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Assessing Morphological Performance of Stream Restoration in North Carolina
Barbara Doll, PE, Ph.D., Extension Specialist NC Sea Grant and Biological and Agricultural Engineering
Department, NC State University
Acknowledgements Co-Investigators
Greg Jennings, BAE Dept., NC State University (retired) Jean Spooner, BAE Dept., NC State University Dave Penrose, BAE Dept., NC State University (retired) Joseph Usset, Statistics Dept., NC State University (Ph.D.
candidate) Mark Fernandez, NC State University, BAE Dept.
(Master’s student) Jamie Blackwell, BAE Dept., NC State University Michael Shaffer, BAE Dept., NC State University
NC Clean Water Management Trust Fund Field Work – Dave Penrose, Greg Jennings, Mike
Shaffer, Karen Hall, Mark Fernandez, Dan Clinton, Lara Rozell, Jess Roberts, numerous other students
What is stream restoration? The process of converting an unstable, altered or degraded
stream corridor, including adjacent riparian zone and floodprone areas to its natural or referenced, stable conditions considering recent and future watershed conditions (NC DWQ)
Natural Channel Design (Hey, 2006)
“Rosgen” Method
Fluvial geomorphological method for designing NATURAL STABLE CHANNELS
Analogue procedure - cross-sectional area and pattern
relationships (i.e. sinuosity) are scaled from a natural stable reference stream to determine the restoration design
High-quality “reference” streams
serve as design templates
Determine Restoration Potential
Performance Range
Reference Reaches
Disturbed Channels
Restored Streams
Research Goal: • Develop tools for measuring functional uplift to advance the practice of stream restoration.
Research Objectives Obj. 1: Develop and evaluate methods for assessing
eco-geomorphological conditions of restored streams. Obj. 2: Compare condition of restored streams to
impaired and high quality reference channels. Obj. 3 Develop a “scale” for evaluating restoration
need and performance Obj. 4: Determine if location, site selection and design
relate to the resulting condition of restored streams
Eco-geomorphological = integration of hydrology, fluvial geomorphology and ecology in river systems
Scope of the Project
Visited 156 streams between 2006 – 2012 Applied five rapid stream assessment methods Sampled macroinvertebrate communities from
85 restored streams Compiled restoration design data for 79 streams Conducted watershed assessment for 130 streams Performed extensive multivariate statistical
analyses
Obj. 1 - Develop and Evaluate Stream Assessment Tools
n=65 restored streams Quantitative/Qualitative
EGA - Eco-Geomorphological Assessment (NCSU for CWMTF)
Qualitative (visual) SPA - Stream Performance Assessment (NCSU) SVAP - Stream Visual Assessment Protocol (USDA) RCE - Riparian, Channel and Environmental Inventory –
(Peterson) RBP- Rapid Bioassessment Protocols – habitat survey
(US EPA)
Eco-Geomorphologial Assessment EGA
A. Channel Condition
C. Aquatic Insects
B. Bank and Riparian Habitat
D. Condition and Function of Structures
Evaluation Categories
Sub-Categories # of variables
Points
Channel Condition
Bedform Condition 10 20 Dominant Substrate Material 3 12 Streambank Stability 6 24
Riparian Habitat
Riparian Vegetation 5 20 Floodplain Condition 6 24
Macro invertebrates
Community Structure 5 24 Cover and Refuge 12 20
In-stream Structures
Structure Function 4 16 Structure Condition 3 12
Total Score 54 172
Stream Performance Index (SPA) • Channel bedform
• Channel pattern
• Floodplain connection
• In-stream habitat features
• Sediment transport
• Streambank Condition
• Streambank vegetation
Rapid Visual Assessment of 17 Variables; Total Points = 110
Five stream assessment methods applied at 65 restored streams – EGA, SPA, RBP, RCE & SVAP
Objective 1 – Continued Evaluate Stream Assessment Tools – Determine how well assessments predict macroinvertebrate metrics). Method: Linear regression, principal component analysis (PCA) and principal component regression (PCR) (n=65 restored streams.
