 0.51 8.76

1.43 1.33 1.00 2.46

2.67 2.11 0.87 11.79

rence intervals of the effective discharge values and observed differences between the mean and power curve approaches. Effective discharge values ranged from 8.82 to 341 m3 s1 and had recurrence intervals ranging from b1.01 to 1.23 years. Eleven of the twenty-three Q em estimates had recurrence intervals V 1.01 years. Only three Q em estimates had recurrence intervals z 1.1 years. Similarly, 17 of 23 Q ep estimates had recurrence intervals V 1.01 years. Three Q ep values had recurrence intervals z 1.1 years. Given that the annual maximum series recurrence interval computations cannot estimate recurrence intervals V1.01, flow exceedance values are needed to describe the relative frequency at which many of

the computed effective discharges occur. Such an analysis reveals that Q em and Q ep values are on average exceeded several times a year. The flows on the Mackinaw River near Green Valley exceeded the site’s Q ep on average 6 days/year (less frequently than at any other location). The flows on the Rock River at Rockton exceeded its Q em and Q ep value more frequently than at any other site (on average 194 days/year). On average, flows equaled or exceeded Q em values 52 days/year, while flows equaled or exceeded Q ep values 67 days/year. The discharges associated with the computed recurrence intervals are typically significantly smaller than a site’s 1.5- to 2.0-year flow events. Q 1.5 values were on average 3.44 times larger than Q em values,

D.W. Crowder, H.V. Knapp / Geomorphology 64 (2005) 167–184

while Q 2.0 values were on average 4.37 times larger than Q em values. Likewise, Q 1.5 values were on average 5.76 times larger than Q ep values, while Q 2.0 values were on average 7.39 times larger than Q ep values. Thus, most of these effective discharge values, regardless of how they were computed, have discharges significantly smaller than the 1.5- to 2.0-year flow events commonly associated with effective/ dominant discharge values. This is consistent with regional effective discharge values computed by Simon et al. (2004). Specifically, Simon et al. (2004) reported median Q e/Q 1.5 ratios of 0.60 and 0.70 for two major ecoregions covering Illinois. The median Q em/Q 1.5 and Q ep/Q 1.5 values computed for all sites in this study are 0.29 and 0.26, respectively. Differences between the Q e/Q 1.5 reported here and by Simon et al. (2004) are likely due to differences in where and how Q e computations were performed (e.g., sediment-rating curve development, class interval assignment) and further demonstrate the sensitivity of Q e computations. While all of the computed effective discharge values are smaller than the 1.5- and 2.0-year flow events, most of the computed effective discharge values are larger than a site’s mean discharge. Nineteen of the Q em estimates were greater than the site’s mean flow. Fifteen of the Q ep values were greater than the site’s mean flow. On average, a site’s mean discharge was 60% of the site’s Q em and 84% of the site’s Q ep. The mean and power curve approaches both generally predicted effective discharges larger than the mean flow and less than the 1.1-year flow event. Yet, two important differences exist between the two approaches. First, Q em values are on average 1.87 times greater than Q ep values (Table 4). Thirteen Q em values were greater than their corresponding Q ep values. Five computations had identical Q em and Q ep values, while five computations had Q em values less than their corresponding Q ep values. Given the differences in the data and class interval sizes used at different sites, identifying which factor caused Q em to be typically larger than or equal to Q ep values is difficult to determine. However, in 14 of the 23 computations (based on the number of class intervals used to compute Q em values), the mean approach assigned smaller loads to class intervals with relatively small flows (particularly the first class interval) and larger sediment loads to class intervals

