Sediment Assessment Methodology: Considerations, Physical and Biological Parameters, and Decision Making

DRAFT

May 2010

By: Paul Kusnierz and Andy Welch MONTANA DEPARTMENT OF ENVIRONMENTAL QUALITY Monitoring and Assessment Section, Water Quality Planning Bureau P.O. Box 200901 Helena, MT 59620-0901 TABLE OF CONTENTS

Introduction ...... 1

Data Collection ...... 2 Reach Length ...... 3 Permanent Sites ...... 4 Training...... 4 Pebble Count ...... 4 Grid Toss ...... 6 Pool Frequency ...... 7 Residual Pool Depth ...... 7 Rosgen Channel Type ...... 8 Biological Data ...... 9

Biological Indices ...... 9

Analysis ...... 10 Data from other entities ...... 11 Linkage to sources ...... 12 Summary ...... 12

References ...... 13

Appendix A. Second Year Approach ...... 21 Additional Methods ...... 21 Subsurface Fines ...... 21 Intragravel Dissolved Oxygen and Flow ...... 22 Residual Pool Volume and V* ...... 22

Appendix B. Field Methodology ...... 24

Introduction

Excess sediment can be detrimental to the biotic communities within a water body. Increasing sediment levels can lead to a decrease in periphyton biomass (Yamada and Nakamura 2002) as well as a decrease in macroinvertebrate density and diversity (Waters 1995) and a shift in macroinvertebrate community (Suttle et al. 2004). Effects on salmonids include impaired growth and survival of juveniles (Suttle et al. 2004), reduced redd escapement by fry (Fudge et al. 2008), and decreased spawning success (Tappel and Bjornn 1983; Reiser and White 1988; Weaver and Fraley 1993). In addition to excess sediment causing a reduction in habitat quality, filling of riffles and pools by sediment, fine or coarse, can effectively reduce the quantity of habitat available for organisms during part or all of their life cycle.

The Montana narrative standard for sediment states: “No increases are allowed above naturally occurring concentrations of sediment or suspended sediment (except as permitted in 75-5-318, MCA), settleable solids, oils, or floating solids, which will or are likely to create a nuisance or render the waters harmful, detrimental, or injurious to public health, recreation, safety, welfare, livestock, wild animals, birds, fish, or other wildlife” (17.30.623 (f)). Thus, defining what “natural” is within a system and demonstrating harm to the aquatic biota as a result of increased sediment are integral to determining whether or not a water body is impaired by sediment. Although it is well documented that high percent fines can limit salmonid embryo survival, high percent fines could be the natural condition for a water body. For example, Magee et al. (1996) demonstrated that elevated percent fines did not seem to be the factor limiting recruitment of cutthroat trout in a Montana stream. This lends support to the suggestion made by Chapman (1988) that high levels of fines may not suppress adult salmonid populations if some other factor such as overwintering habitat is limiting. In addition, if a stream is impacted by sediment, different locations within the stream may be affected differently (Lisle 1989). Identifying the natural sediment condition within a water body as well as demonstrating that sediment is proving harmful to aquatic life among the physical, chemical, and biological complexity present in aquatic systems is no simple task. Despite this challenge, the development of well- planned sampling schemes and collection of biologically relevant and statistically rigorous data should help streamline and simplify this process.

The purpose of this paper is to describe techniques to be used when making sediment caused beneficial use impairment determinations. This is part of the development of a translator that will streamline the decision making process with regards to sediment condition and the effects of sediment on the “Aquatic Life/Fishes” beneficial use. The translator will require specific data to be collected and input in order for the Montana Department of Environmental Quality (DEQ) to make consistent sediment impairment decisions. The methods described are supported by peer-reviewed literature and represent the best options for collecting reproducible and statistically rigorous data with limited bias. The following methods are designed to answer specific questions related to the instream sediment condition. Although this paper represents our best efforts to address sediment impairment in most western Montana streams, it is by no means an end- all approach to making such determinations.

Data Collection

The protocol described herein will be performed for the assessment of sedimentation/siltation and bedload solids for mountain and valley streams in western Montana. These streams may flow out onto the Northwestern Glaciated or Northwestern Great Plains, but originate in the Northern Rockies, Middle Rockies, Canadian Rockies, or Idaho Batholith level III ecoregions (Woods et al. 2002). Additional constraints to this protocol include that streams must be perennial or intermittent, and Strahler order ≤ 5 (Horton 1945; Strahler 1952, 1957). These constraints have been placed on this assessment protocol recognizing that the physical processes and chemical composition of mountain streams in western Montana are vastly different from those of large rivers and eastern Montana prairie streams. These situations present their own unique challenges such as scale for large rivers and turbidity, high summer water temperature, and a tolerant ichthyofauna for eastern Montana prairie streams. Sediment assessment protocols that address large rivers and eastern Montana prairie streams will be developed by DEQ in the future.

Our intent in this process is to protect the most sensitive beneficial use (i.e. fish). To aid in the determination of fine sediment impairment, riffle and pool-tail percent fines will be examined. These both address surface fines within a stream. For the purpose of this document, percent fines are defined as the percentage of sampled substrate material that is either less than 6 mm or less than 2 mm as these are values supported throughout the literature as having effects on aquatic biota (Phillips et al. 1975; Shepard et al. 1984; Weaver and Fraley 1993; Yamada and Nakamura 2002; Suttle et al. 2004; Edwards et al. 2007). If deemed necessary by the assessor, larger or finer size fractions (e.g. 9.5 mm, 0.85 mm (Tappel and Bjornn 1983)) may be examined. The goal of measuring percent fines is to evaluate the quality of available spawning habitat within a stream.

This paper puts forth a more stringent method of sampling percent surface fines (via a pebble count (Wolman 1954)) than has been used in the past by DEQ. This method is designed to reduce sampling bias, improve representation of the substrate present, and improve data comparability and reproducibility both within and between streams. A second means of measuring percent surface fines, the grid toss, will be used in pool tails exclusively (Kershner et al. 2004; MDEQ n.d.; Heitke 2008).

To further address fine sediment issues as well as consider possible aggradation and coarse sediment supply, residual pool depth (Lisle 1987) and pool frequency will be measured. Measuring changes in pool structure are important as pools serve as habitat for a variety of aquatic organisms (Lewis 1969; Bisson et al. 1982; Sullivan et al. 1987) Increased sediment in a system can cause a reduction in pool size, depth, and number, and an increase in distance from one pool to the next (Lisle 1982; Buffington et al. 2002). These resulting changes could have important ramifications for aquatic populations’ stability, persistence, and ability to recover from disturbance (Lonzarich et al. 2004). Measuring pool metrics will help DEQ determine the amount of habitat available for juvenile rearing and adults.

If it is deemed that a single season of sampling is insufficient to make an impairment determination with regards to sediment, data collection during multiple seasons and/or the collection of additional sediment metrics may be necessary. Methods for measuring subsurface fines, intragravel dissolved oxygen (which may be affected by the percent fines (Maret et al. 1993; Kondolf 2000)), residual pool volume, and V* (the fraction of pool volume filled with fine sediment (Lisle and Hilton 1991; 1992)) are discussed in Appendix A.

The following considerations and metrics apply when sediment sources are identified during an assessment, impairment needs to be reassessed, and/or when describing natural in reference waterbodies. All physical metrics should be collected under base flow conditions to limit the likelihood of misidentifying a feature due to flow conditions (e.g. identifying a feature as a run at high or moderate flows when it would be considered a riffle at base flow).