Response Variable: Aquatic Insects
Upstream and in-reach sampling compiled as 6 Macroinvertebrate Metrics :
No. of dominant taxa No. of dominant EPT taxa EPT abundance Dominant taxa in common DIC (%) % shredders and predators Number of indicator taxa
Dominant Taxa
EPT Taxa EPT
Abundance
% Shredders
& Predators
Indicator Taxa
DIC
R2 p R2 p R2 p R2 p R2 p R2 p
EGA 0.24 *** 0.29 *** 0.23 *** ns 0.26 *** ns SPA 0.07 · 0.10 * 0.07 · 0.09 · 0.14 * ns RBP 0.31 *** 0.37 *** 0.33 *** ns 0.42 *** ns RCE 0.28 *** 0.29 *** 0.26 *** ns 0.31 *** ns SVAP 0.18 ** 0.26 *** 0.25 *** ns 0.33 *** ns
Some correlations revealed for Number of Dominant Taxa, No. of Dominant EPT Taxa, EPT Abundance and No. of Indicator Taxa with all five assessment scores. However, variability is high.
Hypotheses: Prediction can be improved by 1) addressing collinearity and subjective variable weights and by 2) adding watershed factors.
Principal Component Analysis (PCA)
Dimension Reduction High-dimensional data with collinearity (lots of
variables that correlate) Reveals underlying structure in the data Produces a number of independent artificial
variables (called principal components or PCs) PCs are linear combinations of the original
variables. The weighted factor for each variable reflects its relative importance in explaining the variability of the specific PC
Dominant
Taxa EPT Taxa EPT
Abundance
% Shredders
& Predators
Indicator Taxa
No. of Variables
Total No. of PC's
% Variability Explained
EGA Total Raw Points 0.24 0.29 0.23 0.03 0.26 1 PCA EGA 0.61 0.68 0.62 0.29 0.58 44 11 76.3% SPA Total Raw Points 0.07 0.10 0.07 0.09 0.14 1 PCA SPA 0.49 0.47 0.32 0.16 0.39 17 7 78.1% RBP Total Raw Points 0.31 0.37 0.33 0.03 0.42 1 PCA RBP 0.37 0.46 0.39 0.20 0.45 13 5 77.4% RCE Total Raw Points 0.28 0.29 0.26 0.01 0.31 1 PCA RCE 0.61 0.69 0.65 0.19 0.65 19 8 77.6% SVAP Total Raw Points 0.18 0.26 0.25 0.02 0.33 1 PCA SVAP 0.59 0.70 0.65 0.09 0.66 14 6 78.6%
R-squared from Linear Regression
Watershed Assessment
using GIS
0
2
4
6
8
10
12
14
16
0.0 10.0 20.0 30.0 40.0 50.0 60.0
EPT taxa vs. Impervious Cover %
0
2
4
6
8
10
12
14
16
0.0 20.0 40.0 60.0 80.0 100.0 120.0
EPT taxa vs. Developed %
0
2
4
6
8
10
12
14
16
50.0 55.0 60.0 65.0 70.0 75.0 80.0 85.0 90.0
EPT taxa vs. CN
0
2
4
6
8
10
12
14
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0.1 1.0 10.0 100.0
EPT taxa vs. Watershed Size
0
2
4
6
8
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0 100 200 300 400 500
EPT taxa vs. Time of Concentration
y = 2.54ln(x) + 14.55R² = 0.59
0
2
4
6
8
10
12
14
16
0.0010 0.0100 0.1000 1.0000
EPT taxa vs. Basin Slope
V1= Basin Slope V2=Time of Concentration V3=Watershed Size
V4 = Curve Number V5=% Developed V6=% Impervious
First three PC’s for Watershed (90% Variance Explained)
Dominant
Taxa EPT Taxa EPT
Abundance
% Shredders
& Predators
Indicator Taxa
No. of Variables
Total No. of PC's
% Variability Explained
EGA Total Raw Points 0.24 0.29 0.23 0.03 0.26 1 PCA EGA 0.61 0.68 0.62 0.29 0.58 44 11 76.3% PCA (EGA +Watershed) 0.74 0.81 0.72 0.26 0.70 50 12 77.3% SPA Total Raw Points 0.07 0.10 0.07 0.09 0.14 1 PCA SPA 0.49 0.47 0.32 0.16 0.39 17 7 78.1% PCA (SPA + Watershed) 0.66 0.68 0.53 0.20 0.56 23 8 78.8% RBP Total Raw Points 0.31 0.37 0.33 0.03 0.42 1 PCA RBP 0.37 0.46 0.39 0.20 0.45 13 5 77.4% PCA (RBP + Watershed) 0.63 0.72 0.59 0.24 0.65 19 6 77.4% RCE Total Raw Points 0.28 0.29 0.26 0.01 0.31 1 PCA RCE 0.61 0.69 0.65 0.19 0.65 19 8 77.6% PCA (RCE + Watershed) 0.77 0.82 0.72 0.17 0.73 25 9 78.2% SVAP Total Raw Points 0.18 0.26 0.25 0.02 0.33 1 PCA SVAP 0.59 0.70 0.65 0.09 0.66 14 6 78.6% PCA (SVAP + Watershed) 0.72 0.79 0.66 0.11 0.66 20 7 79.5% Watershed 0.65 0.70 0.55 0.13 0.52 6 PCA Watershed 0.41 0.43 0.34 0.09 0.40 6 2 78.6%
R-squared from Linear Regression
Conclusion 1 Rapid stream assessments ability to predict
aquatic macroinvertebrate metrics in restored streams can be improved with ordination and addition of watershed variables.
