EFSAB_Final_Report_to_NCDENR

Recommendations for
Estimating Flows to
Maintain Ecological Integrity in
Streams and Rivers in North Carolina

Submitted to the
North Carolina Department of
Environment and Natural Resources
by the
North Carolina Ecological Flows
Science Advisory Board

November 2013
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DEDICATION
The Ecological Flows Science
Advisory Board dedicates this
report to the memory of Steve
Reed in appreciation of his
many contributions to the
state of North Carolina
through his work at the
Division of Water Resources.
For more than three decades,
Steve led efforts to establish
ecological flows in North
Carolina, contributing to the
science and practice, while
leaving a deep impression on
all he met with his kindness
and professionalism. Steve
died suddenly in July, 2012
before the completion of this
effort, but his spirit continued
to inspire the work behind this
report and will inspire the
future work of others.

Photo courtesy of Mark Cantrell

Cover photos courtesy of Vann Stancil

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TABLE OF CONTENTS
EXECUTIVE SUMMARY ................................................................................................................................. iv
GLOSSARY..................................................................................................................................................... vi
ACRONYMS .................................................................................................................................................. ix
1 PREFACE.................................................................................................................................................. 1
1.1 The Department (DENR) and Division (DWR) ............................................................................ 1
1.2 Legislative Background .............................................................................................................. 1
1.3 DENR’s Actions Establishing the EFSAB ..................................................................................... 2
1.4 Activities of the EFSAB ............................................................................................................... 2
2 STATE OF ECOLOGICAL FLOW SCIENCE .................................................................................................. 3
2.1 Characterization of the Ecology of North Carolina Rivers ......................................................... 3
2.1.1 Mountain Streams .............................................................................................................. 3
2.1.2 Piedmont Streams .............................................................................................................. 4
2.1.3 Coastal Plain Streams .......................................................................................................... 4
2.1.4 Headwater Streams ............................................................................................................ 5
2.2 The Importance of Flow in Riverine Systems ............................................................................ 6
2.3 Strategies to Determine Ecological Flows ................................................................................. 7
2.4 Advancing the Science of Ecological Flows ............................................................................... 8
2.4.1 Flow-habitat Relationships ................................................................................................. 9
2.4.2 Flow-ecology Relationships .............................................................................................. 15
2.4.3 Attempts at Stream Classification .................................................................................... 16
3 RECOMMENDATIONS OF THE EFSAB ................................................................................................... 18
3.1 Statewide Ecological Flow Evaluation ..................................................................................... 18
3.1.1 Percentage of Flow Strategy. ............................................................................................ 18
3.1.2 Biological Response Strategy ............................................................................................ 22
3.2 Exceptions to Statewide Recommendation ............................................................................ 25
3.2.1 Headwater Streams .......................................................................................................... 25
3.2.2 Coastal Streams ................................................................................................................ 25
3.3 Additional Recommendations ................................................................................................. 27
3.3.1 Threatened and Endangered Species ............................................................................... 27
3.3.2 Ongoing Validation Using an Adaptive Management Approach ...................................... 27
REFERENCES ................................................................................................................................................ 29
APPENDIX A – Session Law 2010-143 ....................................................................................................... A-1
APPENDIX B – NC Ecological Flows Science Advisory Board Members and Other Contributors ............... B-1
APPENDIX C – Recommendations for Establishing Ecological Flows in Coastal Waterways ..................... C-1
APPENDIX D – Flow Alteration – Biological Response Relationships to Support the Determination of
Ecological Flows in North Carolina ..................................................................................................... D-1
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EXECUTIVE SUMMARY
The Ecological Flows Science Advisory Board (EFSAB) was created to assist the Department of
Environment and Natural Resources (DENR) with developing a scientifically defensible
approach to establishing flows that protect the ecological integrity of streams and rivers in North
Carolina as required under Session Law 2010-143. The EFSAB was tasked with reviewing
published and unpublished studies that characterize the ecology of North Carolina rivers,
relating ecological conditions to flow alteration, and identifying a scientifically defensible
approach to establishing flow requirements for the maintenance of ecological integrity. Per
Session Law 2010‒143, the EFSAB included representatives from the state (the N.C. Division
of Water Resources, the N.C. Division of Water Quality, the N.C. Wildlife Resources
Commission, the N.C. Division of Marine Fisheries, and the N.C. Natural Heritage Program), the
U.S. Fish and Wildlife Service, the National Marine Fisheries Service, and individuals with
expertise in aquatic ecology and habitats from organizations representing agriculture, forestry,
manufacturing, electric public utilities, non-governmental organizations, local governments, and
other individuals and organizations.
The EFSAB elicited input from a wide variety of state, federal, and local agencies as well as
academic and non-governmental organizations in reviewing existing strategies for determining
ecological flows and in conducting new analyses within the State. The EFSAB focused on
reviewing ecological flow literature, hearing and discussing presentations from ecological flow
experts, and reviewing flow-ecology research conducted in North Carolina. In addition, the
EFSAB worked through informal subcommittees to analyze flow-ecology relations using data
(fish and benthic macroinvertebrate) specific to North Carolina streams and to address flow and
ecology issues unique to streams in the coastal plain. The work of these subcommittees helped
the EFSAB make recommendations that were more specific to North Carolina streams than was
possible by relying solely on recommendations obtained from literature review.
Based on the review of existing work and the detailed analyses conducted by the
subcommittees, the EFSAB recommends that DENR use a two-part strategy to establish
ecological flows, determine if future conditions support these flows, and assess whether
additional review and studies are warranted:
1. Percentage-of-flow strategy: establish ecological flows on the basis of 80‒90% flow-by
(i.e., 80‒90% of ambient modeled flow remains in the stream) in combination with a
critical low-flow component that identifies when additional actions may be needed to
protect ecological integrity. The critical low-flow component is intended to minimize
increases in the magnitude and duration of extreme low flows during drought conditions.
If the basinwide hydrologic models and critical low-flow component indicate that there is
not sufficient water available to meet essential water uses and ecological flows at a
given location, further review by DENR is recommended. This strategy of establishing
ecological flows is similar to approaches used by other states and countries. The
EFSAB has not recommended a specific value for the low-flow component, but
recommends that DENR establish these values based on an analysis of typical and
extreme low-flow conditions in North Carolina.
2. Biological-response strategy: evaluate the effects of ecological flows using models
that relate changes in fish and invertebrate communities to current and future flows
derived from the percentage-of-flow strategy. The biological-response strategy directly
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links the statewide fish and invertebrate data collected by DENR with flow data derived
from basinwide hydrologic models to predict biological changes. The EFSAB
recommends that DENR use a 5‒10% reduction in biological condition—using (A)
Shannon-Weaver Diversity Index for fish and (B) number of taxa in the orders
Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies) for
invertebrates—as a threshold for initiating further review by DENR.
The EFSAB recognizes that the science underlying these recommendations will evolve as more
research addresses flow-ecology issues at additional spatial and temporal scales. The EFSAB
understands that the hydrologic and biologic models on which our recommendations are based
may need to be revised as changes in climate and land cover alter patterns in precipitation,
temperature (air and water), and runoff across the state. The flow and biological criteria
recommended by the EFSAB will need to be reevaluated to determine their efficacy throughout
the state and through time. Gaps in available hydrologic and biologic data (headwater, coastal
plain and large rivers) will need to be addressed in order to provide a more complete
representation of flow effects on biological integrity within the state. Consequently, the EFSAB
recommends that DENR take an adaptive management approach to establishing flows that
protect the ecological integrity of North Carolina streams. This approach should address the
following issues:
1. Collect additional hydrologic and biologic data in headwater, coastal plain and large
rivers that are currently underrepresented in DENR datasets. These data will help
determine if these streams fit into current models and assumptions.
2. Adopt, design, and develop strategies to:
a. Validate the efficacy of ecological thresholds and adjust as necessary. Validation
should be informed by new data and/or research.
b. Track the impact of flow changes when and where they occur.
c. Modify characterizations, target flows, and thresholds based on new data,
changing conditions (e.g., land cover, precipitation, hydrology) and lessons
learned.
d. Georeference nodes in each hydrologic model to facilitate analysis.
The recommendations of the EFSAB represent a starting point for developing ecological flows
that protect the integrity of North Carolina streams. By adopting an adaptive management
approach, DENR can ensure that ecological integrity is protected through the refinement and
improvement of the recommendations of the EFSAB over time.

vi
GLOSSARY
7Q10 – the lowest average flow that occurs for seven consecutive days with a recurrence
probability of once every 10 years. The 7Q10 value is typically used for determining
assimilative capacity for receiving streams when permitting wastewater discharges. Flows
equal to the 7Q10 generally occur during drought.
Annual 30-day Minimum Flow – the lowest 30-day mean flow calculated as a moving average
for every 30-day period that is completely within the water year.
BEC – Biological Environmental Classification analysis performed by RTI International.
CHEOPS (Computer Hydro-Electric Operations and Planning Software) – simulation
package developed by HDR, Inc. to evaluate the costs and benefits associated with a wide
range of changes to a hydropower system. CHEOPS was developed for and is currently used
to model flows in the Catawba River.
Condition Class – a classification system in which sampling sites are divided into classes on
the basis of an ecological attribute or collection of attributes. Classes are ordered by the
amount of change in the ecological attribute that occurs along a disturbance gradient that
ranges from undisturbed (Excellent condition class) to highly disturbed condition (Poor condition
class).
cfs – cubic feet per second (1 cfs = 0.646 million gallons per day).
ecological deficit (ecodeficit) – the total difference between the altered and unaltered flow
duration curves, whenever the altered curve falls below the unaltered curve.
ecological flow – “stream flow necessary to protect ecological integrity” [as defined by General
Statute 143-355(o)(1)].
ecological integrity – “the ability of an aquatic system to support and maintain a balanced,
integrated, adaptive community of organisms having a species composition, diversity, and
functional organization comparable to prevailing ecological conditions and, when subject to
disruption, to recover and continue to provide the natural goods and services that normally
accrue from the system” [as defined by General Statute 143‒355(o)(1)].
ELOHA (Ecological Limits of Hydrologic Alteration) – a step-wise flow determination
process that includes establishing a hydrologic foundation, classifying rivers, and determining
flow-ecology relationships before entering a social process to develop flow regime standards
(see Poff et al. 2009).
EPT – the insect orders Ephemeroptera (mayflies), Plecoptera (stoneflies) and Trichoptera
(caddisflies).
EPTr (EPT richness) – total number of taxa collected at a site from the insect orders
Ephemeroptera (mayflies), Plecoptera (stoneflies) and Trichoptera (caddisflies).
flow duration curve – a cumulative curve that shows the percent of time specified discharges
were equaled or exceeded during a given period.
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guild – a grouping of species, or species life stages, based on similar habitat requirements.
Index B – habitat metric used during time series analysis, a component of PHABSIM (see
below), calculated by averaging all weighted usable area (see WUA) habitat values between the
10th and 90th percentiles on a monthly or seasonal basis for a flow record. Used to compare
different flow regimes.
Invertebrate – animals that lack a backbone such as insects, worms, mussels, clams, snails,
and crayfish.
Monthly median – In general hydrologic terms the middle flow value for the rank ordered flows
for all years of a month (i.e. the 50th percentile).
mgd – million gallons per day (1 mgd = 1.547 cubic feet per second).
NWIS (National Water Information System) – US Geological Survey (USGS) and US
Environmental Protection Agency (EPA) work together to provide scientists and policy-makers
an easier way to discover and acquire water quality data from their large water quality
databases and share water monitoring data via a common format and terminology. A Water
Quality Portal is available at www.waterqualitydata.us for downloading monitoring location
information and associated water quality results that are automatically linked and integrated
from both USGS and US EPA databases.
OASIS (Operational Analysis and Simulation of Integrated Systems) – generalized program
for modeling the operations of water resources systems, developed by HydroLogics, Inc., that
routes water through a system represented by nodes and arcs. OASIS is the hydrologic model
currently used by the Division of Water Resources to model flow and water use in the major
river basins.
ORW (Outstanding Resource Water) – a supplemental water quality classification designated
by the EMC for special or unique surface waters in NC having excellent water quality and being
of exceptional state or national ecological or recreational significance.
p-value – a measure of the probability that an observed value (e.g., slope or intercept in a
regression) is the result of random chance. Low p-values (< 0.05) are generally considered to
be statistically significant.
PHABSIM (Physical Habitat Simulation) – a specific model designed to calculate an index to
the amount of microhabitat available for different organisms and life stages at different flow
levels, incorporating two major analytical components: stream hydraulics and organism/life
stage-specific habitat requirements.
PNV (potential natural vegetation) – the types of vegetation that would exist under most
favorable conditions in the conterminous United States as proposed by A.W. Kuchler (1964).
Prevailing ecological conditions – “the ecological conditions determined by reference to the
applicable period of record of the United States Geological Survey stream gauge data, including
data reflecting the ecological conditions that exist after the construction and operation of existing
flow modification devices, such as dams, but excluding data collected when stream flow is
temporarily affected by in-stream construction activity” [from General Statute 143‒355(o)(1)].
viii
Q – volumetric flow rate, typically expressed in cubic feet per second.
Quantile Regression – a type of regression analysis that estimates either the conditional
median or other quantiles (e.g., 80th or 90th) of the response variable. Quantile regression has
been used in ecological studies to uncover predictive relationships between variables where the
relationship is weak or obscured by other variables.
SE (standard error) – a statistical term that measures how accurately a sample represents the
underlying population. The smaller the standard error, the more representative the sample is of
the population.
September Median – the monthly median flow for September (see monthly median). The
September median flow has sometimes been used as a minimum flow in the Southeast
because September is typically the month with the lowest monthly median flow.
Shannon-Weaver Diversity Index – a quantitative measure that reflects how many different
types (such as species) there are in a dataset, and simultaneously takes into account how
evenly the basic entities (such as individuals) are distributed among those types.
Tennant method – a hydrologic standard setting approach for minimum flows based on
percentage of mean annual flow that varies by month.
Trimmed hydrology dataset – a hydrology dataset that excludes a designated percentage of
data at the upper and/or lower ends of the cumulative frequency distribution. For example, a
dataset that excludes the highest and lowest 10% contains the central 80% of the data.
WaterFALL™ (Watershed Flow and ALLocation) – watershed modeling tool developed by
the RTI International using National Hydrography Dataset (NHDPlus) hydrologic catchments to
investigate water availability and allocation at multiple geographic scales.
WUA (weighted usable area) – an amount of habitat determined by PHABSIM, often
represented as square feet of habitat per thousand feet of stream (see PHABSIM).

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ACRONYMS
APNEP – The Albemarle Pamlico National Estuary Partnership
AWWA – American Water Works Association
BEC – Biological Environmental Classification (see glossary for definition)
CEFWG – Coastal Ecological Flows Working Group
CFS – cubic feet per second
CHEOPS – Computer Hydro-Electric Operations and Planning Software (see glossary for
definition)
CHPP – Coastal Habitat Protection Plan
DENR – Department of Environment and Natural Resources
DMF – Division of Marine Fisheries
DO – dissolved oxygen
DWQ – Division of Water Quality (as of 2013, merged with DWR)
DWR – Division of Water Resources
ECU – East Carolina University
EDF – Environmental Defense Fund
EEP – Ecosystem Enhancement Program
EFS – Environmental Flow Specialists, Inc.
EFSAB – Ecological Flows Science Advisory Board
ELOHA – Ecological Limits of Hydrologic Alteration (see glossary for definition)
EMC – Environmental Management Commission
EPT – insect orders of Ephemeroptera (mayflies), Plecoptera (stoneflies) and Trichoptera
(caddisflies) (see glossary for definition)
ERC – Environmental Review Commission
GIS – Geographic Information System
G.S. – General Statute
MAF – mean annual flow
MFC – Marine Fisheries Commission
x
MGD – million gallons per day
NCDA&CS – North Carolina Department of Agricultural and Consumer Services
NCFA – North Carolina Forestry Association
NCFS – North Carolina Forest Service
NHD+ – National Hydrography Dataset Plus
NHP – Natural Heritage Program
NMFS – National Marine Fisheries Service
NOAA – National Oceanic and Atmospheric Administration
NWIS – National Water Information System (see glossary for definition)
OASIS – Operational Analysis and Simulation of Integrated Systems (see glossary for
definition)
ORW – Outstanding Resource Waters (see glossary for definition)
PHABSIM – Physical Habitat Simulation (see glossary for definition)
RTI – Research Triangle Institute
SALCC – South Atlantic Landscape Conservation Cooperative
SARP – Southeastern Aquatic Resources Partnership
SAS – Statistical Analysis System
SE – standard error
SG – Sea Grant Program
TNC – The Nature Conservancy
UNC IMS – University of North Carolina Institute of Marine Sciences
USFWS – United States Fish and Wildlife Service
USGS – United States Geological Survey
WaterFALL™ – Watershed Flow and ALLocation (see glossary for definition)
WRC – North Carolina Wildlife Resources Commission
WRRI – Water Resources Research Institute
WUA – weighted usable area (see glossary for definition)