Reach Length

The goal in defining the sample frame from which data will be collected is to gain a representative snapshot of the stream segment being considered in the assessment. To ensure data representativeness, sampled reaches must be relatively homogenous (i.e., the reach is not a transition between two channel types). The length of a stream reach that will be sufficient to effectively describe habitats can vary depending on the heterogeneity of the stream. Although different programs use different sample frame distances for their reach length (e.g. EPA’s EMAP uses 40 times wetted width (Kaufmann et al. 1999), Rosgen (1996) uses 20 times bankfull width), generally 20 times the wetted width is the minimum distance used (Simonson et al. 1994; Fitzpatrick et al. 1998). Using reach lengths at least 20 times bankfull width means that sampling will likely take place over multiple meander wave-lengths and riffle-pool units and will aid in determining averaged values for specific populations (e.g, riffles) that account for local effects (Bunte and Abt 2001). Leopold et al. (1964) determined that riffle/pool sequences are typically 5-7 bankfull widths in length and Keller and Melhorn (1973) reported riffle/pool to be 3-9 bankfull widths. As a result, the recommended reach length for sediment sampling should be a minimum of 20 times the bankfull width. This reach length will generally be long enough to sample the desired number of physical features, will be appropriate for a greater range of stream widths, can provide more rigor in statistical analysis (Simonson et al. 1994), and will allow for a more reliable representation of residual pool metrics (Keim and Skaugset 2002) than using a shorter reach length. If 20 times the bankfull width is not sufficient in length to meet sampling requirements (e.g., to meet the minimum number of riffles sampled), the reach should be extended beyond 20 times the bankfull width. Using a reach length minimum of 20 times the bankfull width equates well with the reach length system used by DEQ TMDL planners when verifying sediment impairment (MDEQ 2006).

3 Permanent Sites

Each sampling site should be benchmarked (downstream and upstream extent) to allow for future sampling of the same features (Roper et al. 2002; Roper et al. 2003; Olsen et al. 2005). Permanent sampling sites will allow for analysis of change at given locations over time, comparisons of variability within and between sites, and provide a starting point for other groups to conduct sampling (TMDL data collection, annual monitoring by conservation districts, etc.). In addition to helping provide rigorous data for assessing impairment, using permanent sites will facilitate the development of trends through time as watershed restoration plans and best management practices are implemented. Collection of data at permanent sites will be an asset when performing five-year reviews and reassessments of 303(d) listed waterbodies. To benchmark sampling reaches and measured pools, riffles, etc. within a reach, GPS coordinates, photos, and a written description of the location should be documented.

Collaboration between multiple entities within DEQ should take place to determine sampling locations that will be useful to involved groups. Agreement on site locations lengths will help create a database for a water body that can be used and built upon by each section (Monitoring and Assessment, Watershed Management, Watershed Protection) and demonstrate potential changes throughout the 303(d) process.

Training

In order to collect rigorous data that has limited bias and variability, comprehensive training of those collecting data will need to take place (Marcus et al. 2005). Training should include how to classify habitat types (Roper and Scarnecchia 1995) and objectively measure habitat metrics (Roper et al. 2002). Training should include a manual containing clear instructions describing how to set up sampling sites, properly use measurement tools, and correctly record metric measurements. A two-phased training is recommended where the first phase consists of a demonstration of how habitat classification and measurement should take place and the second consists of the trainee(s) collecting data while being overseen by the trainer(s). Proper training of how to collect sediment data is an essential part of making a defendable impairment decision and should be stressed to all those collecting data to be used in sediment assessment(s).

Pebble Count

The sensitivity of riffles to increased sediment supply makes them a suitable location to sample for changes in substrate size distribution (Parker and Klingeman 1982; Dietrich et al. 1989). In addition, riffles serve as a winter refuge for juvenile salmonids. Excess fine sediment can fill interstices and reduce the availability of this crucial habitat (Bjornn et al. 1974). To sample the surface fines within riffles, the Wolman pebble count should be used (Wolman 1954). This method is considered to be accurate and reproducible when sampling a single, homogeneous, population (e.g. one riffle; Kondolf 1997). At least 100 particles should be sampled from each population as this value has been deemed sufficient for the criteria previously stated (Wolman 1954; Brush 1961; Mosley and

4 Tinsdale 1985). The pebble count should be performed within the bankfull channel. Although collecting the particles in this way might skew the results toward the size of sediments outside of the actual aquatic habitat at the time of sampling, Mebane (2001) demonstrated strong correlation between percent fines collected within the bankfull width and mactroinvertebrate metrics. To reduce sampling bias, transects with set sampling locations and a sampling frame should be used to determine which particles will be collected (Bunte and Abt 2001a; Bunte and Abt 2001b; Bunte et al. 2009). Each riffle should be benchmarked to allow for future sampling of the same feature and the detection of sediment composition changes (Roper et al. 2002). In addition, a gravelometer should be used to measure the combined b,c -axis of each particle, as this helps to reduce operator error in measuring the particles and is more compatible with sieve data than using a ruler (Kondolf 1997; Bunte and Abt 2001a; Bunte and Abt 2001b; Bunte et al. 2009). It is recommended that at least four individual riffles be sampled per reach to yield a combined 400 riffle particle count which according to Fripp and Diplas (1993) and Rice and Church (1996) yields the most efficient sampling results. If more than four riffles are present in a sampling reach, four should be randomly chosen to sample. This can be done by defining each riffle with a number and either using a random number table or writing the numbers on pieces of paper, and drawing four numbers out of a hat. If fewer than four riffles are present, the reach should be extended to a length that includes four riffles. The determination of individual riffle populations to be sampled can be performed visually (Roper and Scarnecchia 1995). If substrate within a population is poorly sorted, more than 100 particles may need to be sampled (Wolman 1954).

Within each riffle, four transects will be evenly distributed (from downstream to upstream) at 20, 40, 60, and 80% of the riffle length. Along each transect, 25 sampling locations will be evenly spaced within the bankfull width so that the distance between each is 1/25 of the bankfull width. Bankfull width will be recorded for each pebble count transect. Figure 1 provides a schematic of the riffle pebble count setup.

Figure 1. Setup for performing a riffle pebble count.

Grid toss

The grid toss should be used to describe the substrate quality in the most likely spawning areas within a stream. Salmonids tend to spawn in areas of streams where either downwelling or upwelling is present (Kondolf 2000). As a result, pool tails are a likely salmonid spawning location (Keller et al. 1990) especially for rainbow trout as demonstrated by Muhlfeld (2002). Grid toss counts of fine sediments should be performed in the tail of all scour pools (not formed by logs or some other debris damming the downstream end of the pool) containing suitable sized spawning gravels (D50 = 15 mm to 61 mm for fish 150 mm – 610 mm; Kondolf and Wolman 1993; Kondolf 2000) within the sampling reach. These pools must be at least 50% of the wetted channel width and have a maximum depth ≥ 1.5 times the pool-tail depth (Heitke et al. 2008). If more than ten pools suitable for spawning are identified, the first ten will be sampled. Three of the sampled pool tails should be benchmarked to allow for future sampling of the same feature (Roper et al. 2002). Three grid tosses will be performed in each of the pool tails (as described by Heitke 2008; Figure 2). Using the method found in Heitke (2008), percent fines values for sediment < 6 mm will be measured.