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containing moderate flow events. For example, consider the first and fifth class intervals in Fig. 2A and B. The mean approach averaged 894 instantaneous load measurements and assigned a load of 23 T/day to the first class interval, and averaged 52 instantaneous load measurements and assigned a load of 2218 T/day to the fifth class interval. The power curve, with an R 2 value of 0.92, assigned a larger load (39 T/day) to the first class interval and a smaller load (757 T/day) to the fifth class interval. The effect that the different load assignments have on load histograms is clearly shown in Fig. 2C and D. The effective discharge shifts from the fourth class interval using the mean approach to the first class interval using the power curve approach. This same type of shift occurs when the number of class intervals is increased to 53. The Q em value (35.19 m3 s1) occurs in the eighth class interval, while the Q ep value (21.11 m3 s1) occurs in the fifth class interval. The second difference between the mean and power curve approaches is that the estimated sediment yield is typically about 1.43 times larger for the mean approach (Tables 3 and 4). Given Ferguson’s (1987) observations that load estimates derived from power curves systematically underestimate sediment load/yield values, the mean approach is expected to estimate larger sediment yields. Additionally, the amount of sediment transported by flows less than or equal to the effective discharge is, on average, 2.67 times greater for the mean approach. This reflects the tendency of the mean approach to estimate larger Q e values and load values to class intervals. Assigning larger load values to class intervals and increasing Q e values both increase Y ssVQ e values. If severe enough, this bincreaseQ in sediment load being transported by flows less than or equal to the effective discharge value could significantly influence channel design criteria. 4.3. Influence of class interval assignment The number of class intervals used in an effective discharge computation can significantly influence load histograms and effective discharge values. The influence that increasing the number of class intervals from 25 to 75 has on effective discharge values for the Cache River at Forman is summarized in Table 5. Q ep values were relatively small (2.49–4.97 m3 s1) and occurred within the first class interval for computa-

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Table 5 The influence that the number of class intervals has on effective discharge values Class intervals

Q em (m3 s1)

Q ep (m3 s1)

25 30 35 40 45 50 55 60 65 70 75

34.80 37.29 31.96 34.18 35.91 32.32 38.42 35.22 36.33 37.29 38.12

4.97 4.14 3.55 3.11 2.76 2.49 24.86 22.79 21.03 23.08 18.23

tions with 25–50 class intervals. When 55 or more class intervals were used, Q ep values increased (18.23–24.86 m3 s1) and did not occur in the first class interval. However, Q ep values did not continu-

ously increase with the number of class intervals. A comparison of the load histograms generated using 25, 53 and 75 class intervals (Figs. 2D,E and 3A) also reveals that the shape of the histogram generated by 53 and 75 class intervals are similar to each other, but substantially different from the shape generated using 25 class intervals. The number of class intervals used in a computation also influenced Q em values. Values for Q em ranged from 31.96 to 38.42 m3 s1 and did not continuously increase or decrease with the number of class intervals. A comparison of the load histograms generated using 25, 53, and 75 class intervals (Figs. 2C,F and 3B) demonstrates that under the mean approach, the basic shape of the load histograms did not significantly change as the number of class intervals was increased. However, as previously mentioned, continually increasing the number of class intervals under the mean approach will ultimately

Fig. 3. Load histograms for Cache River at Forman (75 class intervals): (A) power curve approach and (B) mean approach.

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cause a noisy histogram with poor load estimates for each of the class intervals. In this example, Q em values were fairly stable with changes in class interval size. However, experience showed that even with ample data in each class interval, Q em estimates, like Q ep estimates, can change significantly by slightly increasing or decreasing the number of class intervals used in a computation.

5. Discussion A desired outcome of regional effective discharge studies is to develop criteria for predicting effective discharge values for streams lacking flow and sediment monitoring data. To develop reliable regional effective discharge criteria for Illinois, researchers need to obtain four things: (i) information regarding effective discharge values in small streams; (ii) better methods of computing effective discharge; (iii) an improved knowledge of bed load transport processes; and (iv) a means of validating if computed effective discharge values truly represent channel forming/maintenance flows. None of the sites that had sufficient suspended sediment to compute effective discharge values had drainage areas b518 km2. Some researchers have concluded that Q eff recurrence intervals may be dependent on drainage area as well as other geographic and watershed characteristics (Wolman and Miller, 1960; Ashmore and Day, 1988). Consequently, assuming that the currently computed Q e recurrence intervals also apply for small Illinois streams may be inappropriate. To determine how Q e values differ between small and large Illinois streams, appropriate methods of monitoring and computing effective discharges on smaller Illinois’ streams need to be developed. To accurately characterize the flow frequencies and sediment loads of the short duration storm hydrographs of small streams, Q e values will probably need to be computed using sediment samples taken frequently throughout storm events and hourly or subhourly flow frequency curves (Biedenharn et al., 1999). Given that effective discharge represents the discharge that carries the most sediment load over an extended period of time, sediment sampling data used to compute an effective discharge value needs to