1
1 PREFACE
1.1 The Department (DENR) and Division (DWR)
The North Carolina Department of Environment and Natural Resources (DENR or the
Department) is the lead stewardship agency for the preservation and protection of North
Carolina's natural resources. The organization administers regulatory programs designed to
protect air quality, water quality, and the public's health. The Department, through its Division of
Water Resources (DWR), specifically administers water resource planning for the state and has
been tasked with convening and providing staff support to the Ecological Flows Science
Advisory Board (EFSAB).
1.2 Legislative Background
Session Law 2010-143 (Appendix A) amended portions of General Statute (G.S.) 143, Article 38
which deals with water resources. Specifically, the Session Law added language to G.S. 143-
355 requiring DENR to develop basinwide hydrologic models for each of the 17 major river
basins in North Carolina to simulate flows for determining if adequate water is available in the
future to meet all needs, including essential water uses and ecological flows. Basinwide models
are considered a practical approach to water planning because site- and project-specific
evaluations require considerable time and money. However, the proposed planning method
used by DENR will not replace site-specific studies needed for a specific environmental
assessment or permit review. This proposed method will not vary existing permits/licenses or
impose additional regulatory requirements on current permittees related to water quality and
water quantity. Per the statute, DENR is required to provide status reports to the N.C.
Environmental Review Commission (ERC) on the development of basinwide hydrologic models
no later than November 1 of each year, beginning in 2011.
The Session Law defines ecological flow as “the stream flow necessary to protect ecological
integrity.” Ecological integrity is defined as “the ability of an aquatic system to support and
maintain a balanced, integrated, adaptive community of organisms having a species
composition, diversity, and functional organization comparable to prevailing ecological
conditions and, when subject to disruption, to recover and continue to provide the natural goods
and services that normally accrue from the system.” The statute directs DENR to “characterize
the ecology in the different river basins and identify the flow necessary to maintain ecological
integrity.”
Session Law 2010‒143 directs DENR to “create a Science Advisory Board to assist the
Department in characterizing the natural ecology and identifying the flow requirements.” The
statute directs DENR to ask the EFSAB “to review any report or study submitted to the
Department for consideration that is relevant to characterizing the ecology of the different river
basins and identifying flow requirements for maintenance of ecological integrity.” Per Session
Law 2010‒143, the EFSAB shall include representatives from: the N.C. Division of Water
Resources (DWR); the N.C. Division of Water Quality (DWQ); the N.C. Wildlife Resources
Commission (WRC); the N.C. Division of Marine Fisheries (DMF); and the N.C. Natural Heritage
Program (NHP). The statute also directs DENR to invite participation by: the U.S. Fish and
Wildlife Service (USFWS); National Marine Fisheries Service (NMFS); and individuals with
expertise in aquatic ecology and habitat from organizations representing: agriculture; forestry;
2
manufacturing; electric public utilities; and local governments; and other individuals or
organizations with expertise in aquatic ecology and habitat.
While the role of the EFSAB appeared rather clear from the statutory language, it was subject to
early discussion and interpretation by the members. After preliminary discussions and review of
the statute, the EFSAB agreed any recommendations regarding ecological flows would be made
for the purpose of water resource planning, not water-use permitting. The EFSAB also agreed
to recommend scientifically-based methods or approaches, and ecological flow requirements,
which may or may not be numerical. Although the EFSAB is charged with developing a
statewide approach, this approach does not substitute for site-specific evaluation when that is
needed and it does not prevent DENR from requesting a site-specific evaluation. The EFSAB
agreed to provide two primary deliverables: 1) characterization of aspects of the ecology in
different river basins relevant to ecological flows; and 2) identification of the flow regimes
necessary to maintain ecological integrity. The EFSAB is neither responsible for recommending
how DENR responds to a water-availability issue nor responsible for advising DENR on how to
use the EFSAB recommendations and research products.
1.3 DENR’s Actions Establishing the EFSAB
DENR, through DWR, extended invitations to 16 members of the North Carolina scientific and
technical community with expertise in aquatic ecology and habitat to serve on the EFSAB. The
EFSAB has a total of 16 primary members, as well as alternates, from the following agencies or
organizations: DWR, DWQ, WRC, NHP, DMF, Environmental Management Commission (EMC),
USFWS, NMFS, USGS, American Water Works Association (AWWA), the N.C. Department of
Agricultural and Consumer Services (NCDA&CS), the N.C. Forest Service (NCFS), utilities,
local governments, academic institutions, and environmental non-governmental organizations.
In addition, DWR contracted with the N.C. State University’s Natural Resources Leadership
Institute and program for Watershed Education for Communities and Local Officials to assist in
the development of the EFSAB charter, lead development and organization of the agenda for
each EFSAB meeting, facilitate the EFSAB meetings, produce written minutes for each meeting,
and assist with other process management tasks. DWR produced and hosts an ecological flow
website with pages defining ecological flow and discussing the importance of ecological flows to
North Carolina. The website documents the activities of the EFSAB, including presentations,
literature reviewed, meeting recordings and minutes.
1.4 Activities of the EFSAB
The EFSAB met 28 times with the first meeting convened November 8, 2010 and the last
meeting held October 23, 2013. During the initial meetings, the EFSAB established a
charter that included the purpose, goals, procedural rules, and responsibilities for the
EFSAB members, DWR and the facilitation team. Decision-making by the EFSAB was
based on consensus principles. The EFSAB used small-group break-out sessions,
brainstorming, open discussions, and trial balloon techniques to discuss and clarify topics,
capture individual member’s concerns, and put forth potential methods to achieve the
EFSAB’s ultimate goal of advising DENR on its charge of characterizing the ecology of the
river basins and identifying the flow regime necessary to maintain ecological integrity. To
address the latter charge, the EFSAB spent the majority of its time reviewing ecological
flow literature, hearing and discussing presentations from ecological flow experts, and
reviewing research conducted in North Carolina.
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2 STATE OF ECOLOGICAL FLOW SCIENCE
2.1 Characterization of the Ecology of North Carolina Rivers
Physical, biological, and chemical processes determine the presence, absence, abundance and
diversity of species, as well as habitat types present within streams. These processes, coupled
with hydrology, determine the ecology of streams. In general, the freshwater ecology of the
streams of North Carolina is characterized by intermittent and perennial flowing systems with
diverse aquatic fauna (depending in part on detrital energy derived from terrestrial plants at the
headwaters) and productive larger streams with resident diadromous fishes near the estuaries.
North Carolina streams show distinct seasonal patterns and variation in their flow.
The ecology of freshwater streams is as diverse as the landscapes across the state. Streams in
North Carolina vary from small tumbling mountain waterfalls and meandering blackwater
streams to large piedmont and coastal rivers. North Carolina streams include coldwater
communities, mostly in the higher elevations of the mountains where summer water
temperatures generally do not exceed 22 degrees Celsius (72°F). Coolwater streams occur in
lower elevations in the mountains and piedmont with maximum temperatures between 22-25
degrees Celsius (72-77F).
Warmwater streams are flowing waters with maximum water temperatures typically greater than
25°C (77°F). Outstanding Resource Waters (ORW) are designated in nearly all of the basins in
North Carolina and all of the physiographic regions. On the other end of the spectrum, there are
impaired streams in all of the basins and each of the physiographic regions. Specific
information relative to the streams and watersheds in the state can be obtained from DENR’s
basinwide plans.
2.1.1 Mountain Streams
The freshwater streams of the mountains are located within the Blue Ridge Physiographic
Province of the Appalachian mountains of western North Carolina. These watersheds are
characterized by forestland cover, extreme relief and high precipitation resulting in numerous
streams with permanently flowing, steep gradient channels, and well-oxygenated waters. These
streams provide habitats for diverse aquatic life and complex ecological functions. Much of the
land within the mountains is sparsely developed. Major stream systems draining the Blue Ridge
Province in North Carolina include the Little Tennessee, Hiwassee, French Broad/Nolichucky,
Watauga and New rivers (part of the Mississippi River drainage), and the Savannah, Broad,
Catawba, and Yadkin rivers (part of the Atlantic Ocean drainage).
Mountain streams typically have relatively steep gradient in many reaches. Most tributaries are
high gradient streams with cold water capable of supporting trout populations in the upper
reaches. Some tributaries and upper mainstems have cool water capable of supporting
communities characterized by smallmouth bass.
The aquatic communities typically found in mountain drainages are significantly different than
those found in drainages of the piedmont and coastal plain. Mountain streams host some of the
most diverse aquatic communities within North Carolina. They are home to a variety of rare
species, including crayfish, mussels, fish, aquatic insects, and amphibians. For example, the
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25-mile reach of Little Tennessee River between the town of Franklin and Fontana Lake has a
faunal diversity rivaling any in the state.
The hydrology of most streams and rivers in the mountains is relatively unaltered. However, a
number of hydropower projects significantly alter flows, and therefore stream ecology, by
operating in a peaking mode or diverting water around long stretches of stream channel. The
ecology of mountain streams is also influenced by numerous culverts and barriers that impede
the movements of aquatic organisms.
2.1.2 Piedmont Streams
Streams in the Piedmont Physiographic province of North Carolina flow toward the Atlantic
Ocean. Many piedmont streams originate in the mountains, eventually transitioning across the
Fall Line before reaching the coastal plain. Piedmont streams can be considered as
intermediate to the higher gradient, cooler streams of the mountains and the low gradient waters
of the coastal plain. In addition, piedmont streams typically lack the large boulders of mountain
streams yet have more substrate size diversity than the sand dominated streams of the coastal
plain. The majority of aquatic communities in piedmont streams are considered warmwater,
although some coolwater communities are present in the foothills portions of some basins.
Increasing nutrient enrichment, stormwater from urban areas, and wastewater are the primary
impacts to water quality in the eight basins of the piedmont. Most of these impacts are
associated with urban areas. Land conversion from forest and agricultural practices to
suburban uses is occurring throughout the piedmont, especially in the area known as the
“Piedmont Crescent” which extends from Charlotte to Greensboro to Raleigh, often resulting in
impacts to riparian habitats.
Nearly all of the large reservoirs in the state are found in the piedmont. Reservoirs have a
significant influence (positive and negative) on the hydrologic regime of the rivers downstream
of their dams and transform hundreds of miles of riverine habitat into lakes. This is particularly
true in the Catawba, Roanoke and Yadkin basins.
Present in the piedmont region are streams in largely forested areas with comparatively
undeveloped catchments and very good water quality. In recent years, streams of the piedmont
have experienced recurring moderate to severe drought. The drought conditions have led to
acute awareness of the limits of availability of freshwater for drinking, recreation, and
assimilation of discharge effluent.
Changes in hydrology and habitat condition have altered the biological communities of many
piedmont streams and rivers. For example, many species requiring flowing water and good
water quality have been replaced by habitat generalists that can tolerate a variety of conditions.
Only a small percentage of piedmont streams, such as the upper Tar and Roanoke, presently
support a diversity and abundance of freshwater mussels similar to historic distributions.
2.1.3 Coastal Plain Streams
Freshwater streams of the coastal plain are located within the inner and outer divisions of the
Coastal Plain Physiographic provinces. Coastal plain streams are characteristically low to
medium gradient, with sandy to muddy substrate that provides habitat to warmwater adapted
communities. Groundwater and surface water can be tightly linked in the coastal plain. Further,
5
water quantity and quality are closely linked as flow has important effects on salinity and
dissolved oxygen concentrations.
The freshwater and estuarine streams in the coastal plain have different origins. Many
freshwater streams in the coastal plain region are non-tidal streams originating in the coastal
plain. A very small number of non-tidal stream reaches present along the westernmost
boundary of the Inner Coastal Plain Physiographic province originate in one of five piedmont
basins and are typically medium gradient streams in the upper reaches. Saline, tidal streams
originating in the coastal plain are present along the eastern boundary of the Outer Coastal
Plain. Bi-directional flow from wind-driven and astronomical tides may reach into the inner
coastal plain rivers and streams.
Natural communities in coastal plain streams include in-channel and floodplain communities that
support resident and non-resident migratory fishes, and many species adapted to blackwater
and swamp conditions. Riparian floodplains and instream aquatic vegetation associated with
coastal plain stream systems influence the aquatic ecology of the coastal plain region.
Seasonal hydrologic variations, such as flooding, are important to these flood plain forest
communities as well.
The species and ecology in coastal plain streams are often different from those found in inland
waters. For example, diadromous fish (fish that migrate between fresh and salt water) require
high seasonal flows to cue spawning. Flow in these streams is important during both spawning
periods and other times to allow juvenile fish growth within freshwater reaches. Ditching and
channelization are extensive, and links between natural and engineered waterways are
common with resultant modified flows. Barriers, such as culverts and low head dams, restrict
fish movements, particularly diadromous fish and are an issue in many coastal plain and inland
watersheds.
Many ecologically and economically valuable estuarine species are dependent on freshwater
flows in the coastal plain streams to maintain low-salinity conditions. The position of the salt
wedge is a condition critical for transitional communities. Examples of the aforementioned
estuarine species include southern flounder, Atlantic croaker, spot, menhaden, bay anchovy,
blue crab, white shrimp, and striped mullet. Flows that trigger spawning are also critical for the
integrity of these systems. Flows during larval and juvenile growth and development are equally
important.
2.1.4 Headwater Streams
The majority of stream miles in North Carolina are classified as headwater streams (drainage
area < 10 km2) (Olivero and Anderson 2008). Headwaters include the smallest parts of streams,
are located in the extreme upper portions of a watershed, and are the furthest distance from the
stream’s mouth. The origin of headwater streams varies per topography and geology, as well
as the stream’s location within the state. For example, headwater streams in high gradient
areas usually contain large rocks and have high velocities, whereas headwater streams in low
gradient areas are abutted by large floodplain areas and have low velocities.
Headwater streams originate in almost every type of terrestrial community from undeveloped
watersheds to highly developed watersheds containing impervious surfaces and agricultural
areas. Headwater streams have small drainage areas, higher elevations, and are more prone
to dewatering in comparison to downstream waterways. Headwater streams provide habitat for
6
numerous species and play a significant role in the removal of pollutants, nutrients and
sediment, assist in flood control, and provide groundwater recharge.
Headwater streams are narrower and shallower than larger streams and rivers, and the water in
headwater streams contacts the streambed and banks more regularly than in larger streams
and rivers. The health of and impacts to headwater streams affects the health, water quality,
and species composition and abundance of downstream systems. Human alterations, due to
agricultural and urbanization, mainly in the more heavily populated areas of the state, have
altered or eliminated headwater streams.
There are limited hydrologic data from headwater streams within North Carolina. Based on a
GIS evaluation, roughly 8% of the 284 USGS stream gages in North Carolina are located on
headwater streams. Discharge estimates for ungaged streams must be derived from
mathematical regression equations based upon relationships of drainage area and precipitation.
Using these equations, the USGS in cooperation with the state has evaluated low-flow
characteristics statewide and for selected streams in several drainage basins in North Carolina
including the Roanoke, Neuse and Cape Fear (Geise and Mason 1993, Weaver 1996, Weaver
1998). In addition, discharge estimates can now be generated through the use of sophisticated,
proprietary models such as WaterFALL (for further information see Appendix D).
Biological data from headwater streams in North Carolina are also limited. In particular, the
biological data used in the RTI/USGS analyses (Appendix D) were generated from the DWQ
“Stream Fish Community Assessment” and “Benthic Macroinvertebrate Assessment” datasets.
Roughly 10% of the invertebrate sampling sites and 15% of fish sampling sites are located in
headwater streams. These percentages are in contrast to nearly 72% of invertebrate and 50%
of fish sampling sites located in the next class, Creeks (drainage area ≥ 10km2 and < 100 km2)
(Olivero and Anderson 2008).
2.2 The Importance of Flow in Riverine Systems
Flow is generally considered the “master variable” of riverine systems, including adjoining
riparian areas, because it is always a determinant of water quality, biology, physical habitat, and
energy transfer (Poff et al. 1997, Annear et al. 2004). All components of the flow regime
(magnitude, duration, frequency, timing, and rate of change), including natural variability, are
important to maintaining ecological integrity. Natural variability of flows includes intra-annual
and inter-annual variability and consists of extreme low flows, low flows, high flow pulses, small
floods, and large floods. Collectively, these concepts are known as the “natural flow paradigm”
(Poff et al. 1997).
Maintaining flow variability benefits native species that have adapted to such variability and
inhibits invasive species from flourishing (Poff et al. 1997, Cummins et al. 2011). High flows
restructure the channel profile through transport of substrate, bank scour and suspension of
organic matter from the riparian zone; benefit reptiles and amphibians by refilling vernal pools;
and also function as cues for spawning and migration. Low flows benefit top predators by
concentrating prey and plant communities by providing habitat for the establishment of
submerged aquatic vegetation and floodplain plant species. Seasonal low flows may also
benefit freshwater mussel species by improving spawning and the release of glochidia in the
presence of host fish species (DePhilip and Moberg 2010).
Many studies have shown that altering one or more flow regime components can significantly
impact biota, including fish, mussels and aquatic insects (Freeman et al. 2001, Freeman and
7
Marcinek 2006, Knight et al. 2008, DeGasperi et al. 2009, Kennen et al. 2009, Rypel et al. 2009,
Carlisle et al. 2010, Kanno and Vokoun 2010, Peterson et al. 2011, Mims and Olden 2012,
McManamay et al. 2013). A recent meta-analysis showed that, of the 165 studies reviewed,
92% indicated a reduced ecological condition when flows were altered (Poff and Zimmerman
2010). However, it was noted that the data are often noisy and statistical relationships are not
always strong. Many streams and rivers in North Carolina have already been subject to flow
alteration.
2.3 Strategies to Determine Ecological Flows
There are two general strategies that have been used to determine ecological flows: habitat
response models and biological response models. A habitat response model is one in which
the quantity and quality of available habitat is measured relative to variation in flows. A
biological response model is one in which the composition and structure of the biological
community is measured relative to variation in flows. Traditional efforts to understand the
impacts of altering hydrology often focused on the relationship of flow to habitat availability
(Stalnaker et al. 1995, Washington Department of Ecology 2010). Although such habitat
response models are an indirect and intermediate measure of expected biological response,
they are useful when time and money limit the implementation of biological studies. Often,
habitat models utilize habitat use or preference curves for guilds of species to ensure that all
types of habitat are represented in the analysis (Vadas and Orth 2000, Persinger et al. 2010).
North Carolina has relied heavily on habitat response models, such as PHABSIM (Physical
Habitat Simulation), when conducting site-specific flow studies. PHABSIM is a specific model
designed to calculate an index to the amount of microhabitat available for different life stages of
aquatic organisms at different flow levels, incorporating two major analytical components:
stream hydraulics and life stage-specific habitat requirements. DWR, at the request of the
EFSAB, conducted additional analysis of PHABSIM sites in the piedmont and mountain portions
of North Carolina in an effort to better demonstrate how flow and habitat availability (response)
impact biological communities.
Additionally, a new framework to determine ecological flow regimes for large geographic areas
was examined. The Ecological Limits of Hydrologic Alteration (ELOHA) approach outlines a
step-wise process that involves establishing a hydrologic foundation, classifying rivers, and
determining flow-ecology relationships before entering a social process to develop flow regime
standards (Poff et al. 2009). The classification step has been used elsewhere by several
researchers, including those using only hydrologic parameters (Henriksen et al. 2006,
McManamay et al. 2011a) and those using other basin characteristics (Olivero and Anderson
2008, Liermann et al. 2011, McManamay et al. 2011b, Olden et al. 2011). Other basin
characteristics often include metrics such as water temperature, gradient (also referred to as
slope), stream size, and geology.
Many other states and regions have undertaken efforts to determine ecological flow standards.
The EFSAB reviewed many reports and policies, including:
 Alberta (Locke and Paul 2011; also Clipperton et al. 2003)
 Canada (Department of Fisheries and Oceans 2013; Linnansaari et al. 2013)
 Connecticut (Connecticut Department of Environmental Protection 2009)
 Georgia (Evans and England 1995)
 Michigan (Hamilton and Seelbach 2011)
 Potomac River Basin (Cummins et al. 2011)
8
 Nine case studies (Kendy et al. 2012)
 Pennsylvania (Apse et al. 2008)
 South Carolina (de Kozlowski 1988; Bulak and Jobsis 1989)
 Susquehanna River Basin (DePhilip and Moberg 2010)
 Texas (Texas Commission on Environmental Quality et al. 2008)
This literature review revealed that a variety of approaches have been used to determine
ecological flows and the flow standards can be categorized into three basic types–minimum-flow
thresholds, statistically-based standards, and percentage-of-flow standards. Minimum-flow
thresholds include 7Q10, September median, and monthly median. Other minimum-flow
thresholds are based on the Tennant method, which is a percentage of mean annual flow,
varying by month (Tennant 1976). Statistically-based standards consist of a series of metrics
designed to mimic various flow components (e.g., low flows, high flows, flood pulses) within a
range determined from a statistical analysis of the past hydrograph. This type of flow
recommendation typically consists of a set of target flow magnitudes, durations and frequencies
for each month or season. The percentage-of-flow standard allows only a certain percent of
flow to be extracted for off-stream use; the remainder is left in the stream. Minimum-flow
thresholds do not retain intra- and inter-annual variability like percentage of flow approaches.
Literature on flow requirements for coastal systems was also reviewed because low-gradient
and tidally-influenced streams function differently from other inland streams. In coastal systems,
flow may play a secondary role to other factors including tides, salt concentration, and
community structure and function (Jassby et al. 1995, Adams et al. 2002, Alber 2002, Mattson
2002, Powell et al. 2002). General approaches to estuarine inflow management fall into three
categories‒inflow-based, condition-based, and resource-based. The inflow-based approach
keeps flow within selected prescribed bounds under the assumption that taking too much away
is bad for the resources. A condition-based approach is one in which inflow standards are set in
order to maintain a specified condition (e.g., salinity) at a given point in the estuary. Finally, a
resource-based approach sets inflow standards based on the requirements of specific
resources (e.g., shrimp). A separate section is presented on the assessment of ecological flows
within the coastal plain (Appendix C).
2.4 Advancing the Science of Ecological Flows
In addition to reviewing the literature and input from experts who gave presentations to the
group, the EFSAB analyzed the results of new research and analyses specific to North Carolina.
Certain analyses were undertaken by DWR, and others were commissioned by EFSAB
members to support the board’s efforts. Additional research was conducted to meet the
objectives of individual organizations which also proved beneficial and informative to the EFSAB.
During the course of the EFSAB’s tenure, it became clear that the multiple research efforts
should be coordinated to maximize outcomes and avoid duplication. Thus, an Ad-Hoc Water
Research Coordination Group was formed by those entities conducting and/or funding the
research. This group was not a formal part of the EFSAB, although it was instrumental in
advancing the science of ecological flows and keeping the EFSAB informed. The Coastal
Ecological Flows Working Group (CEFWG) was formed to assess ecological flows in the coastal
plain.
9
2.4.1 Flow-habitat Relationships
Over the past several decades, DWR has conducted or assisted in numerous site-specific
studies to evaluate the effect of water resource projects on stream flows and aquatic habitat.
The types of projects have included federal hydropower relicensing, water supply reservoirs,
new or expanded water supply withdrawals, and water resource planning studies.
DWR updated nine PHABSIM study sites from the piedmont and 10 sites from the mountains to
analyze the influence of different flow scenarios on habitat for a variety of species, species life
stages and guilds (Table 1 and Figure 1). While this analysis included streams from across the
state, they were typically clustered in just a few areas. The 19 sites shown are about half of the
studies in which DWR has participated. The studies are clustered because of their association
with multi-site hydropower projects (mountains), water supply projects (piedmont), or the age of
the study that allowed a quick update of the computational platform.
Table 1. DWR PHABSIM sites used for ecological flow analysis.
Piedmont Stream Drainage
Area (mi2) Mountain Stream Drainage
Area (mi2)
Buckhorn Creek 76 Davidson River 14
Buffalo Creek 127 ‘East Fork’ Tuckasegee River 82
Eno River 99.4 Jonathan Creek 14
First Broad River - upper 145 Nantahala River - upper 101
First Broad River - middle 202 Nantahala River - lower 143
First Broad River - lower 230 North Fork Mills River 10
Rocky River 55 Tuckasegee River 287
Tar River 437 West Fork Tuckasegee River - upper 53
West Fork Eno River 11 West Fork Tuckasegee River - lower 56
Whiteoak Creek 14