Figure 2. Locations within a pool tail to be sampled with the grid toss. The figure is from Heitke et al. (2008).

Pool Frequency

Pool frequency is simply a count of the number of pools encountered within a sampling reach. For a pool to be counted, it must occupy at least 50% of the wetted channel width and have a maximum depth ≥ 1.5 times the pool-tail depth (Heitke et al. 2008). When conducting a pool count, it should be noted whether or not the pool is being influenced by woody debris. From the reach-wide pool number, the average distance between pools can be calculated. Average pool spacing can be quite variable, from one channel width to nine or more, depending on the stream type, local setting, and amount of large woody debris (LWD) present (Keller and Melhorn 1973; Bilby 1984; Beschta and Platts 1986; Montgomery et al. 1995). In addition, the narrower the stream, the more variable pool spacing becomes (Montgomery et al. 1995). Despite the variability in this metric and the need to carefully consider all possible factors that can affect it, Wood-Smith and Buffington (1996) were able to correctly classify disturbed and undisturbed watersheds with at least 90% accuracy when using this metric and the ratio of mean residual pool depth to mean bankfull depth in discriminant function analysis. Due to the natural high variability with this metric, it should be applied carefully in sediment impairment determination and should be tested to determine whether or not it may be better to use this metric for detecting change/stability of a stream through time (i.e., the 303(d) listing process).

Residual Pool Depth

Residual pool depth (RPD) should be collected within each sampling reach. Pools in which RPD is measured must be formed by the scouring action of water (not formed by logs or some other debris damming the downstream end of the pool) and be at least 50% of the wetted channel width and have a maximum depth ≥ 1.5 times the pool-tail depth (Heitke et al. 2008). The benefit of using residual metrics over those measured based on wetted width is that they are flow independent (no discharge relationship needs to be determined) and give an indication of the minimum amount of available pool habitat

7 (Lisle 1987). To reduce error in the sampling of pools, a “more is better” approach should be taken. The goal of those collecting data should be to collect residual metrics from every pool within the sample reach as Keim and Skaugset (2002) demonstrated that collecting residual pool volume data over a long reach may provide a better representation than looking at individual pools. If a stream reach contains many pools and time does not permit sampling every one, a random subset should be sampled. These pools can be chosen in the same manner as riffles for pebble counts. This set should contain 10 – 20 pools to capture the variability within the reach and reduce error (Hilton and Lisle 1993; 1999). If more than 25 pools are present within a reach, pools will be chosen randomly for measurement. Instructions on how to collect residual pool depth can be found in Lisle (1987; also see Figure 3). To help standardize the metrics based on physical composition of the stream, mean bankfull width (collected during riffle pebble counts) will be used. Reach mean for RPD will be considered in impairment determination. To detect possible trends, four pools should be benchmarked for future sampling.

Figure 3. Profile (adapted from Lisle 1987) of a pool and locations to measure when determining residual pool depth. dr = residual pool depth; dp = total pool depth at the deepest point along the thalweg; drc = depth of the riffle crest at the thalweg.

Rosgen Channel Type

At each site, measurements will be made to determine Rosgen channel type (A, B, C, etc., Figure 4; Rosgen 1996). This method considers variables such as stream slope, sinuosity, with to depth ration, and flood-prone area and will help ensure that similar streams are being compared. Reference data will be combined based on channel type so that comparisons between reference and candidate sites are meaningful.

Figure 4. Rosgen stream channel types from Rosgen (1994).

Biological Data

Within each sampling reach, macroinvertebtrates and periphyton will be collected. These samples will be collected via DEQ protocol for the specific biological metrics that will be used in analysis of biological condition. All samples will be preserved and stored until after the physical habitat metrics have been analyzed. If it is determined that sediment levels are above natural based on the physical data (Figure 6), then the biological data will be examined as well.

Biological Indices

Although the physical metrics primarily address sediment impacts to fish, they are also applicable to diatoms and macroinvertebrates. As a result of this and DEQ actively developing biological indices for diatoms and macroinvertebrates, the biological indices used in forming impairment decisions will currently focus on these forms of biology. For the collection of macroinvertebrates, the “Targeted Riffle” method (Peck et al. 2001) will be used. Periphyton will be collected following the “Peri-1” method (MDEQ 2005). Diatom indicators similar to those found in Bahls et al. (2008) are near completion and will soon be available for use with this method. These methods are specifically designed to detect sediment impairment at the Level III Ecoregion scale. DEQ is in the process of developing and refining macroinvertebrate indices similar to the macroinvertebrate multimetric indices and river invertebrate prediction and classification system discussed by Feldman (2006). Both diatom and macroinvertebrate indices should be ready for use within a year.

Fish indices are not currently being developed by DEQ, but testing of those already developed for other States’ systems (Hughes et al. 1998, Hughes et al. 2004, McCormick et al. 2001, Mebane et al. 2003) may prove useful in the decision making process. If DEQ does develop fish indicators and/or concludes already developed indices useful in the future, they could be easily added to the process

9 Analysis

DEQ defines “reference” as the condition of a water body capable of supporting its present and future beneficial uses when all reasonable land, soil, and water conservation practices have been applied. For a water body to be “reference” it does not need to be pristine. Instead, reference waterbodies are those that demonstrate little to no impact from anthropogenic sources and are supporting all applicable beneficial uses. Reference waterbodies are either Tier 1 (Natural Condition) or Tier 2 (Minimally Impacted Condition) as determined by criteria that have been considered with intensive, on site inspection and best professional judgment (Suplee et al. 2005).

Data analysis may take place after one season of data is collected (riffle fines, pool-tail fines, pool count, and RPD collected at preferably more than one site). To compare sediment data collected from reference sites with that of impaired sites, a two step process will be used. First, the data will be stratified (by ecoregion, Strahler order, Rosgen channel type, etc.) so that like streams are being compared. Second, two statistical techniques, comparison of percentiles and the Mann Whitney U test, will be used to determine if data collected from an assessed stream differs from the reference condition (Table 1). Both of these techniques are appropriate because there are no distribution requirements for them and water quality data is typically not normally distributed and involves low numbers of samples (Helsel and Hirsch 1992). When performing the analysis, data will be examined in the order defined by the “Reference Priority” section of Table 1 with the decisions being made by the highest priority for which sufficient data is available.

Table 1. Priority for various techniques for comparing assessment waterbodies to reference data and types of analysis used for each . Reference Priority Analysis

1) Internal & External Internal – 1 to 1 External – 1 to many comparison No Reference – Use (higher weight to comparison 1) a. When a maximum value is literature values with internal reference data) a. Mann- sought, apply the 75th percentile of best professional Whitney U test reference to median of target. judgment. 2) Internal with α = 0.10 b. 75th percentile of target must be below the 90th percentile of a. Median of target 3) External * Pool similar reference. must be within range impaired sites. of literature values or 4) Literature 2) a. When a minimum value is interquartile range. sought, apply the 25th percentile of b. Must use reference to median of target. biological indicators. b. 25th percentile of target must be above the 10th percentile of reference.

