1. Flow Metrics 9/08/2011 1 Flow RangeMagnitudeDurationFrequencyOther HighMean high flow volume (MH21) High flow duration (DH17) High pulse count, High flow freq., Flood frequency (FH9) Skewness in annual max flow (MH19) MidMedian annual QFlood Free Season Daily flow fall rate (RA3), Flashiness (RBI) Low4-day harmonic mean low flow (4b3), seasonal Q85 Low pulse duration, Extreme low duration, Coeff. of var. in low flow pulse duration (DL17) Low pulse count, Extreme low freq. Components of flow regime and flow metrics

2. Biometric selection 9/08/2011 2 Index of Biotic Integrity Taxonomic Composition Pollution Tolerance Functional Feeding Group Functional Habit Group Reference Degraded Score Index of Biotic Integrity A composite index tends to discriminate better than its component biometrics between reference and degraded environments Reference Degraded Value Biometric An index combines diverse metrics representing key aspects of aquatic community structure and function

3. Biometric selection 9/08/2011 3

4. From Analysis Results to Flow Hypotheses 4 9/08/2011 “Reference” – Observed “Baseline” - Modeled 105 Potomac-Susquehanna gaged watersheds 700+ delineated watersheds EXAMPLE 1) High Pulse Count “Reference” and “Baseline” are comparable when plotted against watershed size, mean slope and %karst Builds confidence in model Demonstrates no relationships to watershed size, mean slope or %karst, so there is no need to classify stream type for this flow metric

5. From Analysis Results to Flow Hypotheses 5 9/08/2011 Observed Data Modeled Current Data 105 Potomac-Susquehanna watersheds 740+ delineated watersheds 2) %Alteration of High Pulse Count in Observed data and Modeled output show similar responses to change in anthropogenic stress Builds confidence in model As %Forest decreases, indicating increasing anthropogenic activity, high pulse count diverges from “Reference” (observed data) and “Baseline” (modeled data) values

6. 6 9/08/2011 3) Biometrics respond to High Pulse Count From Analysis Results to Flow Hypotheses %Tolerant Organisms Family-Level Taxa Richness Chessie BIBI

7. 7 9/08/2011 Kennen et al. (2009) Determining hydrologic factors that influence stream macroinvertebrate assemblages in the northeastern US Ecohydrol. DOI:10.1002/eco.00 From Analysis Results to Flow Hypotheses Average EPA tolerance values based on richness (RichTOL) % of total richness as non-chironomid dipterans and non- insects % richness of EPT taxa % of total richness as non-insect taxa 4) High Pulse Count is a metric much like High Flow Frequency (FH9) Biometrics respond similarly to FH9

8. 9/08/20118 From Analysis Results to Flow Hypotheses 5) Of the different types of land and water uses, %Impervious shows the strongest relationship to alteration in high pulse count.

9. 9 9/08/2011 From Analysis Results to Flow Hypotheses 6) Project results support the general hypotheses that: Macroinvertebrate community composition deteriorates with increasing frequency (count) of high flow events Alteration in high pulse count is most strongly related to a watershed’s %Impervious surface area

10. 10 9/08/2011 From Analysis Results to Flow Hypotheses Biometric flow-response curve