Figure 1. Location of 19 DWR PHABSIM sites analyzed for the EFSAB report.
PHABSIM involves evaluating the suitability of habitat in the stream reach at a variety of stream
flow levels. Individual cross-sections, or transects, are selected to represent the range of
10
habitat types available at each site. Data are collected during at least three different stream
flow conditions, and PHABSIM files are calibrated using these data to allow simulation of the
physical conditions over a wide range of flows. Each study involves the collection of site-
specific data for the stream channel, including cross-section profiles, depths, velocities,
substrate, and cover objects. Stream velocity, depth, substrate and cover at each stream flow
level are assessed relative to the habitat needs and preferences of the species or guild.
PHABSIM modeling combined with time-series analysis is a two-step approach to evaluating the
availability of habitat to support a species or guild. Habitat versus flow relationships are
determined by PHABSIM, and the frequencies of occurrence for different levels of habitat can
then be compared for different flow regimes using time-series analysis.
The aquatic species or representative guilds selected for modeling depends upon the proposed
project location, identified problem, or management goals. Table 2 list the guilds and species
modeled in the piedmont and mountains for the EFSAB. Each guild or species modeled has a
set of habitat suitability indices that represents how the organism responds to different stream
velocities, depths, substrates, and cover objects. The suitability indices may also be referred to
as preference curves (Figure 2).
Table 2. The guilds and species used for the piedmont and mountain PHABSIM analyses.
Piedmont Sites Mountain Sites

Shallow Guild Shallow Guild
shallow slow, young of year blacknose dace spawning
shallow slow, aquatic vegetation cover blacknose dace fry
shallow slow, woody debris cover blacknose dace juvenile
shallow slow, coarse substrate brown trout fry
shallow slow, fine substrate, no cover brown trout juvenile
shallow fast lower velocity creek chub young-of-year
shallow fast moderate velocity creek chub adult
shallow fast higher velocity longnose dace adult/juvenile/spawning
mayfly northern hog sucker juvenile
stonefly rainbow trout fry
caddisfly mayfly
stonefly

Deep Guild Deep Guild
deep slow, cover brown trout adult
deep slow, cover version 2 brown trout spawning
deep slow, no cover mottled sculpin adult/juvenile
deep fast, fine substrate northern hog sucker adult
deep fast, gravel/cobble substrate rainbow trout adult
deep fast, coarse substrate rainbow trout spawning
golden redhorse adult caddisfly
golden redhorse juvenile

PHABSIM simulates the physical conditions that would result from the range of stream flows for
the selected cross-sections within a stream reach. The habitat suitability indices are correlated
to the physical conditions simulated by PHABSIM in order to produce a set of values indicating
the amount of habitat available for the species or guild assessed at each stream flow level for
11
the study site. The set of values are weighted relative to the suitability of the habitat and are
expressed in terms of the area per 1,000 feet of stream length. The values are referred to as
the weighted usable area (WUA) (Figure 3).

Figure 2. Example of habitat suitability curves (brown trout adult: velocity, depth, substrate-
cover) used in PHABSIM modeling.

Figure 3. Example of WUA habitat-discharge relation (mountain-deep species/life stages)
output from PHABSIM modeling.
The end result of PHABSIM is a habitat versus flow relationship for each guild or species that
covers the range of flows evaluated. The next step–time-series analysis–uses the habitat
versus flow relationship to convert a record of daily stream flows to a record of daily habitat
availability. A record of daily habitat for each species or guild can be generated using records
from a nearby USGS gage station or results of hydrologic modeling where daily flow records are
not available. Time-series analysis can thus be combined with different hydrologic model
12
simulations (e.g., existing withdrawals or future water use projections). The end result of time-
series analysis is a comparison of habitat availability under different flow scenarios, often
represented as a table or curve showing the frequency of habitat levels occurring across a
range of flows for various hydrologic scenarios. The output of the time-series analysis is
reported by month and by season with the seasons being defined as follows. Fall includes
October and November, and Winter extends from December through March. Spring represents
April through June, and Summer is July through September.
For the ESFAB, the studies assessed the effects of 15 flow records, separated into three groups
of scenarios, which were generated using SAS software and routines. The three groups of
stream flow approaches that were evaluated in time-series analysis were: (1) minimum flows
(annual 7Q10, monthly 7Q10, September median, and monthly median); (2) percent of mean
annual flow (MAF) (10 to 60 percent MAF in increments of 10 percent); and (3) percent of inflow
(70 to 90 percent of inflow in increments of 5 percent). The 7Q10 value is typically used for
determining the assimilative capacity of a receiving stream when permitting wastewater
discharges. The September median flow is the lowest monthly median flow in most years and
has been used by some states as a minimum flow requirement for projects.
The unregulated, or baseline, flow records used for the piedmont sites were generated by DWR
from OASIS basin models for a period from 1928 to 2008 with no reservoirs or flow alterations
other than that associated with changes in land cover. Due to the lack of an OASIS model for
the Little Tennessee and French Broad basins, the unregulated flow records used for the
mountain sites were produced by the WaterFALL program for the period from 1967 to 2006 with
no reservoirs or flow alterations, and a 1970’s land cover.
One method of reporting and comparing the WUA for each guild or species from the time-series
analysis is a habitat metric termed Index B. The Index B value is the average of all habitat
values in a month across all years of the analysis that are between the 10th percentile and the
90th percentile of habitat values for the selected month. The outlier values that fall below the
10th percentile and above the 90th percentile are not included in the calculation of Index B.
Index B values were calculated for each guild/species on a monthly basis for each flow scenario.
Project alternatives were assessed by computing the ratio of the Index B value for a particular
flow scenario (numerator) to the Index B value for the unregulated flow record (denominator).
Index B ratios between 0.80 and 1.20 were considered to be within the preferred habitat range.
Ratios < 0.80 or > 1.20 were considered to represent a substantial change to the stream
ecosystem. Index B ratios based on a denominator value less than 1,000 WUA were
considered separately because small changes in the numerator cause disproportionately large
changes in the ratio.
Analysis and reporting of Index B ratios was presented in tabular and graphic form. The graphs
are summarized by seasons, as defined in the time-series analysis output, for either all 19
guilds/species combined or for the deep water guilds/species and the shallow water
guilds/species.
All three flow scenario groups (i.e., minimum flow, percent of MAF, and percent of inflow) for the
piedmont and mountain sites generally exhibited a trend of more guilds/species failing to meet
the 0.80 ratio criterion as the flow regimes departed from the unregulated baseline condition
(Figures 4 and 5). In terms of the percentage of guilds exceeding the 1.20 ratio criterion, the
three flow scenario groups showed differing responses in the piedmont. Most of the nine
piedmont sites exceeded the 1.20 ratio criterion for all of the minimum flow and percent of MAF
13
flow scenarios. This was also true for the percent of inflow group, except for the 85% and 90%
inflow scenarios. Most of the mountain sites also generally exhibited a trend of more
guilds/species exceeding the 1.20 ratio criterion as the flow regimes departed from the
unregulated baseline condition (Figure 6). All of the 10 mountain sites exceeded the 1.20 ratio
criterion for all of the percent MAF scenarios. The majority of the mountain sites exceeded the
1.20 ratio criterion for all of the minimum flow and percent of inflow scenarios, except for the
monthly median, 85% inflow and 90% inflow scenarios.
In general, 12 of the 19 PHABSIM sites in the piedmont and mountains showed a habitat
response in the preferred range for all seasons under one or more of the following three flow
scenarios: monthly median, 85% inflow and 90% inflow. Four of nine sites in the piedmont and
eight of 10 sites in the mountains had all seasons within the preferred habitat range for one or
more of the three flow scenarios. In the piedmont, the lower First Broad River site was within
the preferred range for the monthly median flow scenario and the 90% inflow scenario. Buffalo
and Buckhorn creeks and Tar River were within the preferred range for the 90% inflow scenario.
Tar River was also within the preferred range for the 85% inflow scenario. In the mountains, the
monthly median flow maintained the Tuckasegee and lower Nantahala rivers within the
preferred range for all seasons. Davidson and upper Nantahala rivers and Jonathan Creek
were within the preferred habitat range for the 85% inflow scenario, while the 90% inflow
scenario maintained seven of the 10 sites within the preferred range.

Figure 4: Total percent of eight piedmont deep guilds/species not meeting the 0.80 habitat
criterion under 15 flow scenarios at nine piedmont sites in Summer. Visit the DWR
ecological flow web site for a complete set of habitat response graphs.
14

Figure 5. Total percent and associated mean of 12 shallow guilds/species not meeting the
0.80 habitat criterion under 15 flow scenarios at 10 mountain sites in Spring. Visit
the DWR ecological flow web site for a complete set of habitat response graphs.

Figure 6: Mean, range, and quartiles of the percentage of 12 shallow guilds/species exceeding
the 1.20 habitat criterion under 15 flow scenarios at 10 mountain sites in Spring.
Whiskers represent the range of values and the boundary between the red and blue
boxes represents the median value. Visit the DWR ecological flow web site for a
complete set of habitat response graphs.
15
2.4.2 Flow-ecology Relationships
Ecological flow regime recommendations specific to North Carolina can be developed by
determining how biota in streams respond to changes in flow. One approach involves relating
biological conditions to flow across a range of flow conditions (space for time approach) or by
changes in biological conditions at a site over time. Another approach is to track biotic
conditions over time to changes in flow. Organizations outside of the EFSAB tried both
approaches and reported their results to the Board. The primary biotic conditions considered for
both approaches are related to community structure of fish or macroinvertebrates. The most
information is on community structure of fish and benthic invertebrates. Ecosystem condition
and ecological integrity are inferred from fish and benthic communities in most cases.
Ecological integrity involves the interplay of both ecosystem structure and function and the
ability to respond to environmental perturbations. Functional indicators of ecosystems are
particularly difficult to measure directly, and structural indicators have had a long history of
defining function in environmental management (Brinson and Rheinhardt 1996). Without
statewide data on ecosystem structure and function, the EFSAB relied on fish and invertebrate
community composition to infer ecological integrity.
Fish and benthic macroinvertebrates were evaluated as indicators of ecological integrity. Two
components of the ecology of these assemblages were assessed: (1) habitat availability and (2)
species distribution. Habitat availability of both assemblages was used in the PHABSIM
approach (above). Species distribution approach was assessed by RTI International (RTI) and
USGS. Sensitive indicators for the latter approach were designated as the Shannon-Weaver
Diversity Index of the riffle-run fish guild and the taxa richness of the EPT benthos (the number
of mayfly, stonefly, and caddisfly taxa). These indicators are correlated with ecodeficits, a
measure of flow deficiency over the period of evaluation (typically the period of record), so the
species’ responses reflect recovery (or lack of it) from environmental perturbations.
RTI and USGS conducted numerous statistical analyses to find meaningful relationships
between several fish and aquatic insect metrics and various flow metrics. They used the space
for time approach with 649 fish and 1,227 benthos sites deemed wadeable from nearly all major
river basins in North Carolina. Although wadeable streams include some larger rivers, there
was a lack of information from the largest rivers and many coastal systems.
Initially, the efforts attempted to include other explanatory factors, such as stream size and
basin characteristics, but these were unsuccessful. Ultimately, significant relationships were
found between six flow metrics and Shannon-Weaver Diversity Index of the riffle-run fish guild
and richness of benthic (Ephemeroptera, Plecoptera, Trichoptera) species. The six flow metrics
included the annual and seasonal (winter, spring, summer, and fall) ecodeficits and reductions
in the average 30-day minimum flow. Figure 7(A and B) presents responses of riffle-run fish
guild diversity (Figure 7(A)) and benthic EPT richness (Figure 7(B)) responses to summer
ecodeficit. Refer to Appendix D for additional information regarding the methods and results of
the project that developed the flow-ecology relationships for fish and benthos in North Carolina.
16

Figure 7. Flow-ecology relationships for: (A) the Shannon-Weaver diversity of the riffle-run fish
guild (n = 649) and (B) benthic EPT richness (n = 1,227) in response to summer
ecodeficits in wadeable streams in North Carolina.
The Nature Conservancy (TNC) analyzed spatial-temporal patterns of changes in flow and biota
over time and explained how they are relevant to ecological flow guidelines. While the primary
project purpose was to assist TNC in prioritizing conservation areas, it also was designed to
provide meaningful information to the EFSAB to develop ecological flows.
Fish diversity and abundance at 141 sites in four North Carolina river basins (Roanoke, Cape
Fear, Tar, and Little Tennessee) were compared to flow for the period of 1992 to 2009. These
sites were in wadeable portions of streams and rivers, so data are lacking from large rivers.
Many sites saw relatively little change in fish diversity and abundance over time. However, fish
abundance and diversity declined in portions of the Cape Fear and Tar basins.
To understand the direct influence of water withdrawals, only sites located downstream of
known water withdrawals were analyzed further. While only 14 data points fit this criterion, they
showed a negative relationship between fish diversity and the relative size of the water
withdrawal. While the relationship was statistically significant, the explanatory power of the
relationship was small due to the small sample size. With that caveat in mind, the analysis
showed a 5‒10% decline in species diversity relative to a withdrawal equivalent to 10% of the
mean annual flow. A withdrawal of 50% of the mean annual flow resulted in a 25‒30% decline
in species diversity.
2.4.3 Attempts at Stream Classification
DWR worked closely with Environmental Flow Specialists Inc. (EFS) on efforts to characterize
and classify North Carolina streams based on flow characteristics from USGS gage data. The
effort resulted in a classification scheme comprised of seven stream classes that generally
reflected stream size and flow stability, and was similar to a classification produced by
McManamay et al. (2011a) that had eight classes. However, further analysis by RTI comparing
the two classifications found that they were not similar enough to be used interchangeably.
17
Analysis by RTI also found that classes generated from hydrology derived from USGS gages
often differed from hydrology created from the WaterFALL rain-runoff model. This was true for
both the EFS and McManamay classification frameworks. Therefore, it was concluded that
neither classification approach should be extrapolated beyond the USGS gages to ungaged
sites. Because of the uncertainty associated with the classes generated from either framework,
it was agreed that developing flow recommendations for these different stream classes is not
appropriate at this time.