If all physical metrics are within the reference range, then biological measures do not need to be considered and the water body will be considered “Not Impaired” (Figure 5). If one to three of the metrics are not within the constraints of the reference range, then biology will be the determining factor as to whether or not the water body is considered “Impaired.” When four or more metrics are outside of the reference range, biology will be considered. If the biology indicates non-impairment at this point, a second season of

10 sampling must take place, during which, the same metrics will be sampled again and the additional methods found in Appendix A should be considered for sampling. When two years of data have been collected, they should be combined for analysis.

If after two years of sampling the same outcome is reached, a decision should be made based on the data collected and the best professional judgement of the assessor, a biologist, and DEQ management. This decision flow allows that variability is inherent within streams and that cases may arise where components of the physical composition may seem degraded but without adverse impacts to the biology. It also takes a cautious approach when the majority of metrics suggest physical degradation, but biology suggests non-impairment.

Figure 5. Decision flow chart for determining sediment impairment.

Data from other entities

DEQ realizes that other entities (federal and state agencies, contractors, landowners, etc.) may provide data that is relevant to the water body being assessed. This data will be considered in the formal assessment once it has been determined that it meets DEQ requirements for data quality. If data does not meet the requirements to be directly included in the DEQ collected dataset, it will be used to supplement the determination.

11 Linkage to sources

A key component to the determination of sediment impairment is that sediment levels must be above natural. For this to occur in a water body, there must be anthropogenic sources. When collecting and analyzing data related to the sediment condition of a water body, the question, “Does this measure show a linkage between the source and the impact the source is creating?” must constantly be considered. This is an important concept because a water body could naturally have high levels of fine sediment due to the underlying geology and have anthropogenic sources that do not contribute to the sediment load above the natural variability. An example of this consideration might be a stream with underlying granitic geology that naturally has a high level of fine sediment < 6 mm. This water body may also have locations where cattle crossings are a possible input of fine, dark colored, sediment < 1 mm. If pebble counts indicate that there is are 70% fines < 6mm and 10% fines < 1 mm, but none of the sediment < 1mm is of the same composition as the fines potentially being input at the cattle crossings, then it becomes very difficult (if at all possible) to say that the crossings can be linked to the high amount of fines in the stream. Linking anthropogenic sources of sediment to measureable physical metrics are an important step in determining the natural condition of a water body and must be fully considered when making a defendable impairment decision.

Summary

Sediment is a leading cause of water body impairment in Montana. This pollutant can have a variety of adverse effects on many different aquatic organisms. Past 303(d) listings for sediment were often based on best professional judgment with little data that was rarely collected in a standardized manner. The method discussed in this paper is our attempt at developing a sediment impairment determination method that is both standardized and defendable. The metrics collected and the way in which they are measured are supported by the current peer-reviewed literature. The process we have developed to make an impairment decision helps account for natural variability and specifies when data is insufficient for a determination. The method described should lead to consistent and reproducible sediment impairment decisions.

12 References

Bahls, L., M.Teply, R. Sada de Suplee, and M.W. Suplee. 2008. Diatom biocriteria development and water quality assessment in Montana: a brief history and status report. Diatom Research 23: 533-540.

Barnard , K. and S. McBain. 1994. Standpipe to determine permeability, dissolved oxygen, and vertical particle size distribution in salmonid spawning gravels. Fish Habitat Relationships Technical Bulletin Currents 15: 1-12.

Beschta, R.L. and W.S. Platts. 1986. Morphological features of small streams: significance and function. Water Resources Bulletin 22: 369-379.

Bilby, R.E. 1984. Removal of woody debris may affect stream channel stability. Journal of Forestry 83: 609-613.

Bisson, P.A., J.L. Nielson, R.A. Palmason, and L.E. Gore. 1982. A system of naming habitat types in small streams, with examples of habitat utilization by salmonids during low stream flow. In Acquisition and utilization of aquatic habitat information. N.B. Armantrout. Pp. 62-73. Western Division, American Fisheries Society, Portland, OR.

Bjornn, T.C., M.A. Brusven, M. Molnau, F.J. Watts, R.L. Wallace, D.R. Neilson, M.F. Sandine, and L.C. Stuehrenberg. 1974. Sediment in streams and its effects on aquatic life. Research Technical Completion Report Project B-025-IDA. Idaho Water Resources Research Institute, University of Idaho, Moscow, ID.

Brush, L.M. 1961. Drainage basins, channels, and flow characteristics of selected streams in central Pennsylvania. U.S. Geological Survey Professional Paper 282-F.

Buffington, J.M, T.E. Lisle, R.D. Woodsmith, and S. Hilton. 2002. Control on the size and occurrence of pools in coarse-grained forest rivers. River Research and Applications 18: 507-531.

Bunte, K., S.R. Abt, J.P. Potyondy, and K.W. Swingle. 2009. Comparison of three pebble count protocols (EMAP, PIBO, and SFT) in two mountain gravel-bed streams. Journal of the American Water Resources Association 45: 1209-1227.

Bunte, K, and S.T. Abt. 2001a. Sampling surface and subsurface particle-size distributions in wadeable gravel- and cobble-bed streams for analyses in sediment transports, hydraulics, and streambed monitoring, General Technical Report RMRS-GTR-74. USDA, Forest Service, Rocky Mountain Research Station, Fort Collins, CO.

13 Bunte, K. and S.R. Abt. 2001b. Sampling frame for improving pebble count accuracy in coarse gravel-bed streams. Journal of the American Water Resources Association 37: 1001-1014.

Chapman, D.W. 1988. Critical review of variables used to define effects of fines in redds of large salmonids. Transactions of the American Fisheries Society 117: 1- 21.

Coble, D.W. 1961. Influence of water exchange and dissolved oxygen in redds on survival of steelhead trout embryos. Transactions of the American Fisheries Society 90: 469-474.

Dietrich, W.E., J.E. Kirchner, H. Ikeda, and F. Iseya. 1989. Sediment supply and the development of the coarse surface layer in gravel-bed rivers. Nature 340: 215- 217

Edwards, P.J., T.C. Cain, and C.J. Gagen. 2007. Using multiple fine-sediment size classes to evaluate the condition of trout spawning habitat. From: M. Furniss, C. Clifton, and K Ronnenberg, eds. Advancing the Fundamental Sciences: Proceedings of the Forest Service National Earth Sciences Conference, San Diego, CA, 18-22 October 2004, PNW-GTR-689, Portland, OR: USDA Forest Service, Pacific Northwest Research Station.

Feldman, D. 2006. A report to the DEQ Water Quality Planning Bureau on the proper interpretation of two recently developed macroinvertebrate bioassessment models. Montana DEQ, Water Quality Planning Bureau, Helena, MT.

Fitzpatrick, F. A., I.R. Waite, P.J. D’Aconte, M.R. Meador, M.A. Maupin, and M.E. Gurtz. 1998. Revised methods for characterizing stream habitat in the National Water-Quality Assessment Program, Water-Resources Investigations Report 98- 4052. U.S. Geological Survey, Raleigh, NC.

Fripp, J.B. and P. Diplas. 1993. Surface sampling in gravel streams. Journal of Hydraulic Engineering 119: 473-490.

Fudge, T.S., K.G. Wautier, R.E. Evans, and V.P. Palace. 2008. Effect of different levels of fine-sediment loading on the escapement success of rainbow trout fry from artificial redds. North American Journal of Fisheries Management 28: 758-765.