Conclusion 2 Rapid stream assessments best predict EPT,
indicator and total number taxa metrics.
Artwork by Ethan Nedeau
Obj. 2 – Compare eco-geomorphological condition of
restored streams to impaired and high quality reference channels. SPA - (NCSU) 156 Streams: 93 restored, 21 impaired, 29 reference
quality, and 13 reference streams with minor incision
Method: Use PCA and PC-based factor analysis to compare stream performance by stream condition
Stream Locations
First 3 SPA PC’s
explain 57.5 %
of variance n=156
# Variable F1 F2 F3 15 Streambank condition 0.85 0.02 0.16 17 Floodplain function 0.78 -0.01 0.05 16 Streambank vegetation 0.77 0.26 0.06 14 Sediment transport 0.72 -0.09 0.37 6 Pattern 0.64 -0.21 0.12 10 Rootmats 0.22 0.82 0.04 11 Overhanging veg -0.29 0.74 0.12 8 Leaf packets -0.11 0.71 0.15 9 Undercut banks 0.29 0.68 0.02 3 Riffles length slope 0.16 0.14 0.86 1 Riffles pools alternating 0.14 0.11 0.76 2 Riffles pools located 0.33 -0.02 0.73 4 Riffles clean material 0.02 0.18 0.62 12 Rootwads 0.02 0.38 -0.07 7 Large woody debris -0.13 0.35 0.14 5 Pools length depth 0.24 0.02 0.2 13 Boulder clusters 0.05 -0.04 0.13
Proportion Var 19% 15% 15% Cumulative Var 19% 35% 50%
Factor Scores with
Varimax Rotation Note: Varimax maximizes the sum of the variances of the squared loadings
Factor 1 – General Morphologic Condition
# Variable F1 15 Streambank condition 0.85 17 Floodplain function 0.78 16 Streambank vegetation 0.77 14 Sediment transport 0.72 6 Pattern 0.64
Conclusion: General morphologic condition of restored streams is the same as reference streams and different from impaired streams.
Streambank Condition & Vegetation
Floodplain Connection
Channel Pattern
Sediment Transport
Factor 2 - Habitat
# Variable F2 10 Rootmats 0.82 11 Overhanging veg 0.74 8 Leaf packets 0.71 9 Undercut banks 0.68
Conclusion: There is a high range of variability in habitat for recently restored streams
Stable Undercut Banks
Rootmats
In-Stream Habitat
Leaf Packs
Factor 3 - Bedform
# Variable F3 3 Riffles length slope 0.86 1 Riffles pools alternating 0.76 2 Riffles pools located 0.73 4 Riffles clean material 0.62
Conclusion: There is a high range of variability in bedform for recently restored streams
Channel Bedform
• Riffles • Steps • Pools
Conclusion 3 Restored streams are similar to reference streams in
terms of geomorphic conditions, but for bedform and habitat conditions, restored streams have a lower mean score and greater variability.
Obj. 3 – Develop a “scale” for evaluating restoration need and
performance SPA (NCSU) 130 streams: 84 restored (benthic sampling), 21 impaired
and 25 reference quality Watershed Assessment
Method: PCR, least squares and ridge regression used to predict EPT taxa (for 84 restored streams). Cross-Validation for prediction error. Use the “best” regression model to predict outcomes for reference and impaired streams.
Cross Validation indicates that Ridge Regression results in the lowest prediction error. Also, the cross-validation score is the lowest for PCR if 14 PCs are retained.