18
3 RECOMMENDATIONS OF THE EFSAB
3.1 Statewide Ecological Flow Evaluation
To evaluate flow scenarios in most North Carolina streams, the EFSAB recommends the
following two strategies to assess whether ecological flows are maintained:
1. The percentage of flow strategy using 80‒90% flow-by combined with a critical low flow
component as the ecological flow threshold. If the basinwide hydrologic models indicate
that there is insufficient water available to meet all needs, essential water uses and
ecological flows at a given location, then further review by DENR is recommended.
[Flow-by is defined as “the percentage of ambient modeled flow that remains in the stream.”]
2. The biological response strategy should be used to determine the current and future
modeled biological condition of locations in the basinwide hydrologic models. DENR should
evaluate the change in current and future biological condition as a decision criterion. A
5‒10% reduction in biological condition is suggested as a threshold for further review by
DENR.
The EFSAB recommends a statewide approach to establishing ecological flows based on the
simultaneous use of these two strategies:
3.1.1 Percentage of Flow Strategy
Natural flow regimes are important in maintaining instream, riparian, and floodplain ecosystem
diversity and resilience (Poff et al. 1997). The natural flow paradigm postulates that natural
ecosystems are best protected by maintaining flow regimes close to their unaltered state in
terms of the five flow components (magnitude, duration, frequency, timing, and rates of change),
including intra- and inter-annual variability. The most effective mechanism for resembling a
natural flow regime in altered river systems is to use a percentage of flow approach (Richter et
al. 2011), also known as a “flow-by” approach. It is conceptually simple and relatively easy to
implement.
As an ecological flow standard, the flow-by approach works by requiring a percentage of the
“instantaneous” natural flow to remain in the river (Figure 8). The flow-by approach is being
used in the US, Canada, and Europe (Richter et al. 2011, Locke and Paul 2011). The
percentages typically range from 80‒90%. In the North Carolina basinwide hydrologic
models, the EFSAB recommends that the ecological flow should be 80‒90% of the
instantaneous modeled baseline flow.
The EFSAB recommends a flow-by range of 80‒90% for several reasons. Based on results of
PHABSIM analyses for North Carolina, there was no apparent threshold in the data indicating a
decline in predicted habitat, and flow-by percentages greater than 80% were most consistently
protective of all guilds and species modeled. Furthermore, there was no consensus on a single
flow-by percentage by the EFSAB. A range of 80‒90% is common in the literature and other
jurisdictions. Therefore, the EFSAB recommends a range of 80‒90% as protective for North
Carolina streams. The EFSAB is not recommending using different values for different kinds of
streams, but suggesting that DENR use its discretion to select the most appropriate value for
planning purposes.
19

Figure 8. Example of a percentage flow-by approach.
The definition of “instantaneous” depends on how the flow-by approach is implemented. In a
hydrologic planning model, instantaneous would be set at the normal time step of the model.
For example, in a model that uses daily average flow, the flow-by value would be 80% of the
daily flow for each day in the model’s period of record. On the other hand, if a model uses a
time-step of 15 minutes, the flow-by value would still be 80%, adjusted every 15 minutes. In any
model, the flow-by calculation is simply the baseline flow multiplied by the flow-by percentage.
In a real world implementation, the time step might be daily or every three days. Another
difference in a practical application is that the flow-by might be based on the flow from the
previous day, because, unlike a model, the flow for the current day is unknown. Because the
North Carolina basinwide hydrologic models use daily average flow, the flow-by value
should be calculated on a daily time step.
To the extent possible, flow regimes representing natural conditions (flow regimes without
withdrawals or returns), baseline conditions (flow regimes incorporating current withdrawals and
returns), and projected conditions (flow regimes incorporating current and future withdrawals
and returns) should be estimated by basinwide hydrologic models. Baseline conditions will be
compared with natural and future conditions to assess how much hydrology has been altered
and to determine the effects of future withdrawals and returns. DENR should use this
information to identify areas that have undergone substantial hydrologic change and that
warrant additional attention when considering further water withdrawals. As the hydrologic
models are updated with new withdrawals and returns, baseline conditions should
continue to be used as a benchmark to avoid comparisons to a continually shifting
“current” condition. The recommended baseline should be the management regime
extant when the legislation was passed in 2010.
Another consideration of the flow-by approach is that it should consider cumulative effects;
otherwise, multiple withdrawals could result in an overall reduction in flow below the flow-by
threshold (Figure 9). Therefore, the cumulative net upstream withdrawals at any point in
the basin established after 2010 should not result in flows that are predicted to fall below
the flow-by criterion.
20

Figure 9. A percentage flow-by criterion must take into account the cumulative effects of water
use along each stream. This example shows how quickly five withdrawals each
adhering to an 85% flow-by criterion for incoming flows can result in a large
cumulative loss in flow (56% reduction between 0 and 10 km) for a section of stream
with no inflows (e.g., tributaries or return flows).
Percentage flow-by should be combined with a critical low-flow component that is intended to
protect the aquatic ecosystem during periods of drought (Figure 10). The critical low flow
represents a point at which further human-induced reductions in flow are likely to result in
unacceptable levels of risk to the health of aquatic resources. Low-flow events are most critical
for contributing to biological impacts. The critical low-flow criteria are derived from historic flow
records and represent expected low flows. These criteria are intended to prevent increasing the
frequency or duration of extreme low flows (drought conditions) that are damaging to ecosystem
health. Other jurisdictions are beginning to use a critical low-flow component for protecting
ecological integrity. For example, Alberta, Canada uses the monthly 20th percentile flow as a
critical low flow (Locke and Paul 2011). The EFSAB recommends DENR incorporate critical
low flow as a component of the ecological flow threshold.
Ecological flows are set as the larger of the flow-by or critical low-flow values on a daily
time step. If actual flows fall below the criterion for ecological flows, DENR should evaluate
current water uses to determine the best path forward/strategy to minimize ecological effects
while meeting the basic needs of current water users.
As a means of assessing the potential for ecological impacts based on projections of future
water use, the EFSAB recommends DENR use the baseline hydrology dataset defined above
and a daily flow record containing only days when flows are between the 10th and 90th
percentiles (trimmed hydrology dataset) to avoid assessments based on impacts of extreme low
or high flows. The purpose of this recommendation is to assist DENR in identifying basins or
nodes which are at higher risk for not maintaining ecological flows. DENR should evaluate
potential for adversely impacting ecological flows at all flow nodes.
21

Figure 10. Percentage flow-by is shown with a critical low-flow threshold to protect against
increasing the severity and duration of drought periods. The criterion for ecological
flow is the larger flow value defined by the daily percentage flow-by or the critical
low-flow threshold.
Using the flow records described above, the EFSAB proposes the following approach to
evaluate ecological flows at model flow nodes:
1. The ecological-flow threshold should be calculated as the greater of the flow-by and
critical low-flow values. If none of the nodal flows from the baseline record fall below this
threshold, then the risk of impact relative to ecological flows will be considered to be low
and no immediate action is recommended (green flag).
2. If one or more days of the existing or projected daily model flows fall below the
ecological-flow threshold but all of the projected flows within the trimmed hydrology
dataset remain above the ecological-flow threshold, this should alert DENR to begin
further review of water usage that may be contributing to the deviations (yellow flag).
Management tools including water shortage and drought response plans should be
evaluated for the purpose of maintaining ecological integrity.
3. Stream reaches associated with nodes having one or more days of the trimmed
hydrology dataset less than the ecological-flow threshold should be given additional
review by DENR (red flag). Management tools including water shortage and drought
response plans should be evaluated for the purpose of maintaining ecological integrity.
Additional review could include actions such as conducting site-specific evaluations or
review and modeling of any biological data that are available.
The establishment of ecological flows based on a combination of percentage flow-by and critical
low-flow thresholds represents the best available methodology for the protection of aquatic
22
resources. However, these methods are based on hydrologic models that may not be
applicable to all streams across the state since the stream gages needed for model verification
may not be available for smaller streams. These models also do not directly address the
relationship between flow alteration in the state and biological effects. Fortunately, North
Carolina has a well-developed biological assessment program that provides data that can be
used to model the effects of flow alteration on biology.
3.1.2 Biological Response Strategy
Biological response models developed by RTI and USGS should be used to evaluate the effects
of flow regimes on fish (as measured by the Shannon-Weaver Diversity Index of the riffle-run
fish guild) and benthic macroinvertebrates (measured as EPT richness) on the basis of annual
and seasonal (winter, spring, summer, fall) ecodeficit, and reductions in the average annual 30-
day minimum flow. Ecodeficits are determined by computing the total negative change in flows
between altered and unaltered flow duration curves obtained from basinwide hydrologic models
(Figure 11; also see Appendix D).

Figure 11. Ecodeficits are calculated by measuring reductions in flow between altered and
unaltered flow duration curves.
These fish and benthic macroinvertebrate response models were derived from biological
monitoring data collected by DENR at 649 and 1,227 wadeable streams and rivers throughout
North Carolina, respectively (NCDENR 2013a and NCDENR 2013b). Quantile regressions
were used to develop the relationship between the 0.8 (i.e., 80th) response quantiles of fish and
benthos and ecodeficits for the annual, winter, spring, summer and fall seasons (Table 3).
Figure 12A presents the 0.8 quantile regression relationship of the riffle-run fish guild Shannon-
Weaver Diversity Index and summer ecodeficit and Figure 12B presents the same relationship
for benthic EPT richness.
23
Table 3. Statewide quantile regression models (Y = A + BX) relating ecodeficit (X) to
biological responses (Y) for riffle-run fish guild (Shannon-Weaver Diversity Index)
and benthic macroinvertebrates (EPT richness).
Riffle-run Fish Guild: Shannon-Weaver Diversity Index
Intercept (A) Slope (B)
Ecodeficit Value SE1 p-value2 Value SE p-value
Annual 100 2.580 <0.001 -1.429 0.429 <0.001
Winter 100 2.383 <0.001

-1.353 0.530 0.011
Spring 100 2.365 <0.001

-1.653 0.332 <0.001
Summer 100 1.797 <0.001

-2.761 0.469 <0.001
Fall 100 2.326 <0.001

-2.093 0.444 <0.001
Benthic macroinvertebrates: EPT richness
Intercept (A) Slope (B)
Ecodeficit Value SE p-value Value SE p-value
Annual 100 2.210 <0.001

-2.344 0.387 <0.001
Winter 100 2.050 <0.001

-2.427 0.334 <0.001
Spring 100 2.009 <0.001

-2.657 0.307 <0.001
Summer 100 2.005 <0.001

-2.433 0.257 <0.001
Fall 100 1.730 <0.001

-2.341 0.166 <0.001
1 Standard Error 2 p-value < 0.05 is considered statistically significant

Figure 12A. Quantile regression (0.8 quantile) showing the relation between summer
ecodeficit and riffle-run fish guild Shannon-Weaver Diversity Index (greyed area
indicates 95% confidence interval).
24

Figure 12B. Quantile regression (0.8 quantile) showing the relation between summer
ecodeficit and macroinvertebrate EPT richness (greyed area indicates 95%
confidence interval).
These biological response models provide DENR with an estimate of the most probable
statewide effect of flow alteration on biological condition. While these models provide a direct
link between flow alteration and biological effects for North Carolina streams and rivers, such
models are not designed to be highly predictive for specific sites. The uncertainty is high when
considering a particular time and place within a stream. Rather they provide expected and
statistically significant trends under various scenarios. Therefore, DENR should evaluate the
use of these models to assess changes in biological conditions associated with
projected changes in flow with the intent of developing biological criteria for
implementing further review of the ecological effects of flow alteration (e.g.,
implementation of site-specific studies such as PHABSIM). A 5‒10% change in biological
condition is suggested as an initial criterion for further review. This criterion is based on
the average range of EPT richness within the invertebrate condition classes (Excellent, Good,
Good-Fair, Fair, and Poor) as defined by DENR (see Table 17 in Appendix D). The 5‒10%
criterion represents a change of one-quarter to one-half of the width of a condition class (e.g.,
Excellent to Good). The 5‒10% criterion should be evaluated by DENR as more data are
collected.
The RTI/USGS report recommended varying the criteria for acceptable change on the basis of
the condition class of the stream or river. A 5% change would be tolerated for sites rated as
excellent, 10% for sites rated as good or good-fair, 15% for sites rated as fair, and a minimum
flow criterion for sites rated as poor. The rationale for this approach was to provide higher
protection for sites with high EPT taxa richness and lower protection for sites with lower EPT
taxa richness. The EFSAB decided not to adopt the RTI/USGS variable criteria because (1) it
requires that the site condition be known before the criterion can be applied and (2) there was
concern that the 15% acceptable change criterion was too large.
25
The adoption of a range (5‒10%) applied statewide carries important implications. For example,
EPT taxa richness at the least disturbed sites is known to vary by region (i.e., decreases from
mountains to piedmont to coastal plain) and to decrease with diminishing water-quality
conditions. Consequently, the amount of change (number of EPT taxa) that will be acceptable
using the 5‒10% criterion will vary by region (i.e., larger change allowed in the mountains and
smaller change allowed in the coastal plain) and by level of disturbance (larger change allowed
at sites with excellent conditions, smaller change allowed at sites with poorer conditions). While
the EFSAB supports the 5‒10% change criterion, it acknowledges that the application of this
criterion may result in the reduction of conditions at sites with exceptional quality conditions. The
EFSAB encourages DENR to consider additional protection for sites with outstanding biological
characteristics.
3.2 Exceptions to Statewide Recommendation
Headwater and coastal plain streams require different criteria for the establishment of ecological
flows. The following sections present recommendations for addressing these special situations.
3.2.1 Headwater Streams
There are limited biological and hydrologic data in headwater streams within North Carolina.
These streams have a higher vulnerability to disturbance, and the broader statewide approach
may not adequately reflect the potential for impact to ecological integrity. Therefore, for
streams with drainage basins < 10 km2, DENR should conduct additional analyses to
determine the potential for impact.
The EFSAB recommends that DENR conduct additional review, over and above that
recommended in the broader statewide approach, for proposed flow alteration of
headwater streams. For the purposes of this recommendation headwater streams are defined
as streams with drainage areas of < 10 km2 (3.9 mi2). This size class threshold for headwater
streams has been utilized in recent riverine assessments conducted by TNC for the Northeast
(Olivero and Anderson 2008) and Southeast (Olivero-Sheldon and Anderson 2013).
3.2.2 Coastal Streams
The Coastal Ecological Flows Working Group (CEFWG) developed a framework for providing
recommendations by introducing four potential approaches to determining ecological flows for
coastal streams, depending on the origin of the stream, the gradient or slope of the stream and
whether the stream has wind- or tidal-driven flow (Table 4):
Table 4. CEFWG proposed framework to determine ecological flows.
Origin Gradient Ecological Flow Approach
Statewide
Recommendation
Habitat
Relationship
Downstream
Salinity
Overbank
Flow
Piedmont Medium X X X
Coastal Plain Medium X X X
Coastal Plain Low X X X
Coastal Plain Wind or tidally
driven flow X X