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accurately represent the long-term transport rates of a stream. One- or two-year sediment records may not reflect the various sediment transport rates of wet, dry, and normal years. One- and two-year sediment records may not even sample the loads carried by the 1.5- to 2-year flow events commonly assumed to be the effective discharge. Of the 88 monitoring sites analyzed, 59 had suspended sediment records of b5 years; 43 had records that were 2 years or less. Consequently, in addition to increasing sediment monitoring on small streams, long-term sediment monitoring programs on streams of all sizes throughout Illinois need to be maintained and expanded. Additionally, further examination regarding how sediment sampling frequency affects sediment yield, sediment load histograms and effective discharge estimates within small and large watersheds needs to be performed. Sampling daily, weekly, or daily and throughout storm events will provide different amounts of data, which may or may not accurately quantify long-term sediment load histograms, individual storm loads, and sediment hysteresis effects. The conceptual implications that sediment hysteresis has on effective discharge estimates warrants consideration. Does a steady continuous discharge provide the same geomorphic functions as the same discharge occurring on the rising and/or falling limb of a storm hydrograph? Moreover, criterion needs to be developed for defining effective discharge values on streams where two or more discharge intervals carry similar loads and could each be considered the effective discharge (e.g., some of the load histograms described by Ashmore and Day, 1988). In this study, effective discharge recurrence intervals were computed only for gaging locations that produced load histograms that had a relatively distinct effective discharge value. Despite the relatively large volume of literature devoted to effective discharge and dominant discharge theory, computing effective discharge remains a subjective procedure. Here, two approaches were used to estimate the average sediment load carried by each class interval in an effective discharge computation. The first (power curve) approach is a technique that has been used in several previous studies and is known to systematically underestimate suspended loads and yields. A second approach (the mean approach) was used to estimate the average

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sediment load carried by each class interval in an effective discharge computation. This approach does not appear to systematically bias sediment yields, but requires relatively large sediment data sets to assure that load values can be estimated for each class interval in a computation. Requiring that sediment samples be collected over at least 90% of a stream’s flow frequency and at least up to the 2.0-year recurrence interval, along with requiring a load histogram to have a distinct peak, reduced the 88 potential monitoring sites to 20 where effective discharge values could be estimated. The power curve and the mean approaches both yielded effective discharge recurrence intervals that were typically larger than a stream’s mean flow and less than its 1.1-year flow event. On average, the mean approach predicted higher Q e and Y ss values and reflect potentially important differences on how sediment loads are assigned to class intervals. These results are encouraging in light of the power curve’s bias toward underestimating sediment yields. However, which of these two methods is the most appropriate is not clear. The most appropriate method may, in part, depend on the amount of sediment data available. As both methods are sensitive to the number of class intervals used in the computation, estimating Q e values becomes a process subject to large uncertainties. Previous researchers have known about this sensitivity and adopted different criteria for assigning the number of class intervals in a computation. However, Sichingabula (1999) argued that this problem is severe enough to abandon effective discharge computations where class interval sizes must be subjectively decided. We agree that a more objective method of computing effective discharge values is vital to reliably compute and compare the Q eff values of two or more streams. In this study, suspended sediment loads are used to compute effective discharge. However, effective discharge values have also been computed using bed loads (e.g., Pickup and Warner, 1976) and total loads (e.g., Andrews, 1980). Knighton (1998) suggested that using bed load is more appropriate than using suspended sediment loads when describing channel formation processes and that bed load computations will typically generate higher effective discharge values than suspended sediment loads. Unfortunately,