Grost, R.T., W.A. Hubert, and T.A. Wesche. 1991. Field comparison of three devices used to sample substrate in small streams. North American Journal of Fisheries Management 11: 347-351.

Hames, D.S., B. Conrad, A. Pleus, D. Smith. 1996. Field comparison of the McNeil sampler with three shovel-based methods used to sample spawning substrate

14 composition in small streams. Northwest Indian Fisheries Commission, Report. TFW-AM-9-96-005.

Hansen, E.A. 1975. Some effects of groundwater on brown trout redds. Transactions of the American Fisheries Society 104: 100-110.

Heitke, J.D., E.J. Archer, D.D. Dugaw, B.A. Bouwes, E.A. Archer, R.C. Henderson, J.L. Kershner. 2008. Effectiveness monitoring for streams and riparian areas: sampling protocol for stream channel attributes. Unpublished paper on file at: http://www.fs.fed.us/biology/fishecology/emp.

Helsel, D.R. and R.M. Hirsch. 1992. Statistical Methods in Water Resources. Elsevier, New York, NY.

Hilton, S. and T.E. Lisle. 1993. Measuring the fraction of pool volume filled with fine sediment. Research Note PSW-RN-414. USDA Forest Service, Pacific Southwest Research Station, Albany, CA.

Hoffman, R.J. 1986. A horizontal intragravel pipe for sampling water quality in salmonid spawning gravel. North American Journal of Fisheries Management 6: 445-448.

Hughes, R.M., P.R. Kaufmann, A.T. Herlihy, T.M. Kincaid, L. Reynolds, and D.P. Larson. 1998. A process for developing and evaluating indices of fish assemblage integrity. Canadian Journal of Fisheries and Aquatic Sciences 55: 1618-1631.

Hughes, R.M, S. Howlin, and P.R. Kaufmann. 2004. A biointegrity index (IBI) for coldwater streams of western Oregon and Washington. Transactions of the American Fisheries Society 133: 1497-1515.

Horton, R.E. 1945. Erosional development of streams and their drainage basins. Bulletin of the Geological Society of America 56: 275-370.

Kaufmann, P.R., P. Levine, E.G. Robison, C. Seeliger, and D.V. Peck. 1999. Quantifying physical habitat in wadeable streams. EPA/620/R-99/003. U.S. Environmental Protection Agency, Washington D.C.

Keim, R.F. and A.E. Skaugset. 2002. Physical aquatic habitat I. errors associated with measurement and estimation of residual pool volumes. North American Journal of Fisheries Management 22: 145-150.

Keller, E.A., G.M. Kondolf, and D.J. Hagerty. 1990. Groundwater and fluvial processes: selected observations. Geological Society of America Special Paper 252: 319- 340.

15 Keller, E.A. and W.N. Melhorn. 1973. Bedforms and fluvial processes in alluvial stream channels: selected observations. In Fluvial Geomorphology. M. Morisawa (ed.). State University of New York, Binghamton, NY.

Kershner, J.L, E.K. Archer, M. Coles-Rithchie, E.R. Cowley, R.C. Henderson, K. Krantz, C.M. Quimby, D.L. Turner, L.C. Ulmer, and M.R. Vinson. 2004. Guide to effective monitoring of aquatic and riparian resources. General Technical Report RMRS-GTR-121. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO.

Kondolf, G.M. 2000. Assessing salmonid spawning gravel quality. Transactions of the American Fisheries Society 129: 262-218.

Kondolf, G.M. 1997. Application of the pebble count: notes on the purpose, method, and variants. Journal of the American Water Resources Association 33: 79-87.

Kondolf, G.M. and M.G. Wolman. 1993. The sizes of salmonid spawning gravels. Water Resources Research 29: 2275-2285.

Leopold, L.B., M.G. Wolman, and J.P. Miller. 1964. Fluvial processes in geomorphology. W.H. Freeman and Co., San Francisco, CA.

Lewis, S.L. 1969. Physical factors influencing fish populations in pools of a trout stream. Transactions of the American Fisheries Society 98: 14-19.

Lisle, T.E. and S. Hilton. 1999. Fine bed material in pools of natural gravel bed channels. Water Resources Research 35: 1292-1304.

Lisle, T.E. and S. Hilton. 1992. The volume of fine sediment in pools: an index of sediment supply in gravel-bed streams. Water Resources Bulletin 28: 371-383.

Lisle, T.E. and S. Hilton. 1991. Fine sediment in pools: an index of how sediment is affecting a stream channel. R-5 Fish Habitat Relationships Technical Bulletin Currents 6: 1-6.

Lisle, T.E. 1989. Sediment transport and resulting deposition in spawning gravels, north coastal California. Water Resources Research 25: 1303-1319.

Lisle, T.E. 1987. Using “residual pool depths” to monitor pool depths independently of discharge. Research Note PSW-394. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, CA.

Lisle, T.E. 1982. Effects of aggradation and degradation on riffle-pool morphology in natural gravel channels, northwestern California. Water Resourses Research 18: 1643-1651.

16 Lonzarich, D.G., M.L. Warren, Jr., and M.E. Lonzarich. 2004. Consequences of pool habitat isolation on stream fishes. In Ouachita and Ozark Mountains symposium: ecosystem management research. J.M. Guldin, tech. comp. General Technical Report SRS-74. USDS Forest Service, Southern Research Station, Asheville, NC.

Magee, J.P, T.E. McMahon, and R.F. Thurow. 1996. Spatial variation in spawning habitat of cutthroat trout in a sediment-rich basin. Transactions of the American Fisheries Society 125: 768-779.

Marcus, W.A., S.C. Ladd, J.A. Stoughton, and J.W. Stock. 1995. Pebble counts and the role of user-dependent bias in documenting sediment size distributions, Water Resource Research 31: 2625–2631.

Maret, T.R., T.A. Burton, G.W. Harvey, and W.H. Clark. 1993. Field testing of new monitoring protocols to assess brown trout spawning habitat in an Idaho stream. North American Journal of Fisheries Management 13: 567-580.

McCormick, F.H., R.M. Hughes, P.R. Kaufmann, D.V. Peck, J.L. Stoddard, and A.T. Herligy. 2001. Development of an index of biotic integrity for the mid-Atlantic highlands region. Transactions of the American Fisheries Society 130: 857-877.

McNeil, W.J. and W.H. Ahnell. 1964. Success of pink salmon spawning relative to size of spawning bed materials. USFWS Special Scientific Report –Fisheries No. 469. Washington, D.C.

MDEQ. 2006. Survey site lengths for geomorphic characterization. Montana DEQ, Water Quality Planning Bureau, Helena, MT.

MDEQ. 2005. Field Procedures Manual for Water Quality Assessment Monitoring. Montana DEQ, Water Quality Planning Bureau, Helena, MT.

MDEQ. n.d. Longitudinal field methodology for the assessment of TMDL sediment and habitat impairments. Montana DEQ, Water Quality Planning Bureau, Helena, MT.

Mebane, C.A., T.R. Maret, R.M. Hughes. 2003. An index of biological integrity (IBI) for Pacific Northwest rivers. Transactions of the American Fisheries Society 132: 239-261.

Montgomery, D.R., J.M. Buffington, R.D. Smith, K.M. Schmidt, and G. Pess. 1995. Pool spacing in forest channels. Water Resources Research 31: 1097-1105.