26
The statewide ecological flow recommendation may be used where discharge and stage are still
closely correlated. Water level stations exist within the coastal plain below modeled reaches of
streams with piedmont reaches. These water levels could be correlated with the upstream or
nearby flows records from USGS gage stations. When correlation meets some criteria of
pattern similarity, regression can be used to extend known flows to ungaged reaches.
The low elevation, flat terrain and proximity to tidal, saline water combine to prevent the use of
current hydrologic models in the coastal plain that are used to determine the flow-by and
ecodeficit recommendations. Different approaches to ecological flows from those described are
required, although there is a lack of detailed understanding to offer specific protocols for this
region. Thus, a more general framework is recommended that categorizes coastal plain
streams and identifies four ecological flow approaches to be considered based on stream
category. The approaches include extension of the statewide flow-by criteria; conditions of
habitat, primarily for anadromous fish; downstream salinity; and overbank flow. Each stream
category may be subjected to more than one, but not all, approaches.
Flow requirements and recommendations for the viability of living aquatic resources have been
developed for eastern North Carolina and for specific river basins. The DMF has developed the
Coastal Habitat Protection Plan (CHPP) (Deaton et al. 2010) based on the concept of protecting
habitat for protection of living marine resources, especially fish and shellfish. The fish and
shellfish of concern include both residents of fresh to oligohaline waters and species that live
part of their life cycle in saline waters. This approach has goals similar to the efforts of the
EFSAB. The DMF should be directly engaged in establishing an approach based on CHPP and
other fisheries management plans. This action should also include plans to protect threatened
and endangered species.
Salinity is a key water quality factor dependent on flow. Organisms have different physiological
tolerances and dependencies for salinity that may vary with life stage. The physiological ranges
are directly related to reproductive, developmental and other ecological success of the
organisms. Further, salinity distribution is linked to the potential for low dissolved oxygen (DO)
conditions, especially in bottom waters. Affected organisms include both animals and plants.
Either position of a prescribed salinity or the salinity at a prescribed position has been used by
other states to index ecological flows. A recent study on the effects of future water withdrawals
in Greenville, NC, used salinity within the Tar River as its indicator of effect. The study should
provide insight into how salinity may be used for assessing ecological flow effects.
Overbank flow is dependent on stage with varying dependence on discharge associated with
location and elevation of a reach. Riparian, freshwater wetlands are often inundated during
colder months and dry or infrequently flooded during warmer months. This pattern is needed to
maintain community structure and ecosystem function of these wetlands. Blackwater streams
have unique characteristics derived from high dissolved organic matter concentrations and low
DO originating from wetlands, combined with slow velocities. Ecological flows within the coastal
plain should be sufficient to maintain the seasonal flooding regime in order to protect the
ecological integrity of these wetlands, a factor not necessary for streams in the mountains or
piedmont.
The framework presented here advances the assessment of ecological flows within the coastal
plain but not to the extent of that in other regions. It represents a way forward, but requires
further understanding of the relationships that control ecological flows and institution of
assessment efforts. Such efforts can be undertaken using resources within North Carolina, but
no one program within the state has the expertise or resources to fully advance and refine the
27
framework. It will take coordination and cooperation of the agencies within DENR and the
research community.
The EFSAB proposes that DENR continue to work with the CEFWG, and other agencies and
organizations as appropriate to further develop this framework. Several agencies within DENR
can contribute expertise and effort to CEFWG. The Albemarle Pamlico National Estuary
Program (APNEP) has ecological flows as a primary mission within its Comprehensive
Conservation and Management Plan (APNEP 2012). The DMF and WRC have expertise on the
key species and habitats of coastal North Carolina. The expertise of DWR is essential to
extending both ecological condition of coastal ecosystems and the hydrological modeling. The
Ecosystem Enhancement Program (EEP) also would have interest and relevant expertise. The
Water Resources Research Institute (WRRI) and Sea Grant Program (SG) at NC State
University provide a connection to the research community. All of these agencies have an
interest and stake in ecological flows within the coastal plain that go beyond the immediate
legislative needs directing the EFSAB.
Representatives of state agencies and others should meet to determine (1) general goals and
objectives, (2) their needs within this topic, (3) expertise and resources available from each, and
(4) a plan to achieve both general and individual goals. Once these agencies can establish their
aggregated objectives and general approach, other organizations can be invited to participate.
Other contributors should include various willing partners who participated in the EFSAB (e.g.,
industry and agricultural groups, federal and local government entities, environmental groups).
This should include RTI International, which did not have membership on the EFSAB but
contributed greatly. Initial leadership should come from someone associated with EFSAB
activities, but once a path forward is determined, this requirement may not be necessary.
Coincidental to this activity should be the stimulation of research directed toward ecological
flows within the coastal plain. WRRI and SG would be the likely sources of funds for this action,
but other agencies may have more directed funding opportunities. (See appendix of CEFWG
report for more information.)
3.3 Additional Recommendations
3.3.1 Threatened and Endangered Species
The flow requirements of listed species are often not fully understood. In order to conserve
state and federally listed species, the EFSAB recommends that the flow needs of these species
should be considered by DENR in addition to the standard recommendations offered in this
report. For planning purposes, portions of basins (e.g., nodes) that include listed species
should be treated by DENR as needing additional analysis in consultation with the WRC, NMFS
and USFWS. When a decision moves beyond planning, then applicable environmental review
documents will be sought from appropriate agencies. The EFSAB also encourages DENR and
other appropriate agencies to support further research on the flow requirements of listed
species.
3.3.2 Ongoing Validation Using an Adaptive Management Approach
There is uncertainty in the science and the existing models, thus a risk averse strategy was
used when devising recommendations. Changes in climate and land use are expected to have
significant effects on patterns of temperature, precipitation, hydrology and ecology. Monitoring
28
and predicting these changes will be critical for success in maintaining ecological integrity of
North Carolina’s rivers and streams. An adaptive management approach is required to
continually advance the science and reduce areas of uncertainty. Therefore, DENR should:
1. Emphasize new data (hydrologic and biological) collection and evaluation in headwaters,
in the coastal plain, and in large rivers, recognizing that current biological models and
assumptions may not address these systems.
2. Adopt/design/develop strategies to:
a. Validate ecological thresholds (strategies should be informed by new data or
research);
b. Track the impact of flow changes when they occur;
c. Modify characterizations, target flows, and thresholds based on new data, changing
conditions (e.g., land cover, precipitation, hydrology) and lessons learned; and
d. Georeference nodes in each hydrologic model to facilitate analysis.

29
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hydrologic regimes. Ecology 93(1):35-45.
NCDENR. 2013a. Division of Water Resources Water Quality Programs, Biological
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from http://portal.ncdenr.org/web/wq/ess/bau/ncibi-data.
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with a review of methodologies and applications in ecohydrology. Ecohydrology (2011). DOI:
10.1002/eco.251.
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Nature Conservancy, Eastern Regional Office, Boston. 40 p. + appendices.
Olivero-Sheldon, A. and M. Anderson. 2013. Stream classification framework for the SARP
region. The Nature Conservancy. 30 p.
Persinger, J.W., D.J. Orth, and A.W. Averett. 2010. Using habitat guilds to develop habitat
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Peterson, J.T., J.M. Wisniewski, C.P. Shea and C.R. Jackson. 2011. Estimation of mussel
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Report 98-4135.

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APPENDIX A – Session Law 2010-143
GENERAL ASSEMBLY OF NORTH CAROLINA
SESSION 2009
SESSION LAW 2010-143
HOUSE BILL 1743
AN ACT to direct the department of environment and natural resources to develop basinwide
hydrologic models, AS RECOMMENDED BY THE ENVIRONMENTAL REVIEW COMMISSION.
The General Assembly of North Carolina enacts:
SECTION 1. G.S. 143-350 reads as rewritten:
"§ 143-350. Definitions.
As used in this Article:
(3) "Essential water use" means the use of water necessary for firefighting, health,
and safety; water needed to sustain human and animal life; and water necessary to
satisfy federal, state, and local laws for the protection of public health, safety, welfare,
the environment, and natural resources; and a minimum amount of water necessary to
maintain support and sustain the economy of the state, region, or area.
SECTION 2. G.S. 143-355 is amended by adding a new subsection to read:
"(o) Basinwide Hydrologic Models. - The Department shall develop a basinwide hydrologic
model for each of the 17 major river basins in the state as provided in this subsection.
(1) Definitions. - As used in this subsection:
a. "Ecological flow" means the stream flow necessary to protect ecological integrity.
b. "Ecological integrity" means the ability of an aquatic system to support and maintain a
balanced, integrated, adaptive community of organisms having a species composition,
diversity, and functional organization comparable to prevailing ecological conditions and,
when subject to disruption, to recover and continue to provide the natural goods and
services that normally accrue from the system.
c. "Groundwater resource" means any water flowing or lying under the surface of the earth
or contained within an aquifer.
d. "Prevailing ecological conditions" means the ecological conditions determined by
reference to the applicable period of record of the United States Geological Survey stream
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gauge data, including data reflecting the ecological conditions that exist after the
construction and operation of existing flow modification devices, such as dams, but
excluding data collected when stream flow is temporarily affected by in-stream construction
activity.
e. "Surface water resource" means any lake, pond, river, stream, creek, run, spring, or
other water flowing or lying on the surface of the earth.
(2) Schedule. - The Department shall develop a schedule for basinwide hydrologic model
development. In developing the schedule, the Department shall give priority to developing
hydrologic models for river basins or portions of river basins that are experiencing or are likely to
experience water supply shortages, where the ecological integrity is threatened or likely to
become threatened, or for which an existing hydrologic model has not been developed by the
Department or other persons or entities.
(3) Model. - Each basinwide hydrologic model shall:
a. Include surface water resources within the river basin, groundwater resources within the
river basin to the extent known by the Department, transfers into and out of the river basin
that are required to be registered under G.S. 143-215.22H, other withdrawals, ecological
flow, instream flow requirements, projections of future withdrawals, an estimate of return
flows within the river basin, inflow data, local water supply plans, and other scientific and
technical information the Department deems relevant.
b. Be designed to simulate the flows of each surface water resource within the basin that is
identified as a source of water for a withdrawal registered under G.S. 143-215.22H in
response to different variables, conditions, and scenarios. The model shall specifically be
designed to predict the places, times, frequencies, and intervals at which any of the
following may occur:
1. Yield may be inadequate to meet all needs.
2. Yield may be inadequate to meet all essential water uses.
3. Ecological flow may be adversely affected.
c. Be based solely on data that is of public record and open to public review and
comment.
(4) Ecological flow. - The Department shall characterize the ecology in the different river basins
and identify the flow necessary to maintain ecological integrity. The Department shall create a
Science Advisory Board to assist the Department in characterizing the natural ecology and
identifying the flow requirements. The Science Advisory Board shall include representatives
from the Divisions of Water Resources and Water Quality of the Department, the North Carolina
Wildlife Resources Commission, the North Carolina Marine Fisheries Commission, and the
Natural Heritage Program. The Department shall also invite participation by the United States
Fish and Wildlife Service; the National Marine Fisheries Service; representatives of
organizations representing agriculture, forestry, manufacturing, electric public utilities, and local
governments, with expertise in aquatic ecology and habitat; and other individuals or
organizations with expertise in aquatic ecology and habitat. The Department shall ask the
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Science Advisory Board to review any report or study submitted to the Department for
consideration that is relevant to characterizing the ecology of the different river basins and
identifying flow requirements for maintenance of ecological integrity. The Department shall
consider such other information, including site specific analyses, that either the Board or the
Department considers relevant to determining ecological flow requirements.
(5) Interstate cooperation. - To the extent practicable, the Department shall work with
neighboring states to develop basinwide hydrologic models for each river basin shared by North
Carolina and another state.
(6) Approval and modification of hydrologic models. -
a. Upon completion of a hydrologic model, the Department shall:
1. Submit the model to the Commission for approval.
2. Publish in the North Carolina Register notice of its recommendation that the
Commission approve the model and of a 60-day period for providing comment on the
model.
3. Provide electronic notice to persons who have requested electronic notice of the
notice published in the North Carolina Register.
b. Upon receipt of a hydrologic model, the Commission shall:
1. Receive comment on the model for the 60-day period noticed in the North Carolina
Register.
2. Act on the model following the 60-day comment period.
c. The Department shall submit any significant modification to an approved hydrologic
model to the Commission for review and approval under the process used for initial
approval of the model.
d. A hydrologic model is not a rule, and Article 2A of Chapter 150B of the General Statutes
does not apply to the development of a hydrologic model.
(7) Existing hydrologic models. - The Department shall not develop a hydrologic model for a
river basin for which a hydrologic model has already been developed by a person or entity other
than the Department, if the Department determines that the hydrologic model meets the
requirements of this subsection. The Department may adopt a hydrologic model that has been
developed by another person or entity that meets the requirements of this subsection in lieu of
developing a hydrologic model as required by this subsection. The Department may make any
modifications or additions to a hydrologic model developed by another person or entity that are
necessary to meet the requirements of this subsection.
(8) Construction of subsection. - Nothing in this subsection shall be construed to vary any
existing, or impose any additional regulatory requirements, related to water quality or water
resources.
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(9) Report. - The Department shall report to the Environmental Review Commission on the
development of basinwide hydrologic models no later than November 1, of each year."
SECTION 3. The first report required by G.S. 143-355(o), as enacted by Section 2 of
this act, is due no later than November 1, 2011.
SECTION 4. This act is effective when it becomes law.
In the General Assembly read three times and ratified this the 8th day of July, 2010.
s/ Walter H. Dalton
President of the Senate

s/ Joe Hackney
Speaker of the House of Representatives

s/ Beverly E. Perdue
Governor
Approved 1:52 p.m. this 22nd day of July, 2010

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APPENDIX B – NC Ecological Flows Science
Advisory Board Members and Other Contributors
Ecological Flows Science Advisory Board Members
1. Academic Research
Amy Pickle, Nicolas Institute for Environmental Policy Solutions, Duke University
2. Agriculture
Dr. Jeff Hinshaw, NC State University
Alternate – David Williams, NC Division of Soil and Water Conservation
3. Electric Public Utilities
Hugh Barwick, Duke Energy Carolinas
Alternate – Thomas Thompson, Duke Energy Carolinas
4. Environmental Non-Governmental Organizations
Sam Pearsall, Environmental Defense Fund
Alternate – Rebecca Benner, The Nature Conservancy
5. Local Governments
Linda Diebolt, Hazen & Sawyer
Alternate – Rusty Rozzelle, Mecklenburg County Land Use and Environmental Services
6. NC American Water Works Association (AWWA-WEA)
Jaime Henkels Robinson, CH2M HILL
7. NC Division of Water Resources (DWR)
Fred Tarver
Alternate – Ian McMillan
8. NC Division of Water Quality (DWQ) (integrated into NC DWR in August 2013)
No representation past August 2013
9. NC Environmental Management Commission (EMC)
No representation past August 2013
10. NC Forestry Association (NCFA)
Bill Swartley, Forestry Non-Point Source Branch, NC Forest Service – Department of
Agriculture & Consumer Services
Alternates – Peter Caldwell, USDA Forest Service & Tom Gerow, NC Forest Service -
Department of Agriculture & Consumer Services
11. NC Natural Heritage Program (NHP)
Judy Ratcliffe
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12. NC Marine Fisheries Commission (MFC)
Dr. Bob Christian, East Carolina University
Alternate – Kevin Hart, NC Division of Coastal Management
13. NC Wildlife Resources Commission (WRC)
Chris Goudreau
Alternate – Vann Stancil
14. US Geological Survey (USGS)
Tom Cuffney, USGS - Raleigh
Alternate – Holly Weyers, USGS - Raleigh
15. US Fish and Wildlife Service (USFWS)
Mark Cantrell, Asheville Field Office
Alternate – Sarah McRae, Raleigh Field Office
16. US National Marine Fisheries Service (NMFS)
Fritz Rohde
A list of the NC Ecological Flows Science Advisory Board members and alternates who have
served in the following capacities but are no longer serving or no longer serving in these roles
for various reasons follows. These members are listed here to recognize their contributions:
 Jessi Baker, NC Division of Marine Fisheries (Alternate to Dr. Bob Christian, East
Carolina University)
 Donnie Brewer, Environmental Management Commission – Water Quality and Water
Allocation Committees
 Cat Burns, The Nature Conservancy (Alternate to Dr. Sam Pearsall, Environmental
Defense Fund)
 Scott Chappell, NC Division of Marine Fisheries (Alternate to Dr. Bob Christian, East
Carolina University)
 Vernon Cox, NC Dept of Agriculture and Consumer Services (Alternate to Dr. Jeff
Hinshaw, NC State University)
 John Crutchfield, Progress Energy Carolinas
 Jim Mead, NC Division of Water Resources
 Amy Pickle, Environmental Management Commission – Water Quality and Water
Allocation Committees
 Steve Reed, NC Division of Water Resources (Alternate to Jim Mead, Division of Water
Resources)
 Arlene Roman, City of Gastonia (Alternate to Linda Diebolt, Local Governments)
 Jay Sauber, NC Division of Water Quality
EFSAB Working Groups
To further investigate certain topics outside the scheduled meetings of the EFSAB, ad hoc
working groups were formed. The EFSAB determined that these topics were worthy of further
investigation beyond the scheduled meetings. Another benefit of the working groups was the
involvement of outside subject matter experts, such as was accomplished with the formation of
the Coastal Ecological Flows Working Group. Each group then reported its findings during the
scheduled meetings of the EFSAB; their findings and recommendations are captured in the
meeting summaries. Writing teams developed and proposed sections of the EFSAB report to
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the board in order to ensure a comprehensive and complete report to the NC Division of Water
Resources. These working groups and their members are listed here to recognize their
contributions.
Ecological Flows Science Advisory Board Report Writing Teams
 Mark Cantrell, US Fish & Wildlife Service
 Dr. Bob Christian, East Carolina University
 Thomas Cuffney, US Geological Survey
 Linda Diebolt, Hazen & Sawyer
 Chris Goudreau, NC Wildlife Resources Commission
 Jeff Hinshaw, NC State University
 Sarah McRae, US Fish & Wildlife Service
 Jim Mead, Environmental Defense Fund
 Sam Pearsall, Environmental Defense Fund
 Amy Pickle, Nicolas Institute for Environmental Policy Solutions, Duke University
 Judy Ratcliffe, NC Natural Heritage Program
 Jaime Robinson, NC American Water Works Association
 Fred Tarver, NC Division of Water Resources
 Tom Thompson, Duke Energy Carolinas
Ad Hoc Research Water Coordination Group
 Thomas Cuffney, US Geological Service
 Mary Davis, Southeast Aquatic Resources Partnership
 Robert Dykes, RTI International
 Michele Cutrofello Eddy, RTI International
 Chris Goudreau, NC Wildlife Resources Commission
 Phillip Jones, RTI International
 Ian McMillan, NC Division of Water Resources
 Jim Mead, EDF Volunteer
 Rua Mordecai, SALCC
 Lauren Patterson, RTI International
 Sam Pearsall, Environmental Defense Fund
 Jennifer Phelan, RTI International
 Fred Tarver, NC Division of Water Resources
Coastal Ecological Flows Working Group
 Dr. Bob Christian, East Carolina University, Chair
 Eban Bean, East Carolina University
 Dean Carpenter, Albemarle-Pamlico National Estuary Partnership
 Scott Ensign, AquACo
 Mike Griffin, East Carolina University
 Kevin Hart, NC Division of Coastal Management
 Mike O'Driscoll, East Carolina University
 Mike Piehler, University of NC Institute of Marine Science
 Judy Ratcliffe, Natural Heritage Program
 Fritz Rohde, National Oceanic and Atmospheric Administration
 Bennett Wynne, NC Wildlife Resources Commission