the quantification of bed load transport rates is often quite difficult. Bhowmik et al. (1980) found that bed load samplers were incapable of adequately sampling the bed load transport rates of many of Illinois’ rivers. Moreover, the information needed to employ empirical bed load transport equations (e.g., bed material composition, cross-sectional geometry, and other parameters) is not readily available at most gaging sites. Finally, as observed by Graf (1983), bed load transport estimates can differ by several orders of magnitude depending on the empirical equation used. Consequently, computing effective discharge values based on bed load transport at many stations throughout a geographical region presents the same challenges as would be encountered using suspended sediment loads, but with much less available data. Regardless of these difficulties, a need exists for a better understanding of bed load transport rates within Illinois streams and how effective discharge values based on bed load and/or total load differ from those estimated using suspended sediment load. Observations by Wolman and Miller (1960) and Phillips (2002) suggest that in some streams, at least two discharges play an important role in forming and maintaining channel morphology. One of these discharges may be less than the bankfull discharge, while the other one may equal or exceed bankfull discharge. Consequently, establishing the specific roles that Q e, Q b, and other flows have within Illinois streams will be a crucial next step in establishing the appropriateness of applying dominant discharge theory in Illinois. Failure to understand these roles and to validate whether Q e can be used as a dominant discharge estimate may lead to channel designs that adversely influence conditions within as well as upstream and downstream of a restored site. Computing cross-sectional shear stresses and/or stream power occurring at various discharges and locations along a stream (as described by Magilligan, 1992; Knighton, 1999) provides a means of comparing the geomorphic work being performed by an effective discharge to its capacity to mobilize the streambed. Sediment modeling, as discussed in Shields et al. (2003), provides an additional method for validating and comparing the roles that Q e, Q b, or a range of discharge and the sediment loads they transport will have within streams.

D.W. Crowder, H.V. Knapp / Geomorphology 64 (2005) 167–184

6. Conclusions Effective discharge values were computed for streams throughout Illinois using two different methods to estimate sediment–discharge relationships (the power curve and mean approaches). Both methods produced Q e values that were typically larger than a stream’s mean flow but less than its 1.1-year flow event. Such values are significantly smaller than the 1.5-year flow event often assumed to be equivalent to a stream’s effective/dominant discharge. However, Q e values for streams draining areas b518 km2 are unavailable due to a lack of sediment and discharge monitoring on small streams. Effort needs to be made monitoring and computing effective discharge values at additional locations, particularly on smaller Illinois streams before attempts to regionalize Q e values can be made. A lack of data regarding morphologic and hydraulic conditions at monitoring sites also makes it impossible to validate whether these flows are actually dominant (channel maintenance/forming) flows. In general, effective discharge computations are significantly influenced by the sediment data used to compute effective discharge. Specifically, the technique used to establish sediment-rating relationships influences both the Q e value and the total sediment yield estimated to be carried by a stream. Effective discharge values are also sensitive to the number of class intervals used in a computation. The subjective nature and large uncertainties associated with effective discharge values continues to make the concept of comparing the geomorphic work being done in a watershed and/or designing stream restoration projects using effective discharge estimates problematic. Techniques are needed to make effective discharge values more objective. A fundamental understanding of the physical processes occurring within a channel under various flow rates (particularly at the stream’s effective and bankfull discharges) is also needed to better define the utility of dominant discharge theory.

Acknowledgements The support provided by the Illinois Environmental Protection Agency (IEPA) under Financial

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Assistance Agreement 9622 is gratefully acknowledged. The Illinois State Water Survey (ISWS), a Division of the Illinois Department of Natural Resources, also provided support. David Soong from the USGS graciously provided the mean, standard deviation, and skew coefficients of the maximum annual discharge data used in computing effective discharge recurrence intervals and each gaging station’s 1.25-, 1.5-, and 2-year flow events. The insights and editorial comments provided by Laura Keefer from the ISWS are also appreciated.

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