Mosley, M.P. and D.S. Tinsdale. 1985. Sediment variability and bed material sampling in gravel bed rivers. Earth Surface Processes and Landforms 10: 465-482.

17 Muhlfeld, C.C. 2002. Spawning characteristics of redband trout in a headwater stream in Montana. North American Journal of Fisheries Management 22: 1314-1320.

Olsen, D.S., B.P. Roper, J.L. Kershner, R. Henderson, and E. Archer. 2005. Sources of variability in conducting pebble counts: their potential influence on the results of stream monitoring programs. Journal of the American Water Resources Association 41: 1225-1236.

Parker, G. and P.C. Klingeman. 1982. On why gravel bed streams are paved. Water Resources Research 18: 1409-1423.

Peck, D.V., J.M. Lazorchak, and D.J. Klemm. 2001. Environmental Monitoring and Assessment Program – Surface Waters: Western Pilot Study Field Operations Manual for Wadeable Streams. EPA/XXX/X-XX/XXX April 2001, U.S. Environmental Protection Agency, Washington, D.C.

Phillips, R.W., R.L. >antz, E.W. Claire, and J.R. Moring. 1975. Some effects of gravel mixtures on emergence of coho salmon and steelhead trout fry. Transactions of the American Fisheries Society 104: 461-466.

Platts, W.S., W.F. Megahan, and W.G. Minshall. 1983. Methods for evaluating stream, riparian, and biotic conditions, general technical report INT-138. USDA Forest Service, Rocky Mountain Research Station, Ogden, UT.

Reiser, D.W. and R.G. White. 1988. Effects of two sediment size-classes on survival of steelhead and Chinook salmon eggs. North American Journal of Fisheries Management 8: 432-437.

Reiser, D.W., and Wesche, T.A. 1977. Determination of physical and hydraulic preferences of brown and brook trout in the selection of spawning locations.Water Res. Ser. No. 64.Water Resources Research Institute, University of Wyoming, Laramie.

Rice, S. and M. Church. 1996. Sampling surficial fluvial gravels: the precision of size distribution percentile estimates, Journal of Sedimentary Research 66: 654-665.

Roper, B.B., J.L. Kershner, and R.C. Henderson. 2003. The Value of Using Permanent Sites When Evaluating Stream Attributes at the Reach Scale. Journal of Freshwater Ecology 18: 585-592.

Roper, B.B, J.L. Kershner, E. Archer, R. Henderson, and N. Bouwes. 2002. An evaluation of physical stream habitat attributes used to monitor streams. Journal of the American Water Resources Association 38: 1637-1646.

18 Roper, B.B. and D.L. Scarnecchia. 1995. Observer variability in classifying habitat types in stream surveys. North American Journal of Fisheries Management 12: 49-53.

Rosgen, D.L. 1994. A classification of natural rivers. Catena 22: 169-199.

Rosgen, D.L. 1996. Applied River Morphology. Wildland Hydrology, Pagosa Springs, CO.

Shepard, B.B., S.A. Leathe, T.M. Weaver, and M.D. Enk. 1984. Monitoring levels of fine sediment within tributaries to Flathead Lake, and impacts of fine sediment on bull trout recruitment. Wild Trout III Symposium, Yellowstone National Park, WY, September 24-25, 1984.

Simonson, T.D., J.Lyons, and P.D. Kanahl. 1994. Quantifying fish habitat in streams: transect spacing, sample size, and a proposed framework. North American Journal of Fisheries Management 14: 607-615.

Sowden, T.K. and G. Power. 1985. Prediction of rainbow trout embryo survival in relation to groundwater seepage and particle size of spawning substrates. Transactions of the American Fisheries Society 114: 804-812.

Strahler, A.N. 1952. Dynamic basis of geomorphology. Geological Society of America Bulletin 63: 923-938.

Strahler, A.N. 1957. Quantitative analysis of watershed geomorphology. Transactions of the American Geophysical Union 38: 913-920.

Stylt, T.L. and J.C. Fichenich. 2002. Techniques for measuring substrate embeddedness, EMRRP Technical Notes Collection (ERDC TN-EMRRP-SR-36). U.S. Army Engineer Research and Development Center, Vicksburg, MS.

Sullivan, K., T.E. Lisle, C.A. Dolloff, G.E. Grant, and L.M. Reid. 1987. Stream channels: the link between forests and fishes. In Streamside management: forestry and fishery interactions. E.O. Salo and T.W. Cundy, eds. Pp. 39-97. College of Forest Resources, University of Washington, Seattle, WA.

Suplee, M., R. Sada de Suplee, D. Feldman, and T. Laidlaw. 2005. Identification and assessment of Montana reference streams: a follow-up and expansion of the 1992 benchmark biology study. Montana DEQ, Water Quality Planning Bureau, Helena, MT.

Suplee, M. 2008. Sampling the Gibson Reservoir tailrace pool and the upper Sun River for dissolved oxygen and total dissolved gas, Sampling and analysis plan-2008 addendum. Montana DEQ, Water Quality Planning Bureau, Helena, MT.

19 Suttle, K.B., M.E. Power, J.M. Levine, and C. McNeely. 2004. How fine sediment in riverbeds impairs growth and survival of juvenile salmonids. Ecological Applications 14: 969-974.

Tappel, P.D. and T.C. Bjornn. 1983. A new method of relating size of spawning gravel to salmonid embryo survival. North American Journal of Fisheries Management 3: 123-135.

VanDusen, P.J., C.J.F. Huckins, and D.J. Flaspohler. 2005. Associations among selection logging history, brook trout, macroinvertebrates, and habitat in northern Michigan headwater stream. Transactions of the American Fisheries Society 134: 762-774.

Waters, T.F. 1995. Sediment in streams: sources, biological effects, and control. American Fisheries Society Monograph 7.

Weaver, T.M. and J.J. Fraley. 1993. A method to measure emergence success of westslope cutthroat trout fry from varying substrate compositions in a natural stream channel. North American Journal of Fisheries Management 13: 817-822.

Wolman, M.G. 1954. A method of sampling coarse river-bed material. Transactions of the American Geophysical Union 35: 951-956.

Woods, A.J., J.M. Omernik, J.A. Nesser, J. Shelden, J.A. Comstock, and S.H. Azevedo. 2002. Ecoregions of Montana, 2nd edition (color poster with map, descriptive text, summary tables and photographs). Map scale 1:1,500,000.

Wood-Smith, R.D. and J.M. Buffington. 1996. Multivariate geomorphic analysis of forest streams: implications for assessment of land use impacts on channel condition. Earth Surface Processes and Landforms 21: 277-393.

Yamada, H. and F. Nakamura. 2002. Effect of sediment deposition and channel works on periphyton biomass in the Makomanai River, northern Japan. River Research and Applications 18: 481-493.

Young, M.K., W.A. Hubert, and T.A. Wesche. 1991. Bias associated with four stream substrate samplers. Canadian Journal of Fisheries and Aquatic Sciences 48: 1882-1886.

Zimmermann, A.E. and M. Lapointe. 2005. Intergranular flow velocity through salmonid redds: sensitivity to fines infiltration from low density sediment transport events. River Research and Applications 21: 865-881.