Prediction Error for 3 Regression Methods (n=84 streams)
Apply Ridge
Model to 130
Streams to predict
Total No. Dominant
EPT Values
n=21 n=25 n=84
Impaired Reference Restored
Predicted No. Dominant EPT Taxa – Ridge Regression Model
n=10 n=11 n=5 n=20 n=31 n=53
Y Intercept 4.33 BS Basin Slope 1.38 %D % Developed -1.26 BC Boulder clusters 0.88 CN CN -0.75 UB Stable Undercut banks 0.65 RM Rootmats 0.46 Pool Pools length depth -0.40 R-P Riffles pools alternating 0.39 SV Streambank vegetation -0.31 PT Pattern -0.28 R Riffles clean material 0.26
OV Overhanging veg -0.25 LP Leaf packets -0.24 RW Rootwads -0.24 LWD Large woody debris 0.21 FF Floodplain function -0.20 RPL Riffles pools located 0.18 ST Sediment transport 0.10 % % Impervious -0.10 RIF Riffles length slope 0.08 SC Streambank condition -0.07 Size Watershed Size 0.04 Tc Time of Concentration 0.04
Dominant EPT Taxa = 4.33 +1.38 *BS-1.26* %D + 0.88* BC =0.75*CN+ 0.65 UB +0.46*RM-0.44 Pool + 0.39*R-P -0.31*SV + etc….
Ridge Regression Equation
A scale for evaluating the “potential” uplift for stream restoration projects can be developed from sampling biologic communities and assessing habitat and watershed in a range of stream conditions and applying ordination and regression statistics.
Note: Macroinvertebrates are not an appropriate metric for urban streams
Conclusion 4
Obj. 4 – Determine if location, site selection and design relate to the resulting eco-geomorphological
condition of restored streams
79 restored streams -benthic macroinvertebrates & watershed assessment
Method: Use PCA, PCR and PC-based factor analysis to determine which factors correlate with benthic metrics. Use Ridge Regression to predict dominant EPT taxa.
Potential explanatory variables for restoration performance and biotic indices
Watershed
• % Impervious % Developed • Runoff Curve Number Basin Slope • Time of Concentration Watershed Size
Landscape
• Ecoregion • Valley Slope • Substrate (D50, D84, % Sand)
Design
• Bankfull Width Bankfull Mean Depth • Width/Depth Ratio Average Channel Slope • Sinuosity Bankfull Cross-Sectional Area • Entrenchment Ratio, ER
Ridge Regression Model Results Morphology +
Watershed
Conclusion: Morphology + Watershed factors explain a substantial amount of variability in EPT taxa.
Dominant EPT Taxa = 4.44 + 1.14*Sval – 1.44*%Dev. +1*Dbkf – 1.28*%Sand+ 0.91*ER- 1.06*CN+ 0.86*BS - etc….
Ridge Regression Equation – 17 Variables
Positive Negative Sval 1.14 % Developed -1.44 Dbkf 1.00 % Sand -1.28 ER 0.91 CN -1.06 Basin slope 0.86 Watershed Size -1.02 D84 0.62 Save -0.49 Wbkf 0.50 K -0.48 % Impervious 0.47 D50 -0.18 Tc 0.17 Abkf -0.16 [W/D] -0.12
Dominant EPT Taxa = 4.44 + 1.14*Sval – 1.44*%Dev. +1*Dbkf – 1.28*%Sand+ 0.91*ER- 1.06*CN+ 0.86*BS - etc….
Ridge Regression Equation After Variable Elimination
Positive Negative Basin Slope 1.66 CN -1.42 ER 1.02 K -0.46 D50 0.81 [W/D] -0.42 Svalley 0.74 Tc -0.05 Wbkf 0.74
Conclusion 1: Larger (wider) streams in steeper valleys with course substrate with un-developed watersheds have more EPT taxa
Dominant EPT Taxa = 4.44 + 1.14*Sval – 1.44*%Dev. +1*Dbkf – 1.28*%Sand+ 0.91*ER- 1.06*CN+ 0.86*BS - etc….
Ridge Regression Equation
Positive Negative Basin Slope 1.66 CN -1.42 ER 1.02 K -0.46 D50 0.81 [W/D] -0.42 Svalley 0.74 Tc -0.05 Wbkf 0.74 Conclusion 2: Wider floodplain widths indicate higher EPT taxa numbers.
Ridge Regression Model Results Morphology + Watershed after
variable elimination
Conclusion: Eliminating factors results in a reduction in the variability explained in the EPT taxa
Conclusion 5 Larger (wider) streams in steeper valleys with larger
substrate and un-developed watersheds have higher numbers of dominant EPT taxa
Conclusion 6 Larger accessible floodplain widths (higher ER
values) correlate with higher EPT taxa values.
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