Assessing Morphological Performance of Stream Restoration in North Carolina

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1 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

2 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

3 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)

4 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

5 High-quality reference streams serve as design templates

6 Determine Restoration Potential Restored Streams Reference Reaches Research Goal: Develop tools for measuring functional uplift to advance the practice of stream restoration. Performance Range Disturbed Channels

7 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

8 Scope of the Project Visited 156 streams between 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

9 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)

10 Eco-Geomorphologial Assessment EGA B. Bank and Riparian Habitat D. Condition and Function of Structures C. Aquatic Insects A. Channel Condition

11 Evaluation Categories Channel Condition Riparian Habitat Macro invertebrates In-stream Structures Sub-Categories # of variables Points Bedform Condition Dominant Substrate Material 3 12 Streambank Stability 6 24 Riparian Vegetation 5 20 Floodplain Condition 6 24 Community Structure 5 24 Cover and Refuge Structure Function 4 16 Structure Condition 3 12 Total Score

12 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

13 Five stream assessment methods applied at 65 restored streams EGA, SPA, RBP, RCE & SVAP

14 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.

15 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

16 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. Dominant Taxa EPT Taxa EPT Abundance % Shredders & Predators Indicator Taxa DIC R 2 p R 2 p R 2 p R 2 p R 2 p R 2 p EGA 0.24 *** 0.29 *** 0.23 *** ns 0.26 *** ns SPA * * 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

17 Hypotheses: Prediction can be improved by 1) addressing collinearity and subjective variable weights and by 2) adding watershed factors.

18 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

19 R-squared from Linear Regression Dominant Taxa EPT Taxa EPT Abundance % Shredders & Predators Indicator No. of Taxa Variables Total No. of PC's % Variability Explained EGA Total Raw Points PCA EGA % SPA Total Raw Points PCA SPA % RBP Total Raw Points PCA RBP % RCE Total Raw Points PCA RCE % SVAP Total Raw Points PCA SVAP %

20 Watershed Assessment using GIS

21 EPT taxa vs. Impervious Cover % EPT taxa vs. Developed % EPT taxa vs. CN EPT taxa vs. Time of Concentration EPT taxa vs. Watershed Size EPT taxa vs. Basin Slope y = 2.54ln(x) R² =

22 V1= Basin Slope V2=Time of Concentration V3=Watershed Size V4 = Curve Number V5=% Developed V6=% Impervious

23 First three PC s for Watershed (90% Variance Explained)

24 R-squared from Linear Regression Dominant Taxa EPT Taxa EPT Abundance % Shredders & Predators Indicator No. of Taxa Variables Total No. of PC's % Variability Explained EGA Total Raw Points PCA EGA % PCA (EGA +Watershed) % SPA Total Raw Points PCA SPA % PCA (SPA + Watershed) % RBP Total Raw Points PCA RBP % PCA (RBP + Watershed) % RCE Total Raw Points PCA RCE % PCA (RCE + Watershed) % SVAP Total Raw Points PCA SVAP % PCA (SVAP + Watershed) % Watershed PCA Watershed %

25 Conclusion 1 Rapid stream assessments ability to predict aquatic macroinvertebrate metrics in restored streams can be improved with ordination and addition of watershed variables.

26 Conclusion 2 Rapid stream assessments best predict EPT, indicator and total number taxa metrics. Artwork by Ethan Nedeau

27 Obj. 2 Compare ecogeomorphological 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

28 Stream Locations

29 First 3 SPA PC s explain 57.5 % of variance n=156

30 Factor Scores with Varimax Rotation Note: Varimax maximizes the sum of the variances of the squared loadings # Variable F1 F2 F3 15 Streambank condition Floodplain function Streambank vegetation Sediment transport Pattern Rootmats Overhanging veg Leaf packets Undercut banks Riffles length slope Riffles pools alternating Riffles pools located Riffles clean material Rootwads Large woody debris Pools length depth Boulder clusters Proportion Var 19% 15% 15% Cumulative Var 19% 35% 50%

31 Factor 1 General Morphologic Condition Conclusion: General morphologic condition of restored streams is the same as reference streams and different from impaired streams. # Variable F1 15 Streambank condition Floodplain function Streambank vegetation Sediment transport Pattern 0.64

32 Streambank Condition & Vegetation

33 Floodplain Connection Channel Pattern

34 Sediment Transport

35 Factor 2 - Habitat Conclusion: There is a high range of variability in habitat for recently restored streams # Variable F2 10 Rootmats Overhanging veg Leaf packets Undercut banks 0.68

36 In-Stream Habitat Stable Undercut Banks Leaf Packs Rootmats

37 Factor 3 - Bedform Conclusion: There is a high range of variability in bedform for recently restored streams # Variable F3 3 Riffles length slope Riffles pools alternating Riffles pools located Riffles clean material 0.62

38 Channel Bedform Riffles Steps Pools

39 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.