20 Appendix A. Second Year Approach

In cases where the decision flow chart indicates “at risk” after the first year of data collection, a second year of data collection must take place. The goal of collecting data in a second year is to capture temporal variability in the five standard metrics and increase the rigor of the dataset. In addition, the second year of data collection provides an opportunity to use additional metrics that may help to answer specific questions. When planning the second year of data collection, a local biologist and/or hydrologist should be contacted (if feasible) to determine what additional metrics may be appropriate to address the issues in that particular water body.

Additional Metrics

The following additional methods are suggested to be used when the core metrics discussed in the main portion of this document do not yield a straight forward determination with regards to sediment impairment. These methods require more time and resources to complete than performing those previously mentioned. As a result, they should be reserved for use during the second season of sampling in addition to the core metrics sampled during the first. If time and resources permit and/or a specific question needs to be answered, these additional methods may be considered for integration into the initial assessment process. These methods are to be used in addition to those previously discussed and should not be the sole determining factor in making a sediment impairment decision.

Subsurface Fines

The subsurface substrate in gravel river beds tends to be finer than that of the surface layer (Parker and Klingman 1982). Although the creation of redds by salmonids effectively reduces the amount of fines compared to non-redd substrate (McNeil and Ahnell 1964), over time, the interstices can refill with fine sediment (Zimmermann and Lapoint 2005). Because salmonid embryo development takes place in subsurface substrate, collection of these particles may provide a better link to survival than surface fine measures. In much of the literature, the overall composition of the substrate is considered (Tappel and Bjornn 1983; Shepard et al. 1984; Maret et al. 1993; Weaver and Fraley 1993; VanDusen et al. 2005) making combined surface/subsurface fine measurements potentially more comparable to the literature.

When sampling subsurface substrate, either McNeil (McNeil and Ahnell 1964) coring or some method of shovel technique may be used (Grost 1991; Young et al. 1991; Hames et al. 1996). Young et al. (1991) determined that the McNeil core method most often yielded results that were similar to the true substrate composition though differences between the McNeil and shovel methods were few. Grost et al. (1991), was able to demonstrate that the two methods yielded similar results in the field and suggested the use of a stilling well around the shovel to improve sampling accuracy. When a stilling well and shovel method was used by Hames et al. (1996) they found that this method compared relatively well to the McNeil method though they were unable to develop a

21 conversion that would make the results of these two technique comparable. Hames et al. (1996) made the recommendation that regardless of the method used, the same method should always be used so that data are comparable. For the purposes needed by DEQ it is recommended that the shovel technique with a stilling well be used to collect subsurface sediment samples. This method is less costly and requires the use of lighter equipment than the McNeil method (Grost et al. 1991; Hames et al. 1996). By using this method alone, DEQ will be using the method that is most efficient and appropriate for a variety of situations with minimal monetary investment for the collection of comparable and meaningful data. Shovel samples should be collected in suspected (pool-tails and the head of riffles (Reiser and Wesche 1977)) or known spawning locations to provide data that are biologically relevant with regards to salmonid embryo development. If another method such as McNeil cores has already been collected for the assessed water body, it is recommended that the same method be used to ensure data comparability.

Intragravel Dissolved Oxygen and Flow

Fine sediments less than 1 mm have been linked to reduced permeability in gravel (Kondolf 2000). This reduction of permeability can reduce intragravel flow and thus, dissolved oxygen as well. Two distinct methods for sampling intragravel dissolved oxygen are often used in the literature: 1) the standpipe (Terhune 1958; Coble 1961; Hansen 1975; Sowden and Power 1985; Barnard and McBain 1994) and 2) the horizontal pipe (Hoffman 1986; Maret et al. 1993). The standpipe method has been used by DEQ (Suplee 2008). Which method should be used is dependent on the questions being asked. If a long term dataset (weeks, months, etc) with multiple measurements of dissolved oxygen at one location is desired, the horizontal pipe method is likely to be used. The use of the standpipe method would likely be more appropriate when taking multiple point measurements, especially when developing a relationship between dissolved oxygen and some other variable such as percent fines or intragravel flow is desired. The standpipe method is the most likely method to be used by DEQ, but the weight of materials required to perform it may limit its use as a supplemental indicator for sediment impairment. As a result, it is recommended that standpipes and methods to collect substrate dissolved oxygen in this way be modified (e.g., decreased pipe length and pipe pounder weight) to make its use less cumbersome for remote and difficult to access sites. Sediment dissolved oxygen and intragravel flow can both be measured in a standpipe (Terhune 1958; Barnard and McBain 1994). As with subsurface sediment sampling, intragravel dissolved oxygen and flow should be sampled in known or suspected salmonid spawning locations.

Residual Pool Volume and V*

Residual pool volume can be measured while collecting other residual pool metrics, but because it requires many more point measurements to calculate may be too time consuming for initial assessment purposes. V* or the fraction of pool volume filled with fine sediment as been shown to correlate with the annual sediment yield of a channel and may be used to monitor the status of sediment supply in a system (Lisle and Hilton 1999). This method can also help capture the effects of specific sediment inputs (Lisle and

22 Hilton 1991; Lisle and Hilton 1992). This metric uses the residual pool volume with the addition of measuring the volume of fine sediment deposits within a pool. Lisle and Hilton (1991; 1992; 1999) provide a synopsis of how residual pool volume and V* should be collected and how each can be used to monitor sediment loading in streams.

23 Appendix B. Field Methodology

Flow of Activities: 1-Moving upstream, measure the reach length 2-Moving downstream, count the number of pools and riffles and map the reach 3-Moving upstream, perform grid toss, riffle count, and macroinvertebrate collection 4-Moving downstream, measure residual pool depth and collect periphyton

1) Determine the downstream extent of the sampling reach and use flagging to mark the location and record GPS coordinates. The area upstream of this point will be the area you have pre-determined as being sampled.

2) Determine the reach length. I. Record five bankfull measurements: one at the downstream extent of the reach, and then one at each of four more points working upstream from the initial measurement. Each measurement should be approximately two to three bankfull widths apart. Be careful to limit the amount of walking within the stream that takes place while making these measurements. Also be careful to limit substrate disturbance when walking in the stream. II. Calculate the mean bankfull width for the five measurements. Multiply this value by 20. The result is the reach length over which sampling will take place. III. Use a measuring tape to follow one side of the stream along the bankfull width, and measure the reach length from the downstream extent. When reaching the upstream end of the bankfull channel, use flagging to mark the location and record GPS coordinates. Note: If more than one channel exists, the one containing the higher discharge (ocular estimate) should be followed and later measured.

3) Count pools, identify riffles for sampling, and map the reach.

I. Working in a downstream direction (from the top of the reach), count the number of pools that, visually approximated, occupy at least 50% of the wetted channel width at one location in the main channel and have a maximum depth ≥ 1.5 times the pool-tail depth along the pool’s thalweg. II. For each pool counted, mark (Yes/No) if the pool is formed, in any way, by woody debris. III. Draw a map on the field form as you work in the downstream direction, referencing the location of pools, riffles, and pool-forming woody debris (Figure 1). After mapping the reach, label each pool and riffle with a number starting at the bottom of the reach with “Pool 1” and “Riffle 1,” increasing the number in the upstream direction (Figure 1).

Figure 1. Example of map drawn at the sampling site.

4) Collect pool-tail grid toss, riffle pebble count, and macroinvertebrates.

Working in the upstream direction, perform the appropriate sampling depending on the feature encountered.