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Threatened and Endangered Species Working Group
 Mark Cantrell, US Fish & Wildlife Service
 Chris Goudreau, NC Wildlife Resources Commission
 Sarah McRae, US Fish & Wildlife Service
 Judy Ratcliffe, NC Natural Heritage Program
Guest speakers
Experts on various topics contributed their time to help the EFSAB learn about ecological flows
science. The following people provided educational presentations to the EFSAB at their
meetings.
 Mark Anderson, The Nature Conservancy
 Dr. Bob Christian, East Carolina University
 Tom Cuffney, US Geologic Survey
 Michelle Eddy, RTI International
 Mary Davis, Southern Instream Flow Network
 Robert Dykes, RTI International
 Tom Fransen, NC Division of Water Resources
 Mary Freeman, USGS Patuxent Wildlife Research Center
 Chris Goudreau, NC Wildlife Resources Commission
 Philip Jones, RTI International
 Jim Mead, NC Division of Water Resources
 Kimberly Meitzen, The Nature Conservancy
 Brian McCrodden, Hydrologics
 Thomas Payne, Normandeau Associates
 Sam Pearsall, Environmental Defense Fund
 Jennifer Phelan, RTI International
 Fred Tarver, NC Division of Water Resources
 Ty Ziegler, P.E., HDR/DTA
Facilitation Team
A facilitation team, administered by the Natural Resources Leadership Institute, convened in
October 2010. Based on the charter of the EFSAB and guidance from the board, the facilitation
team managed the meetings of the EFSAB and provided project support to the board and DWR.
 Mary Lou Addor, EdD, NC State University Cooperative Extension – Natural Resources
Leadership Institute
 Christy Perrin, NC University Cooperative Extension – Watershed Education for
Communities and Officials
 Nancy Sharpless, Natural Resources Leadership Institute
Recognition of a former facilitation team member for his earlier contributions to the process
(October 2010–August 2012).
 Patrick Beggs, NC University Cooperative Extension

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APPENDIX C – Recommendations for Establishing
Ecological Flows in Coastal Waterways
Membership of Coastal Ecological Flows Working Group (CEFWG)
Bob Christian, ECU, chair
Eban Bean, ECU
Dean Carpenter, APNEP
Scott Ensign, AquACo
Mike Griffin, ECU
Kevin Hart, NC DMF
Mike O'Driscoll, ECU
Mike Piehler, UNC IMS
Judy Ratcliffe, Natural Heritage
Fritz Rohde, NOAA
Bennett Wynne, NC Wildlife Resources

(We refer the reader to more detailed summaries of coastal ecological flows activities as part of
the EFSAB minutes located on the NC Division of Water Resources website.)
Summary
The low elevation, flat terrain and proximity to tidal, saline waters combine to prevent the use of
current hydrologic models in the coastal plain. Different approaches to ecological flows from
those described are required, although we lack detailed understanding to provide specific
protocols for this region. A more general framework is recommended that categorizes coastal
plain streams and identifies four ecological flow approaches to be considered based on stream
category. The approaches include extension of the state-wide flow-by criteria; condition of
habitat, primarily for anadromous fish; downstream salinity; and overbank flow. Each stream
category may be subjected to more than one, but not all, approach. We propose that agencies
and organizations within and outside of DENR form a joint committee to further develop this
framework.
Uniqueness of coastal ecosystems with respect to ecological flows
Progressing from the piedmont to the coast, streams and rivers become more distinct from
those in other regions of the state based primarily on their (1) hydrogeomorphology and
hydrodynamics, (2) ecology, and (3) human modifications. Key hydrogeomorphic and
hydrodynamic features arise from the flat terrain, low elevation, and tidal influence. Flat terrain
and low elevation result in the inundation of extensive riparian swamps, while tidal influence
disconnects watershed runoff from being the sole factor affecting river stage. Tides influence
coastal rivers far upstream from the saline estuary resulting in tidal freshwater reaches whose
hydrology is fundamentally different from rivers in the Piedmont and Mountain regions. Tides
may be dominated by astronomical conditions or wind with the latter being more important in the
enclosed sounds of the Northeast. These factors result in the potential disconnect between
stage and flow and the strong link between flow and water quality (i.e., salinity and dissolved
oxygen concentration). Modeling approaches used for the rest of the state for ecological flows
do not apply in some coastal plain streams. In addition ground water and surface water are
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more intimately connected than farther inland. Ecology of coastal waterways includes
communities highly influenced by nekton that spend much of their life history within estuarine
and ocean waters. Also, submerged aquatic vegetation and riparian wetland trees are integral
parts of the ecosystem as foundation species. Finally, current and historical industries have
altered the hydrology and ecology of the region. Wetlands have been replaced by linear
drainage ways for agricultural production, while more recent desalinization and mining have
discharged concentrated brine and depleted groundwater levels, respectively. In summary, the
combination of these factors necessitates different approaches to modeling ecological flows
than are being used in other regions of the state.
Objectives of Coastal Ecological Flows Working Group (CEFWG)
The overall objective of the CEFWG was to assess the general ability to establish an ecological
flows approach for coastal streams, recognizing that a formal recommendation of an approach
was unlikely. Rather, the CEFWG has provided a framework for establishing an ecological
flows approach. The following summarizes the steps to meet the objective:
 Assess applicability of previous coastal work
o Other states
o Greenville Study
 Develop stream typology
 Advance spatial modeling and mapping
 Establish relevant ecological and biological dependencies on flow
 Develop frameworks for potential coastal ecological flows criteria and protocols if
possible
 Identify factors limiting ecological flows protocols and needed research within coastal
systems
Details are provided in the presentation summary at the EFSAB meeting on July 17, 2013.
Stream typology (led by Scott Ensign)
The coastal plain river network exists with gradients of slope, elevation, influences of tides and
seawater intrusion, and degree of human alteration. A stream typology was considered
necessary to incorporate recognition of these gradients into ecological flows decision making.
The typology in Figure 1 was established as a simplification of a more complex one. It is used
to classify reaches under consideration for water flow modifications. The typology identifies
several major classification factors: origin, slope or gradient, and wind or tidal influence on
stage. These factors are presented as a decision tree that has been used to identify
approaches to ecological flows assessment. It should be stressed, however, that there may be
no clear demarcation between one category and another, but rather a continuum of influences
from the different factors.
This typology highlights two important features of coastal plain rivers. First, ecological flow
models based on stage-habitat relationships cannot be used in tidal freshwater rivers. Instead
of controlling river stage, discharge emanating from upstream primary controls the upstream
intrusion of saltwater. Therefore, ecological flow modeling in tidal freshwater rivers should focus
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on the effects of flow modification on saltwater intrusion, not on flow affecting the availability of
submerged habitat within the channel.
Second, stage-habitat relationships like those used in other regions of the state may be
modified for use in the non-tidal coastal plain rivers. However, unlike the models used in other
regions, it is necessary to account for the habitat provided by riparian wetlands. Riparian
wetlands and swamps occupy a large portion of the coastal plain, are inundated for long period
of the year, are highly connected with the hydrology of the channel, and are critical to the
ecology of coastal plain rivers.

Figure 1. Typology of coastal streams proposed for Coastal Ecological Flows approach
decision making.
Spatial modeling and mapping (led by Mike Griffin and Eban Bean)
An initial effort was made to map the typology and other characteristics of the coastal plain and
its waterways. Existing data sources were researched and integrated to evaluate the accuracy,
relevancy, and applicability for coastal waterway classifications. Key characteristics mapped
were (1) the position of the upper and lower coastal plain, (2) origin of waterways, (3) slope
categorization, (4) region of tidal influence, and (4) extent of the salt and freshwater interface. A
summary map is provided (Figure 2), representing the combination of key features.
Key features were addressed based on the typology shown in Figure 1. The upper and lower
coastal plain were initially divided by the Suffolk Scarp (Altor et al. 2005). However, this
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separation was considered to be of less importance to ecological flows than initially proposed.
Mapping was done without this distinction. Waterway origins were manually digitized based on
the western boundary of the Coastal plain (Fenneman and Johnson 1946). Stream slopes were
determined using USGS 30m resolution digital elevation models (Gesch 2007, Gesch et al.
2002), a stream dataset provided by Kimberly Meitzen of the Nature Conservancy. The slopes
varied over several orders of magnitude. A potential threshold between low and medium slope
was set at 2.5 mm/m. This threshold places most medium slope streams in the Sand Hills,
upper coastal plain, Cape Fear watershed and some tributary streams. The region of tidal
influence was designated to be all streams below 1 m in elevation. This threshold appears to
generally conform to observations on the Roanoke by Stanley Riggs and Dorothea Ames. The
extent of the salt and freshwater interface was estimated by waters classified by the former NC
Division of Water Quality (NCDWQ) to be “saltwater” (SA, SB, and SC). Note that the chosen
thresholds are proposed to initiate further discussion and consideration.

Figure 2. Summary map of key features for consideration of coastal ecological flows.
Relevant ecological and biological dependencies on flow
The CEFWG considered the aquatic communities and ecosystems of the coastal plain and
focused attention on two major assemblages: nekton and plant, foundation species. Nekton
were characterized as anadromous, catadromous, estuarine or resident species. The former
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two migrate and spawn in freshwater or the ocean, respectively. The final group tends to grow
and develop in the same general area.
Of particular interest are the anadromous fish species. These are ecologically and economically
important. Many of these species are important to the food web, acting as key links to primary
production or as top predators. Both commercial and recreational fisheries are dependent on
some of these species. A large database for them exists within the state as a result.
Furthermore, habitat suitability models are available for most species. One critical aspect of
habitat is flow. Flow is important to fish spawning and for the times of larval and juvenile
growth and development. During these times, flow helps establish the position of the salt
wedge and the extent of the freshwater habitat. Dissolved oxygen is another important aspect
of habitat suitability related to flow. The Coastal Habitat Protection Plan (CHPP, Deaton et al.
2010) of the NC Division of Marine Fisheries (DMF) identifies some of these factors for specific
anadromous fish species (Table 1 from Deaton et al. 2010). Other studies and environmental
management actions concern flow requirements for these species. For example flow relations
to habitat suitability for the Roanoke River have been established and incorporated into its
environmental management.
Anadromous species range across a wide geographic area from conception to adulthood and
spawning. Resident species have a narrower range of existence. Many species tend to reside
in the lower coastal plain and specifically within the wind/tidal influenced waterways.
Table 1. Physical spawning (adult) and egg development requirements for resident freshwater
and anadromous fishes inhabiting coastal North Carolina [Reproduced from Deaton et al. 2010].
[S] = Suitable and [O] = Optimum.
Species
Salinity (ppt) Temperature (C) Dissolved oxygen
(mg/l) Flow (cm/s) Other parameters
Adult Spawn/
Egg Adult Spawn/
Egg Adult Spawn/
Egg Spawning Spawn/Egg
Alewife [S] 0-5 [S] 0-5
[O] 0-2 [S] 11-28
[O] 17-21 [S] ≥3.6 [S] >4 [O] slow
current
[S] suspended
solids <1000 mg/l
American
shad [S] 0-18 [S] 0-18 [S] 10-30 [S] 13-26 [S] >5 [S] 30-90
Blueback
herring [S] 0-5 [S] 0-22
[O] 0-2 [S] 14-26
[O] 20-24 [S] >5 [O] strong
current
[S] suspended
solids <1000 mg/l
Striped
bass [S] 0-5 [S] 0.5-10 [S] 20-22 [S] 12-24
[O] 18-22 [S] >5 [S] 30.5-500
[O] 100-200
Yellow
perch [S] 0-13 [S] 0-2 [S] 6-30 [S] >5 [S] suspended
solids <1000 mg/l
White
perch [S] 5-18 [S] 0-2 [S] 10-30 [S] 12-20 [S] >5 [S] suspended
solids <100 mg/l
Sturgeon,
Atlantic [S] 0- >30 [S] 0-5 [S] 0- >30 [S] 12-20
Sturgeon,
Shortnose [S] 0- >30 [S] 0-5 [S] 0- >30 [S] 5-15
C-6
Riparian wetlands are an integral part of the aquatic ecosystems of the coastal plain. Overbank
flow into these wetlands provides water and nutrients to the forests and marshes, but extended
flooding in summer can deplete dissolved oxygen that stresses organisms in the wetlands.
Swamps dominate the freshwater riparian wetlands and serve as nursery areas and habitat for
a variety of aquatic invertebrates, finfish, and birds. Trees act as foundation species for these
ecosystems by providing key habitat characteristics of shade, soil stability, and
evapotranspiration.
Some coastal plain streams possess submerged aquatic vegetation that also act as foundation
species. These species provide habitat for its community and stabilize sediment. The position
and extent of these species is flow dependent, in part because of the flow controls the upstream
extent of salinity intrusion, and thus alters the habitat requirements for submerged species.
The aforementioned assemblages of organisms provide links between flow and ecological
integrity for ecological flow assessments. Individual categories of streams listed in Figure 1 can
be expected to be associated with different assemblages (Table 2). Anadromous fish are an
important component of the ecosystems of most categories. Plant foundation species are
important in low gradient and wind/tidal influenced systems. Resident species of nekton may be
another key to ecological flows of wind/tidal influenced systems. The assemblages at this scale
are the same for low gradient streams within both the lower and upper coastal plain. Therefore
these two categories were merged for later considerations.
Table 2. Link between waterway category and key assemblage that could be used for
ecological flow assessment.
Origin Slope
Assemblage
Anadromous
Fish Resident Fish
Vegetation
(Foundation
species)
Piedmont Medium gradient X
Upper Coastal Plain Medium gradient X
Upper Coastal Plain Low gradient X X
Lower Coastal Plain Low gradient X X
Lower Coastal Plain Wind or tidal driven flow X X

Framework for potential coastal ecological flows criteria and protocols
It is quite evident that assessing ecological flows within the coastal plain is problematic and
requires multiple approaches and approaches beyond those available for the piedmont and
mountains. We do not have the knowledge at this time to identify quantitatively specific
approaches. Rather we propose a framework that includes three potential directions for which
quantitative approaches could be established. This framework is based on the relationship
between flow, stage and salinity – all of which relate to habitat and ecosystem functions. Stage
depends less on gradient related flow with lower elevation and proximity to the coast. Within
stream habitat, volume depends on both stage and flow. Riparian, wetland habitat depends on
stage and hence overbank flow. Position of salinity and extent of freshwater along a river
depend on both flow and stage. These factors have been integrated into 4 ecological
determinants from which assessment approaches can be established: (1) extension of whatever
approaches endorsed by the EFSAB for piedmont streams, (2) direct discharge/habitat
relationships based on CHPP and related guidelines, (3) position of a prescribed salinity or
amount of salinity at a prescribed position, and (4) pattern of overbank flow. These are
associated with the different waterway categories in Table 3.
C-7
Table 3. Categories of waterways within the coastal plain and relevant ecological flows
determinants.
Origin Slope
EF determinant
EFSAB
extension
Discharge &
Habitat
Downstream
salinity
Overbank
flow
Piedmont Medium gradient X X X
Coastal Plain Medium gradient X X X
Coastal Plain Low gradient X X X
Coastal Plain Wind or tidal driven flow X X