40 Obj. 3 Develop a scale for evaluating restoration need and SPA (NCSU) performance 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.

41 Prediction Error for 3 Regression Methods (n=84 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.

42 Apply Ridge Model to 130 Streams to predict Total No. Dominant EPT Values Impaired Reference Restored n=21 n=25 n=84

43 Predicted No. Dominant EPT Taxa Ridge Regression Model n=11 n=10 n=5 n=20 n=31 n=53

44 Ridge Regression Equation Dominant EPT Taxa = *BS-1.26* %D * BC =0.75*CN UB +0.46*RM-0.44 Pool *R-P -0.31*SV + etc. Y Intercept 4.33 BS Basin Slope 1.38 %D % Developed BC Boulder clusters 0.88 CN CN UB Stable Undercut banks 0.65 RM Rootmats 0.46 Pool Pools length depth R-P Riffles pools alternating 0.39 SV Streambank vegetation PT Pattern R Riffles clean material 0.26 OV Overhanging veg LP Leaf packets RW Rootwads LWD Large woody debris 0.21 FF Floodplain function RPL Riffles pools located 0.18 ST Sediment transport 0.10 % % Impervious RIF Riffles length slope 0.08 SC Streambank condition Size Watershed Size 0.04 Tc Time of Concentration 0.04

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46 Conclusion 4 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

47 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.

48 Potential explanatory variables for restoration performance and biotic indices Watershed Landscape % Impervious Runoff Curve Number Time of Concentration Ecoregion Valley Slope Substrate (D50, D84, % Sand) % Developed Basin Slope Watershed Size Design Bankfull Width Bankfull Mean Depth Width/Depth Ratio Average Channel Slope Sinuosity Bankfull Cross-Sectional Area Entrenchment Ratio, ER

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50 Ridge Regression Model Results Morphology + Watershed Conclusion: Morphology + Watershed factors explain a substantial amount of variability in EPT taxa.

51 Ridge Regression Equation 17 Variables Dominant EPT Taxa = *Sval 1.44*%Dev. +1*Dbkf 1.28*%Sand+ 0.91*ER- 1.06*CN+ 0.86*BS - etc. Positive Negative S val 1.14 % Developed D bkf 1.00 % Sand ER 0.91 CN Basin slope 0.86 Watershed Size D S ave W bkf 0.50 K % Impervious 0.47 D T c 0.17 A bkf [W/D] -0.12

52 Ridge Regression Equation After Variable Elimination Dominant EPT Taxa = *Sval 1.44*%Dev. +1*Dbkf 1.28*%Sand+ 0.91*ER- 1.06*CN+ 0.86*BS - etc. Positive Negative Basin Slope 1.66 CN ER 1.02 K D [W/D] S valley 0.74 T c W bkf 0.74 Conclusion 1: Larger (wider) streams in steeper valleys with course substrate with un-developed watersheds have more EPT taxa

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54 Ridge Regression Equation Dominant EPT Taxa = *Sval 1.44*%Dev. +1*Dbkf 1.28*%Sand+ 0.91*ER- 1.06*CN+ 0.86*BS - etc. Positive Negative Basin Slope 1.66 CN ER 1.02 K D [W/D] S valley 0.74 T c W bkf 0.74 Conclusion 2: Wider floodplain widths indicate higher EPT taxa numbers.

55 Ridge Regression Model Results Morphology + Watershed after variable elimination Conclusion: Eliminating factors results in a reduction in the variability explained in the EPT taxa

56 Conclusion 5 Larger (wider) streams in steeper valleys with larger substrate and un-developed watersheds have higher numbers of dominant EPT taxa

57 Conclusion 6 Larger accessible floodplain widths (higher ER values) correlate with higher EPT taxa values.