If a pool is encountered, perform a grid toss: I. For a grid toss to take place in a pool, the pool must: 1) be formed by the scouring action of water (not formed by logs or some other debris damming the downstream end of the pool), 2) visually approximated, occupy at least 50% of the wetted channel width at one location in the main channel and have a maximum depth ≥ 1.5 times the pool-tail depth along the pool’s thalweg, and 3) contain substrate with D50 = 15 mm to 61 mm (visual estimate). Note on the field form the pools that were not sampled because they did not meet the substrate requirement.

25 II. Up to ten pools will be sampled with the grid toss. If more than ten suitable pools are present, the first ten encountered within the reach will be sampled. If fewer than three suitable pools are present, the reach should be extended upstream until it is long enough that three pools can be measured. Note on the field form how far upstream from the end of the reach you have traveled to encounter three suitable pools. Also, mark on the form which pools outside of the original reach were sampled and draw a map of the extended portion of the reach and label the sampled features. Randomly select and benchmark the pools sampled by recording the GPS coordinates, indicating on the map which pools were sampled, and taking photographs of the pool and associated landmarks (Figure 1). III. Sample the pool tail by working from river right to river left by tossing the grid at points 25%, 50%, and 75% across the pool’s wetted width at a distance that is 10% of the pool’s length or 1 m from the pool tail crest, whichever is less (Figure 2). Be sure to follow the contour of the pool tail when making the tosses. For each of the 49 internal intersections on the grid and the top right corner, count how many completely cover particles (each intersection is approximately 6 mm). Mark this number on the field form. If a portion of the grid lands on a particle that is a small boulder or larger (>512 mm b-axis), do not assess the intersections that fall on such substrate. On the field form, record the number of particles < 6 mm out of the number assessed (e.g., 8/40). If, in small streams, the grid tosses overlap, mark in the appropriate location on the field form to indicate that overlap occurred. If algae or organic debris on the streambed blocks visual identification of the particles underneath, do not move the obstruction. Record the number of intersections covering particles < 6 mm out of those that are not obstructed and can be assessed.

Figure 2. Locations within a pool tail to be sampled with the grid toss (figure is from Heitke et al. 2008).

If a riffle is encountered, perform the riffle pebble counts and macroinvertebrate sampling: I. Four riffles will be sampled, each with a 100 pebble count. If more than four riffles are present, randomly choose four by numbering each riffle and using a random number generator or drawing numbers from a hat to choose the four to be sampled. If fewer than four riffles are present, the reach should be extended upstream until it is long enough that four riffles can be measured. Mark on the field form how far upstream from

26 the end of the reach you have traveled to encounter four riffles. Also, mark on the form which pools outside of the original reach were sampled and draw a map of the extended portion of the reach and label the sampled features. Benchmark each riffle that is sampled by recording the GPS coordinates, indicating on the map which riffles were sampled, and taking photographs of the riffle and associated landmarks (Figure 1). II. Measure the length of the riffle. Within each riffle, four (measuring tape) transects will be evenly distributed (from downstream to upstream) at 20, 40, 60, and 80% of the riffle length. Along each transect, 25 sampling locations (assuming a 100 pebble count; it would be at 38 locations for a 152 pebble count) will be evenly spaced within the bankfull width so that the distance between each is 1/25 of the bankfull width (use a calculator to determine the distance at which each particle should be selected) (Figure 3). Bankfull width will be recorded for each pebble count transect. III. To sample the riffle, start at the downstream transect and sample from river right to river left, then proceed to the next upstream transect and sample from river left to river right, and repeat this pattern as moving upstream to the final two transects (Figure 3). Select each pebble by placing the sampling frame on the substrate directly below the calculated (1/25 bankfull distance) location on the measuring tape and pick up the particle that is directly underneath the sample frame intersection.

Figure 3. Setup for performing a riffle pebble count.

27 IV. Measure each particle by finding the smallest hole in the gravelometer that the particle will fit through. Particle size data will be recorded on the field form using the “dot/slash” system where 10 particles = . The four dots should be filled in first, followed by the outside lines of the box, and, finally, the diagonal lines. Note on the field form if particles outside of the wetted channel appear to be skewed (e.g., consist of primarily fine sediment). V. After performing a pebble count within a riffle, the riffle should be sampled for macroinvertebrates with the “Targeted Riffle” method (Peck et al. 2001; Figures 4 and 5). The methodology for sediment assessment will be the same as shown in figures 4 and 5 with two exceptions: 1) A two kicks will take place in each riffle that is sampled with a pebble count, and 2) A different form (from the one used for the EMAP project) will be used to record where and when riffle kicks were collected. Note in the field form the riffle and location within the riffle where kick(s) were sampled.

Figure 4. Locating sampling points for the targeted riffle sample (figure is from Peck et al. 2001). Note from the text of this appendix that two riffle kicks will be collected from each riffle in which a pebble count took place.

Figure 5. Instructions describing how to collect a riffle kick sample (figure is from Peck et al. 2001). *Note: a different field form will be used from that used for the EMAP project.

5) Collect residual pool depths and periphyton.

Working in the downstream direction, measure every suitable pool for residual pool depth (RPD) and collect periphyton from all representative habitats.

I. For RPD to be measured, the pool must: 1) be formed by the scouring action of water (not formed by logs or some other debris damming the downstream end of the pool) and 2) visually approximated, occupy at least 50% of the wetted channel width at one location in the main channel and have a maximum depth ≥ 1.5 times the pool-tail depth along the pool’s thalweg.

29 II. Every pool suitable for measuring RPD within a reach should be measured. If more than 25 pools are present, randomly choose 20 by numbering each pool and using a random number generator or by drawing numbers from a hat. If fewer than three suitable pools are present, the reach should be extended upstream until it is long enough that three pools can be measured. Note on the field form how far upstream from the end of the reach you have traveled to encounter three suitable pools. Also, mark on the form which pools outside of the original reach were sampled and draw a map of the extended portion of the reach and label the sampled features. Randomly select and benchmark three of the pools sampled by recording the GPS coordinates, indicating on the map which pools were sampled, and taking photographs of the pool and associated landmarks (Figure 1). III. To measure RPD, use a rod to measure from the substrate surface to the water surface at two locations: 1) the deepest point in the pool along the thalweg (dp) and 2) the depth of the riffle crest at the thalweg (drc)(Figure 6). The RPD (dr) = dp - drc. Record both measurements and the calculated RPD on the field form.

Figure 6. Profile of a pool and locations to measure when determining residual pool depth (adapted from Lisle 1987). dr = residual pool depth; dp = total pool depth at the deepest point along the thalweg; drc = depth of the riffle crest at the thalweg.

IV. While walking through the reach, periphyton will be collected via the “Peri-1” method (MDEQ 2005). Figure 7 contains instructions with regards to collection periphyton with this method.

Figure 7. Procedure to follow when collecting periphyton within the sampling reach (figure from MDEQ 2005).

7) Perform Rosgen Stream Classification measurements.

A channel cross section will be measured using a laser level at the crest of one of the pebble count sampled riffles. Note on the field form which of the riffles was measured. To measure the cross section, follow the instructions in figures 8 and 9 (MDEQ 2005).

Figure 8. Procedure to measure cross sections for Rosgen channel type classification (figure from MDEQ 2005).

Figure 9. Diagram of a stream cross section, description of measurements, and order in which measurements should be made (figure from MDEQ 2005).