Ecological flow relationships as proposed by the EFSAB, similar to those proposed for other
regions, may be used where discharge and stage are still closely correlated. Water level
stations exist within the coastal plain below modeled reaches of streams with piedmont reaches.
These water levels could be correlated with the upstream or nearby flows from gage stations.
When correlation meets some criteria of pattern similarity, regression can be used to extend
known flows to ungaged reaches.
Flow requirements and recommendations for the viability of living aquatic resources have been
developed for eastern North Carolina and for specific river basins. The NC DMF has developed
the CHPP based on the concept of protecting habitat for protection of living marine resources,
especially fish and shell fish. The fish and shell fish of concern include those discussed here.
This approach has goals similar to the efforts of the EFSAB. The DMF should be directly
engaged in establishing an approach based on CHPP and other environmental management
plans. This action should also include plans to protect threatened and endangered species.
Salinity is a key water quality factor dependent on flow. Organisms have different physiological
tolerances and dependencies for salinity that may vary with life stage. These in turn affect
reproductive, developmental and other ecological success. Further, salinity distribution is linked
to the potential for low dissolved oxygen conditions, especially in bottom waters. Affected
organisms include both animals and plants. Foundation and keystone species can be affected.
Either position of a prescribed salinity or the salinity at a prescribed position has been used by
other states to index ecological flows. A recent study on the effects of future water withdrawals
in Greenville, NC, used salinity within the Tar River as its indicator of effect. The study should
provide insight into how this factor could be used for assessing ecological flow effects.
Overbank flow is dependent on stage with varying dependence on discharge associated with
location and elevation of a reach. Riparian, freshwater wetlands are often inundated during
colder months and dry or infrequently flooded during warner months. This pattern is needed to
maintain community structure and ecosystem function of these wetlands. Blackwater streams
from high dissolved organic matter concentrations and low DO flushed from wetlands, along
with slow velocities, drive unique characteristics. Ecological flows within the coastal plain thus
should address the ecological integrity of these wetlands more than what might be expected for
the piedmont or mountains.
Process for moving forward
The framework presented here advances the assessment of ecological flows within the coastal
plain but not to the extent of that in other regions. It represents a way forward, but requires
further understanding of the relationships that control ecological flows and institution of
assessment approaches. These can be provided by the resources of North Carolina. No one
program within the state has the expertise or resources to fully advance and refine the
C-8
framework. It will take coordination and cooperation of the agencies within DENR and the
research community.
Several agencies within DENR can contribute expertise and effort to the cause. The Albemarle
Pamlico National Estuary Program (APNEP) has ecological flows as a primary mission within its
Comprehensive Conservation and Management Plan (2012). Its research director, Dean
Carpenter, participated in the CEFWG and APNEP is prepared to further the work of the
working group, at least for the watersheds of the Albemarle and Pamlico Sounds. The DMF and
NC Wildlife Resources have expertise on the key species and habitats of coastal North
Carolina. CHPP, fisheries management plans and habitat suitability models should be applied
to the ecological flows. The expertise of what were the Division of Water Quality and Division of
Water Resources is essential to extending both ecological condition of coastal ecosystems and
the hydrological modeling. The Ecosystem Enhancement Program (EEP) also would have
interest and relevant expertise. The Water Resources Research Institute (WRRI) and Sea
Grant Program (SG) at NC State University provide a connection to the research community.
All of these agencies have an interest and stake in ecological flows within the coastal plain that
go beyond the immediate legislative needs directing the EFSAB.
Representatives of state agencies and others should meet to determine (1) general goals and
objectives, (2) their needs within this topic, (3) expertise and resources available from each, and
(4) a plan to move to achieve both general and individual goals. Once these agencies can
establish their aggregated objectives and general approach, other organizations can be invited
to participate. Other contributors should include various willing partners who participated in the
EFSAB (e.g., industry and agricultural groups, federal and local government entities,
environmental groups). This should include RTI, which did not have membership on the Board
but contributed greatly. Initial leadership should come from someone associated with EFSAB
activities, but once a path forward is determined, this requirement may not be necessary.
Coincidental to this activity should be the stimulation of research directed toward ecological
flows within the coastal plain. WRRI and SG would be the likely sources of funds for this action,
but other agencies may have more directed funding opportunities. Below is a list of research
needs developed by the CEFWG and EFSAB.
Suggested research within coastal systems
Considerable information is needed before a quantitative approach can be established for the
coastal plain. Below is a list of research or development that would benefit this effort.
1. Determine correspondence of known discharge patterns with nearby coastal plain
stream flow patterns.
2. Determine the upper-most extent of tidal influence across coastal plain.
3. Evaluate juvenile abundance indices vs. flow and salinity/conductivity.
4. Map salinity distribution across coastal plain.
5. Quantify stream typology classes.
6. Evaluate Roanoke slabshell and other mussel distributions and abundance as
informative of salinity and flow patterns.
C-9
7. Determine hydrologic metrics and characteristics of coastal streams.
8. Determine reference flow regimes for each river basin.
9. Assess the balance of withdrawals from and discharges to coastal streams.
Literature Cited
Albemarle-Pamlico National Estuary Partnership. 2012. Comprehensive Conservation and
Management Plan 2012-2022. NC Department of Environment and Natural Resources,
Raleigh, NC.
Ator, S. W., J. M. Denver, D. E. Krantz, W. L. Newell, and S. K. Martucci. 2005. A Surficial
Hydrogeologic Framework for the Mid-Atlantic Coastal Plain. USGS Professional Paper 1680.
Reston, VA.
Deaton, A.S., W.S. Chappell, K. Hart, J. O‘Neal, B. Boutin. 2010. North Carolina Coastal Habitat
Protection Plan. North Carolina Department of Environment and Natural Resources. Division of
Marine Fisheries, NC. 639 pp.
Fenneman, N.M., and Johnson, D.W. (1946) Physiographic divisions of the conterminous U. S.
Reston, VA: U.S. Geological Survey.
Gesch, D.B., 2007, The National Elevation Dataset, in Maune, D., ed., Digital Elevation Model
Technologies and Applications: The DEM User’s Manual, 2nd Edition: Bethesda, Maryland,
American Society for Photogrammetry and Remote Sensing, p. 99-118.
Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., and Tyler, D., 2002, The National
Elevation Dataset: Photogrammetric Engineering and Remote Sensing, v. 68, no. 1, p. 5-11.

D-1
APPENDIX D – Flow Alteration – Biological
Response Relationships to Support the Determination
of Ecological Flows in North Carolina

FLOW ALTERATION – BIOLOGICAL RESPONSE RELATIONSHIPS
TO SUPPORT THE DETERMINATION OF ECOLOGICAL FLOWS IN
NORTH CAROLINA

A Final Report prepared for Environmental Defense Fund, North Carolina
Department of Environment and Natural Resources, and North Carolina Wildlife
Resources Commission

By RTI International and U.S. Geological Survey

ii
Contents
1. INTRODUCTION ......................................................................................................................................... 1
2. STREAM CLASSIFICATION .......................................................................................................................... 3
2.1 EFS Stream Classification System .................................................................................................. 3
2.2 Biological-Environmental Classification (BEC) System .................................................................. 4
Methods ................................................................................................................................ 5 2.2.1
Data ................................................................................................................................................... 5
Aquatic Biology Data ..................................................................................................................... 5
Environmental Data .................................................................................................................... 10
Analysis Approach ........................................................................................................................... 11
Results ................................................................................................................................. 12 2.2.2
Fish .................................................................................................................................................. 12
Macroinvertebrates ........................................................................................................................ 14
2.3 Conclusions ................................................................................................................................. 15
3. FLOW–BIOLOGY RELATIONSHIPS ............................................................................................................ 16
3.1 Methods ...................................................................................................................................... 16
Biological Metrics ................................................................................................................ 16 3.1.1
Hydrologic Foundation ........................................................................................................ 16 3.1.2
Flow Metrics ........................................................................................................................ 18 3.1.3
Statistical Analysis Approach .............................................................................................. 21 3.1.4
Step 1: Quantile or Upper-Limit Regressions .................................................................................. 21
Quantile Regression .................................................................................................................... 21
Upper-Limit Regression ............................................................................................................... 23
Step 2: Linear or Non-Linear Regressions ....................................................................................... 24
Step 3: Relationships by Regional Classification or State ............................................................... 24
Step 4: Normalized or Raw Biological Data ..................................................................................... 26
3.2 Results ......................................................................................................................................... 31
3.3 Conclusions ................................................................................................................................. 37
4. ECOLOGICAL FLOW FRAMEWORK .......................................................................................................... 38
4.1 Proposed Ecological Flow Framework ........................................................................................ 38
4.2 Example Application of the Ecological Flow Framework ............................................................ 39
4.3 Implementing the Ecological Flow Framework in the Upper Neuse River ................................. 41
5. ACKNOWLEDGEMENTS ........................................................................................................................... 48
6. REFERENCES ............................................................................................................................................ 49

iii
Figures
Figure 1. The five steps of the ELOHA framework (from Poff et al., 2010) ............................... 1
Figure 2. Flow-based habitat guilds used to classify NC fish species ........................................ 7
Figure 3. NCDENR Stream Fish Community Assessment Program sampling sites used
in the development of BEC System ............................................................................ 7
Figure 4. NCDENR Benthic Macroinvertebrate Biological Assessment Unit sampling
sites used in the development of BEC System............................................................ 9
Figure 5. Amount of fish biological variability explained by a priori classification
systems ..................................................................................................................... 12
Figure 6. Kappa results for predicting a priori classifications with riffle-run fish guild
data (a kappa value > 0.4 can be considered ‘fair to good’ agreement) .................. 14
Figure 7. Availability of WaterFALL® hydrologic data in NC by River Basin ............................. 17
Figure 8. Schematic illustrating annual ecodeficit and ecosurplus for a site in the
Roanoke River Basin ................................................................................................. 20
Figure 9. Process of calculating quantile regressions for flow–biology relationships:
(A) Flow–biology data, (B) Algorithm partitioning data for quantile
regression, (C) Straight line fit to quantile................................................................ 22
Figure 10. Process of calculating the upper-limit regression for flow–biology
relationships: (A) Flow–biology data, (B) Partition x-axis into evenly spaced
increments and select 80th percentile of data points, (C) Linear regression
of selected data ........................................................................................................ 23
Figure 11. NCDDWR Stream Fish Community Assessment Program sample sites with
riffle-run fish guild species by EDU ........................................................................... 25
Figure 12. Benthic Macroinvertebrate Biological Assessment Unit sampling sites by
Omernik Level III ecoregion ...................................................................................... 26
Figure 13. Riffle-run fish guild species richness within each river basin and EDU .................... 28
Figure 14. Average benthic EPTr (25th, 50th, and 75th percentiles) within each Omernik
Level III ecoregion (open circles indicate all sites, closed circles indicate
sites with good (G) or excellent (E) site condition) .................................................. 29
Figure 15. Summer ecodeficit (25th, 50th, and 75th percentiles) at benthic monitoring
sites with excellent condition within each Omernik Level III ecoregion .................. 29
Figure 16. Responses of riffle-run fish guild diversity and benthic EPTr to annual
ecodeficit (fish and benthic biological condition on y-axis are scaled to
100%) ........................................................................................................................ 34
Figure 17. Responses of riffle-run fish guild diversity and benthic EPTr to winter
ecodeficit (fish and benthic biological condition on y-axis are scaled to
100%) ........................................................................................................................ 34
Figure 18. Responses of riffle-run fish guild diversity and benthic EPTr to spring
ecodeficit (fish and benthic biological condition on y-axis are scaled to
100%) ........................................................................................................................ 35

iv
Figure 19. Responses of riffle-run fish guild diversity and benthic EPTr to summer
ecodeficit (fish and benthic responses on y-axis are scaled to 100%) ..................... 35
Figure 20. Responses of riffle-run fish guild diversity and benthic EPTr to fall ecodeficit
(fish and benthic biological condition on y-axis are scaled to 100%) ....................... 36
Figure 21. Responses of riffle-run fish guild diversity and benthic EPTr to decreases in
annual average 30-day minimum flow (fish and benthic biological condition
on y-axis are scaled to 100%) ................................................................................... 36
Figure 22. Ecological Flow Framework applied to summer ecodeficit–benthic EPTr
flow-biology relationship .......................................................................................... 40
Figure 23. Ecological Flow Framework applied to summer ecodeficit–riffle-run fish
guild diversity flow–biology relationship .................................................................. 41
Figure 24. The Eno River State Park and the Neuse at Goldsboro catchments used in
the example applications of the Ecological Flow Framework .................................. 42
Figure 25 (A and B). Step-by-step application of Ecological Flow Framework (using the summer
ecodeficit – benthic EPTr flow-biology relationship) applied to stream
segment at Eno River State Park and the Neuse River at Goldsboro. ...................... 46
Figure 26. Ecological Flow Framework (using the summer ecodeficit – riffle-run fish
guild diversity flow-biology relationship) applied to stream segment at the
Eno River State Park and the Neuse River at Goldsboro (MGD = Millions of
Gallons per Day). The values in this figure are determined using the same
step-by-step approach described in Figure 25 (A) and (B). ...................................... 47

v
Tables
Table 1. The percent similarity between EFS stream classes determined using USGS
gage versus WaterFALL® hydrologic data .................................................................... 4
Table 2. Description of riffle-run fish guild metrics ................................................................... 8
Table 3. Description of benthic macroinvertebrate metrics .................................................. 10
Table 4. Environmental variables considered in the development of the BEC System .......... 11
Table 5. Stream class definitions based on upstream drainage area (adapted from
Olivero and Anderson, 2008) .................................................................................... 13
Table 6. Results of riffle-run fish guild indicator species analysis for a priori
classification systems ................................................................................................ 13
Table 7. ANOSIM showing the correspondence between invertebrate community
structure and regional classification systems with and without
consideration of stream size (*p-value = 0.001) ....................................................... 15
Table 8. Flow metrics initially considered for the development of fish and benthic
flow–biology relationships (n = 94) ........................................................................... 19
Table 9. Flow metrics selected to develop fish and benthic flow–biology
relationships (n = 6) .................................................................................................. 21
Table 10. Summary of the number of sites, flow–biology relationships and statistical
significance of the flow–biology relationships at the EDU and statewide
levels for riffle-run fish guild diversity in response to summer ecodeficits
(significant flow–biology relationships at the p<0.05 are in bold) ........................... 25
Table 11. Summary of the number of sites, flow–biology relationships and statistical
significance of the flow–biology relationships at the Omernik Level III
ecoregion and statewide levels for benthic EPTr in response to summer
ecodeficits (significant flow–biology relationships at the p<0.05 are in bold) ......... 26
Table 12. Maximum and 80th percentile of riffle-run fish guild metrics by river basin ............ 27
Table 13. Maximum and 80th percentile of benthic EPTr by Omernik Level III
ecoregion .................................................................................................................. 28
Table 14. Deviation between the observed and predicted benthic EPTr in the
Piedmont and Coastal Plains ecoregions using flow–biology relationships
developed with normalized and non-normalized benthic data (Deviation =
(predicted-observed)/observed*100%) .................................................................... 30
Table 15. Deviation between the observed and predicted riffle-run fish guild in the
Piedmont and Coastal Plains ecoregions using flow–biology relationships
developed with normalized and non-normalized fish data (Deviation =
(predicted-observed)/observed*100%) .................................................................... 31
Table 16 (A and B). Statewide quantile regression models (Y = A + BX) relating ecodeficit (X) to
biological responses (Y) for riffle-run fish guild diversity (the smaller
number of sites for the minimum 30 day flow flow-biology relationships is
due to only including sites with reductions in flow) ................................................. 32

vi
Table 17 (A and B). Statewide quantile regression models (Y = A + BX) relating ecodeficit (X) to
biological responses (Y) for benthic EPTr (the smaller number of sites for
the minimum 30 day flow flow-biology relationships is due to only including
sites with reductions in flow) .................................................................................... 33
Table 18. Ecological Flow Categories and Biological Response Thresholds for the
proposed Ecological Flow Category Framework to support the
determination of ecological flows in NC ................................................................... 39
Table 19. NCDENR Benthic Site Condition Classes and range of benthic EPTr biological
condition within each class ....................................................................................... 39
Table 20. Current (WaterFALL®) and OASISTM 2050 projected hydrologic condition and
associated benthic EPTr biological condition at the Eno River State Park
stream segment (MGD = Million Gallons per Day) ................................................... 43
Table 21. Current (WaterFALL®) and OASISTM 2050 projected hydrologic condition and
associated benthic EPTr in the Neuse River at Goldsboro (MGD = Million
Gallons per Day, “NA” indicates an increase in the annual average 30-day
minimum flow) .......................................................................................................... 44
Table 22. Current (WaterFALL®) and OASISTM 2050 projected hydrologic condition and
associated riffle-run fish guild diversity in the Eno River State Park stream
segment (MGD = Million Gallons per Day) ............................................................... 44
Table 23. Current (WaterFALL®) and OASISTM 2050 projected hydrologic condition and
associated riffle-run fish guild diversity in the Neuse River at Goldsboro
(MGD = Million Gallons per Day, “NA” indicates an increase in the annual
average 30-day minimum flow) ................................................................................ 45

1
1. INTRODUCTION
The North Carolina General Assembly enacted House Bill 143 (§ 143-350) in 2010, which directs the
North Carolina Department of Environment and Natural Resources (NCDENR) to develop hydrologic
models for each of the 17 river basins in North Carolina (NC)
(http://www.ncwater.org/data_and_modeling/eflows/H1743v7.pdf). This legislation tasks NCDENR to
characterize the ecology in each river basin in order to identify the flows necessary to maintain
ecological integrity. Ecological integrity refers to “the ability of an aquatic system to support and
maintain a balanced, integrated, adaptive community of organisms having a species composition,
diversity, and functional organization comparable to prevailing ecological conditions and, when subject
to disruption, to recover and continue to provide the natural goods and services that normally accrue
from the system [emphasis added]” (NC§ 143-350).
In order to achieve the goals of Bill 143, Session Law (SL) 2010-143 mandated NCDENR to create an
Ecological Flows Science Advisory Board (EFSAB) to advise NCDENR in the characterization of the aquatic
ecology of each river basin and the corresponding ecological flows needed to maintain ecological
integrity. The EFSAB is composed of stakeholders, per specification by SL 2010-143, with expertise in
aquatic ecology and who represent a range of interests regarding the identification and implementation
of ecological flows in NC. The recommendations of the EFSAB will be presented to the NC Environmental
Review Commission (NCERC) by November 1st, 2013.
Although increasingly being recognized by state and federal water managers as an important
management goal, the task of identifying and implementing ecological flows is relatively new. In 2010,
the five-step Ecological Limits of Hydrologic Alteration (ELOHA) was proposed as a framework for
identifying and implementing ecological flows (Poff et al., 2010) (Figure 1). However, at the time of its
release, the relationships between changes in streamflow and biological response presumed within the
ELOHA framework had not been empirically correlated, nor had the full end-to-end process been
demonstrated. Despite the lack of empirical evidence, the ELOHA framework has been widely embraced
and used by at least 10 countries and 18 states in the U.S. (Conserve Online, 2012).

