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Sediment Guidance Workgroup Daniels Fund Building Nov 12, 2013

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1 Sediment Guidance Workgroup Daniels Fund Building Nov 12, 2013
Pebble Counts and TIVs Sediment Guidance Workgroup Daniels Fund Building Nov 12, 2013

2 Today’s Topics Division’s protocol for pebble counts
Division’s current pebble count dataset TIVs Drunella doddsii

3 WQCD’s Pebble Count Methodology

4 Methods for Pebble Counts
Std sampling of surface and subsurface particle-size distributions in wadeable streams Based on methods of USFS Rocky Mountain Research Station – report by K. Bunte/S. Abt Effort results in 400 pebble counts

5 Equipment Materials needed: Surveyors tape & stakes Sampling frame
Gravelometer Waterproof field sheet Pencil

6 Field Method Define reach length Locate bankfull
The place on each bank where the stream rises during a large water event, a 1-2 year flood event Measure bankfull-to-bankfull width Avg. 3 bankfull widths

7 Field Method Define reach length Calculate sampling reach length
20 x avg. bankfull width = reach length Example: 20 x 13’ = 260’

8 Field Method Set transects Set survey tape across bottom of reach (0’)
Equidistantly space transects 1/10th the total sampling reach length Example: 260’ / 10 = 26’ (between transects) Move upstream to 26’ transect, 52’, etc. 26’ 0’ 104’ 26’ 52’ Flow 78’

9 Field Method Measuring along the transect
Measure bankfull to bankfull length (Ex. 20’) Divide length by 10 (Ex. 20’ / 10 = 2’) This is the distance in which to move the sampling frame along the transect tape Begin at left or right bankfull position Flow 2’ 20’ 0’

10 Sampling Frame The sampling frame consists of 4 aluminum bars that are connected to form a square Elastic white bands are stretched horizontally across the frame The spacing of the grid points is adjusted to a size equal to or larger than the dominant large particle size (=D95) 95th percentile grain diameter 60 cm

11 x 10 = 40 Field Method Using the sampling frame
Place corner of frame on the transect tape Particles are collected from under all four grid points 1 x 10 = 40 4 2 counts per transect 3

12 Measurements Gravelometer A sturdy, aluminum template
Holes correspond to Wentworth particle size classes Determine a particle’s sieve diameter in terms of “smaller than” the hole of a given size Example: A rock with a 60 cm b-axis (or intermediate axis) would be tallied as smaller than 64 mm in the “smaller than” approach

13 Field Documentation Field sheet
Habitat, bank, and water line documented

14 Office - Data Entry Data entry from field sheet to pebble count uploader Uploader output = CSV file Field Sheet Electronic Template

15 Pebble Count Database CSV files appended to Division’s pebble count db on annual basis % fines can be queried and matched to macroinvertebrate MMIs or metrics

16 Pebble Count Dataset 504 pebble/bug pairings 390 usable pairings
289 WQCD 106 EPA REMAP 54 EPA NRSA 41 EPA WEMAP 14 USFS 390 usable pairings Older pairs, winter mos., low counts removed 85 reference sites

17 Pebble Count Dataset Retained pairings presently used in:
TIV calculations - % Fines Stressor Identification

18 Tolerance Indicator Values “TIVs”
Brachycentrus americanus

19 Literature Source Estimation and application of indicator values for common macroinvertebrate genera and families of the United States Daren M. Carlisle, Michael R. Meador, Stephen R. Moulton II, Peter M. Ruhl National Water Quality Assessment Program, U.S. Geological Survey 12201 Sunrise Valley Drive, MS 413, Reston, VA, USA Published in Ecological Indicators 7 (2007) 22-33

20 Highlights Tolerance of macroinvertebrate taxa to chemical and physical stressors is widely used in interpretation of bioassessment data If taxa are sensitive to specific pollutants, TIVs will help in diagnosing potential causes of impairment Estimated genus- and family-level indicator values from the NAWQA dataset Weighted averages were calculated for 3 synthetic gradients and 2 uncorrelated physical variables

21 Method Background Substrate size estimation Study area
45 major drainage basin across continental U.S. Including Colorado basins Biological sampling Richest-targeted habitat (RTH) Chemical sampling Substrate size estimation For % fines

22 TIV Estimation Estimated TIVs by calculating abundance-weighted averages (WA) of chemical and physical variables Statistical steps Manage left-censored data Principle component analysis Generate independent stressor gradients Correlations Transform WAs into ordinal ranks (10-pt scale) Assigned genus TIVs to each synthetic stress gradient & variable 1 = sensitive; 10 = tolerant

23 Weighted Average Results
Synthetic gradients Ionic concentration – sulfate, conductivity, pH Nutrient concentration – phosphorus, NO5 (inc. chloride) D.O./water temp Uncorrelated physical variables Suspended sediment Percent fines

24 USGS Results Calculated TIVs for 102 genera and 67 families for each synthetic stress gradient and variable Used common genera Used 300 organism fixed-count method 100 of those 102 genera are documented in Colorado Source: CO EDAS

25 Application The next logical step in Colorado bioassessment
EDAS MMI Thresholds Stressor ID Colorado’s MMI is a tool that is specifically calibrated to detect impairment to aquatic life…but it is limited to detecting stress to the aquatic community, not the specific stressor(s) TIVs provide the means to identify specific stressor(s) by understanding the sensitivities and tolerances of genera to those stressors

26 Testing TIV Concept in Colorado
Tested % fines and phosphorus in 2012 Presented findings at Sep 25, 2012 Listing Methodology workgroup meeting (in Avon, CO) Workgroup saw merit in TIVs but recommended CO-specific TIVs because many genera common to CO were missing 303(d) cycle cancelled

27 Current and Future Work
CO-specific common species CO-specific TIVs and Stressor Identification Test Site Analysis - multimetric and multivariate metrics are used as the raw data to compare the biological condition of a test site and comparable reference sites Method includes all available biological information, accounts for and identifies redundant information which decreases the probability of misclassifying a test site

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