Figure 1. The five steps of the ELOHA framework (from Poff et al., 2010)
The first step of ELOHA is to develop a hydrologic foundation of unaltered and current streamflow
conditions. An understanding of how flows have changed from baseline (e.g., unaltered) to current
conditions is critical to understanding the relationship between changes in flow (Step 3) and ecological
integrity (Apse et al., 2008; Poff et al., 1997). Flow alterations can occur in the five main components of
the hydrologic regime: (1) magnitude of flows, (2) timing of flows, (3) duration of flow events, (4)
frequency of events, and (5) the rate of change (Richter et al., 1996). One significant challenge in the
development of the hydrologic foundation is that the characterization of flows is often limited to
catchments with stream gages. Flow data are not available for most catchments, including many
catchments containing aquatic biology monitoring stations. Flow alterations at biological stations are
therefore often estimated with regression-based extrapolations that restrict flow measures to a single
component of the flow regime, magnitude. In addition, the flow extrapolations are often not able to

2
estimate flows under unaltered conditions nor can they be computed for current conditions on a daily
time step (Kendy, 2012).
Stream classification is proposed as the second step of the ELOHA framework. The purpose of
classifying streams is to allow the flow-biology relationships established with data from a limited
number of streams to be extrapolated to all streams sharing similar flow characteristics (Poff et al.,
2010). Streams are ideally classified by flow metrics that are biologically relevant and based on
unaltered conditions. Numerous institutions have followed the steps of ELOHA in sequence and
successfully developed a stream classification system (e.g., McManamay et al., 2012). However, in doing
so, researchers have encountered the unforeseen need to conclusively link stream classes to the spatial
distribution and assemblages of aquatic biology. If the stream classification does not have biologic
fidelity, then grouping streams may weaken and not strengthen the statistical strength of flow-biology
relationships, as was found for the middle Potomac River (Kendy, 2012; Middle Potomac Watershed
Assessment, 2011).
Step four of the ELOHA framework is the determination of flow-biology relationships. These
relationships form the main link between science and policy, as they are based on hypotheses
developed from a combination of existing hydro-ecological literature, expert knowledge, and field
studies (Poff et al., 2010). In many cases, this proposed approach has spawned elaborate, time intensive,
expert-driven efforts to establish relationships that range from qualitative to quantitative. Potential
limitations of such approaches are that the method is time and resource intensive and not easily
transferable. In addition, the credibility of flow-biology relationships based on best professional
judgment is subject to both scientific and political challenges.
A series of projects have been conducted in NC to support the determination of ecological flows for
the State. Although not explicitly adopted from the start, the projects have addressed all five steps of
the ELOHA framework (Figure 1), and the purpose of this report is to present the methods, results, and
conclusions of these key projects and how the challenges of the ELOHA framework were addressed. The
following sections specifically describe projects focused on stream classification, flow-biology
relationships, and a proposed Ecological Flow Framework to support ecological flow determinations.

3
2. STREAM CLASSIFICATION
Stream classification is the process of grouping streams within a geographic region into a set
number of classes. Stream classes are defined in such a way that all streams within a class exhibit similar
attributes. These attributes can be based on the physical, chemical, biological, ecological, and/or
hydrologic qualities of streams. In the ELOHA framework, stream classification is undertaken using
ecologically meaningful streamflow characteristics to describe streams in terms of hydrological and
ecological baselines (Poff et al., 2010).
Researchers have posited several benefits of incorporating stream classification into ecological flow
determination. Stream classification may improve the significance of statistical analyses by maximizing
attribute differences between different stream classes and minimizing differences within the same class.
Classification also provides justification for extending ecological flow thresholds to streams that lack
sampling data. Once flow-biology relationships and ecological flows have been established for a given
stream class, these relationships and flows can be adopted for any stream within the same class,
regardless of whether the site has been sampled for hydrology or biology. If the streamflow
characteristics used to define the stream classification system are ecologically significant, in theory,
streams in the same group should be similar in terms of biological assemblages and ecological response
to alternations in baseline flow regime. This second characteristic is known as the ‘flow-biology’
relationship (Poff et al., 1997). The following sub-sections describe the stream classification projects that
were conducted to support the determinations of ecological flows in NC, and the main conclusions from
these projects.
2.1 EFS Stream Classification System
In 2011, a stream classification system was developed to support the determination of ecological
flows in the streams and rivers of the State. This classification was developed by Environmental Flow
Specialists, Inc. (EFS) based on 108 hydrologic flow metrics hypothesized to be ecologically relevant
(Henricksen and Heasley, 2010). Data from a total of 231 gages were used in the analysis. Natural
baselines and altered time periods were identified from the hydrologic flow metrics for each sample
location; 185 gages were judged to have been minimally altered by human activities. A stream
classification scheme was then derived via statistical analyses utilizing principal component analysis
(PCA) and k-means clustering algorithms (Henricksen and Heasley, 2010). Six perennial stream classes
and one seasonal stream class were identified for the State based on a wide range of hydrologic
threshold values.
Following the development of the stream classification system, project reviewers began to question
whether the classes adequately captured the variation in aquatic assemblages present in the State. The
hydrologic metrics used in the classification process were judged a priori to be ecologically meaningful.
However, no study had been undertaken to determine whether or how the flow metrics influenced
aquatic biology and/or if the hydrology-based stream classes accurately mapped the geographic
distribution of aquatic biology assemblages. In other words, does a given aquatic species have a greater
probability of appearing in one versus other stream classes? To address this concern, a “biological
fidelity” study (hereafter referred to as “biofidelity” study) was conducted to determine whether the
hydrology-based stream classification developed by EFS also discriminated among biological
assemblages across the seven stream classes.
Briefly, the biofidelity study involved a step-wise analysis approach to evaluate relationships
between hydrologic stream classes and aquatic species and community assemblages. Fish community
and benthic macroinvertebrate data from the State were mapped to the National Hydrology Dataset
Plus (NHD+) catchments and EFS stream classes were determined for each catchment using hydrologic

4
data from U.S. Geological Survey (USGS) stream gages and data modeled with RTI International
Watershed Flow and Allocation (WaterFALL®) model (see page 16 of Section 3 for a description of the
WaterFALL® model). Through this process and a comparison of stream classes determined by gaged and
modeled data in 147 catchments, it was found that overall stream class correspondence between the
two hydrologic data sources was less than 50% (Table 1).
Table 1. The percent similarity between EFS stream classes determined using USGS gage versus
WaterFALL® hydrologic data
EFS Stream Classesa Percent USGS – WaterFALL® Similarity
B – Small Stable 93% (54/58)
C – Large Stable 67% (10/15)
F – Medium Stable 25% (2/8)
D – Small Flashy 10% (4/42)
A – Coastal 10% (2/21)
G – Intermittent 0% (0/2)
E – Piedmont River 0% (0/1)
Total 49% (72/147)
a Classes B, C and F represent stable streams and classes D, A and G represent flashier streams.

An investigation into the discrepancy between stream class determinations led to a finding that
many EFS stream classes were based on extremely sensitive flow metric thresholds. For several classes,
a less than 10% change in one hydrologic metric resulted in a change in stream class. These “jumps” in
stream class were found in comparisons of USGS gage and modeled hydrologic data and USGS gage data
from different periods of record. These results called into question the stability of the EFS stream
classification system; it would not be possible to confidently classify streams beyond the catchments
with USGS gages. Therefore, the biofidelity analysis did not proceed any further, and it was concluded
that an alternate stream classification system to support ecological flow determinations was required. A
description of the methods and results of the biofidelity study are available at
http://www.ncwater.org/files/eflows/sab/20121023/Biofidelity_Analysis_(RTI).pdf.
2.2 Biological-Environmental Classification (BEC) System
Based on the findings of the biofidelity analysis, the EFSAB determined the need for a stream
classification system that
 represents the assemblages and distribution of aquatic biology across the State
 is not based on sensitive threshold values
 results in classes that are consistent and reproducible using USGS stream gage and modeled
hydrologic data
 is easy to understand, implement, and map; and
 is applicable to all catchments throughout State.
These objectives served as the foundation of the Biological-Environmental Classification (BEC)
System project. The specific goals of the BEC System were to use an iterative cluster-classification
approach based on the geographic distributions of aquatic biota assemblages and associated
environmental (physiographic and hydrologic) attributes to produce a stream classification that

5
represented both biology and environmental attributes that influence hydrology. The below sub-
sections describe the methods, results, and conclusions of the BEC System project.
Methods 2.2.1
Data
A variety of aquatic biology and environmental parameters were used in the development of the
BEC System.
Aquatic Biology Data
Fish and benthic macroinvertebrates were selected as the aquatic taxa for the BEC System. Fish are
viewed as the top of the food chain “integrators” of the biological condition of a river system (Kendy,
2012) and have a relatively fast recovery time to climate events that may cause local extirpation. The
mobility, high trophic status, and preference for certain hydrologic conditions allow fish species to serve
as sensitive indicators of ecological integrity with hydrologic alterations (Bragg et al., 2005). Additionally,
fish are valued by wide segments of society and therefore often provide a relevant frame of reference
for achieving public acceptance (Poff et al., 2010). Benthic macroinvertebrates are useful for monitoring
change in flow because they are found in all waters, are sedentary, and are large enough to be easily
collectible (NCDENR, 2012). The sedentary nature of benthos ensures that benthic invertebrate
communities are responding to localized changes in hydrologic conditions. Benthic macroinvertebrate
sample diversity is also considered to be a reliable indicator of local water quality conditions (NCDENR,
2012). The NCDENR Stream Fish Community Assessment Program1
(http://portal.ncdenr.org/web/wq/ess/bau/ncibi-data) served as the source of fish data, and the
NCDENR Benthic Macroinvertebrate Biological Assessment Unit served as the source of the benthic
macroinvertebrate data (http://portal.ncdenr.org/web/wq/benthosdata). Both programs evaluate the
community composition of fish and benthos, and are therefore suitable for the development of the BEC
System.
Fish
Data on fish populations were extracted from the NCDENR Stream Fish Community Assessment
Program. This program collects fish data from “wadeable” streams. Fish site selection is non-random,
with the majority of sites being located at the most upstream, wadeable bridge crossing in a catchment.
The wadeability of a stream varies by location, season, and the amount of precipitation present during
the sampling year. Catchment samplings started in 1991 and river basins are on an alternating sampling
schedule so that each basin is sampled every five years. At present, over 918 sites have been monitored
by the Fish Community Assessment Program. The majority of sampling (73%) occurs in the spring (late
March to early June) (Bryn Tracy, Personal Communication, May 6, 2013). All fish samples are collected
following NC’s standard biological monitoring operating procedures (NCDWQ, 2006). The constraints of
the sampling methodology of the Stream Fish Community Assessment Program should be considered in
the interpretation of the results from the BEC System and flow-biology relationships (described further
in Section 3).
For the purposes of the BEC System, only a portion of the sites and data from the NCDENR Stream
Fish Community Assessment Program were used in the analyses. The NCDENR database includes sites
that were sampled multiple times over nearly two decades. Five hundred and seventy five sites were

1 Previous associated with NC Division of Water Quality (NCDWQ) within NCDENR.

6
sampled more than once between 1991 and 2011. Temporal and spatial filters were applied to the data
to reduce pseudoreplication (i.e., samples that are not independent) and spatial autocorrelation of
environmental variables (i.e., similarity among variables based on the proximity of samples sites)
(Armstrong et al., 2011; Hurlbert, 1984). First, for those sites with multiple samples taken over time,
only data from the most recent samples were maintained. This selection was based on the assumption
that the most current samples are most reflective of current streamflow conditions. Second, a spatial
filter flagging sites located within 0.5 km from each other was applied (Wenger et al., 2008). The
locations of the monitoring sites were based on reported latitude and longitude coordinates assigned to
National Hydrography Dataset Plus Catchment Identification (NHD+ COMID) and “snapped” to the
nearest NHD+ stream segment using ArcGIS. Sites that changed COMID’s during the “snapping” process
were assessed and placed on the correct stream, as indicated by the Stream Fish Community
Assessment Program site name. If the sites were sampled at the same time and located along the same
stream with no direct human alterations between sites (e.g., reservoir, withdrawal, or return), the most
downstream site was kept and the upstream site deleted (Armstrong et al., 2011). If the sites were
sampled at different time periods, were located in different streams, or had an instream alteration
between them, then both sites were kept in the analysis. There were 57 sites located within 0.5 km of
another site, of which 13 were deleted. In addition, all sites located downstream of a reservoir with
greater than twice the drainage area of the fish sample site’s drainage area were removed. This step was
taken to remove sites where flow conditions are dominated by artificial control structures. At the end of
the filtering process for the BEC System, 858 fish sampling sites were available for classification.
A total of 156 fish species have been recorded by the NCDENR Stream Fish Community Assessment
Program. Fish species differ in their habitat preferences (Persinger et al., 2011; Pyron & Lauer, 2004),
which are largely formed by hydraulic conditions. Experts from NCDENR, NC Wildlife Resources
Commission (NCWRC) and National Marine Fisheries Service adopted a system similar to Persinger et al.
(2011) and classified the 156 NC species based on their primary habitat preferences during their
spawning and adult life stages, considering combined sensitivity to streamflow velocity and depth
(Figure 2). The riffle-run guild species assemblage was selected to represent fish for the development of
the BEC System because it is known to be sensitive to both streamflow characteristics. Fish species that
rely on riffles as their habitat during spawning or adult/juvenile stages of their life cycle may be
significantly impacted by reduced flows. In addition, the riffle-run guild is well represented across the
State, with members of the riffle-run guild present in 667 of the 858 stream fish community sampling
sites (Figure 3). All of these sites were included in the BEC System analyses. However, only 649 sites
were included in the flow-biology relationships because 18 sites were located in zones of tidal influence
not modeled by WaterFALL® (described further in Section 3).

7

Figure 2. Flow-based habitat guilds used to classify NC fish species

Figure 3. NCDENR Stream Fish Community Assessment Program sampling sites used in the
development of BEC System
Data from the NCDENR Stream Fish Community Assessment Program included the number of fish
collected for each species identified. From these data, three biologic metrics were derived: abundance,
species richness, and the Shannon-Weaver Diversity Index (Mas-Riera et al., 1990; Gutierrez-Estrada et
al., 2008; Arias-Gonzalez et al., 2012) (hereafter referred to as “diversity index” or “riffle-run fish guild
diversity”) (Table 2). The diversity index is calculated by (Eq. 1):
∑( )( ( )) (Equation 1)
where H is the diversity index, R is the number of species present, and pi is the proportion of individuals
(abundance) belonging to the ith species at the site. The value of the diversity index increases with the
number of species present and as the “evenness” of the number of individuals belonging to each species
increases. The diversity index approaches zero as the abundance becomes more concentrated in a single
species.

8
Table 2. Description of riffle-run fish guild metrics
Metric Definition Pros Cons
Abundance Total number of fish
collected Easy to understand
Sensitive variable and
abundance may increase if
exotic species are present
Species
Richness
Total number of species
present
Easy to understand
More robust to change
Small range of variability
when examining only Riffle-
Run guild
Diversity
Index
Index of the evenness of
fish abundance distributed
among the species present
Complex for
management to
understand and
implement
Incorporates both abundance
and species richness
Benthic Macroinvertebrates
The NCDENR Benthic Macroinvertebrate Biological Assessment Unit served as the source of the
benthic macroinvertebrate data. Since 1978, the NC program has collected over 6,500 benthic
macroinvertebrate samples at 1,737 sites in both wadeable and non-wadeable
waters(http://portal.ncdenr.org/web/wq/benthosdata). Data on habitat, prevailing water quality
parameters, and benthos are collected at each sampling site to assess site conditions (NCDENR, 2012).
The macroinvertebrate data used in this report were collected using the NCDENR standard and swamp
sample collection methods, as described in the Standard Operating Procedures manual (NCDENR, 2012).
The NCDENR methods categorize invertebrate abundances as rare, common, and abundant. These
categorical abundances were converted to numeric values (1, 3, and 10, respectively) in accordance with
NCDENR procedures for calculating biological condition indices. The constraints of the sampling
methodology of the Benthic Macroinvertebrate Biological Assessment Unit should be considered in the
interpretation of the results from the BEC System and flow-biology relationships (described further in
Section 3).
For the purposes of the BEC System, benthic data were linked to NHD+ catchments and filtered
following the same procedures used for NC Stream Fish Community Assessment fish data. Through this
filtering process, a total of 1,328 benthic sites were available for the BEC System analysis (Figure 4).
Similar to fish, only 1,227 sampling sites were included in the flow-biology relationships because
WaterFALL® does not model tidally influenced streams (described further in Section 3).

9

Figure 4. NCDENR Benthic Macroinvertebrate Biological Assessment Unit sampling sites used in the
development of BEC System
Similar to fish, the individual benthic data were cleaned and grouped into assemblages and metrics
reflective of flow. Ambiguous taxa were removed from each sample by distributing the abundance of
ambiguous parents among their children in accordance with the relative abundance of the children
using the Distribute Parents Among Children (DPACs) method (Cuffney and Brightbill, 2010). The
resulting taxa-by-sample matrix was examined to assess the variability in identifications among samples
(e.g., taxa identified to species in some samples and genus in others). Identifications were standardized
across samples to provide a consistent dataset. Assemblage metrics were calculated using the attributes
file associated with the USGS Invertebrate Data Analysis Software (IDAS) software (Cuffney and
Brightbill, 2010). The attributes were optimized for the dataset using the southeastern tolerance and
functional group information augmented with national information (NCDENR, 2012; Barbour et al.,
1999). Information on streamflow velocity preferences were derived from Vieira et al. (2006). A total of
148 species groupings and metrics were considered. Spearman rank correlations between assemblage
metrics and flow metrics (see Section 3 for a greater description of flow metrics) were used to identify
benthic biological response metrics that warranted further investigation. These candidate metrics were
evaluated in terms of ecological significance, interpretability, data quality (e.g., abundance metrics were
derived from categorical data), response range, use in assessing water quality, and correlation with
hydrologic variables.
Three biologic response metrics were selected for further analysis: 1) Ephemeroptera, Plecoptera,
and Trichoptera richness (EPTr), 2) average tolerance (RichTOL), and 3) the number of taxa that prefer
fast velocities (FastVelR) (Table 3). EPTr is the number of taxa in the insect orders Ephemeroptera,
Plecoptera, and Trichoptera, which include some of the most alteration-intolerant species of benthos.
EPTr is widely used to assess changes in water quality and is sensitive to changes in habitat conditions.
RichTOL is the average tolerance of taxa in a sample. Tolerance values range from 0 (intolerant) to 10
(tolerant) and were derived from NCDENR (NCDENR, 2012) and the Environmental Protection Agency
(EPA) (Barbour et al., 1999). The third metric was the number of taxa in a sample that prefer fast stream
velocities (FastVelR) as defined by Vieira et al. (2006). These three metrics have the additional
advantage of being derived from measures of taxa richness. Consequently, they do not rely on
converting categorical abundances to numeric values as do metrics based on abundance. Of these three
metrics, EPTr had the best combination of response range, correlation with indicators of hydrologic
alterations, and relevance to water quality conditions. Therefore, EPTr was used to represent benthos in
the development of the BEC System.

10
Table 3. Description of benthic macroinvertebrate metrics
Metric Definition Pros Cons
EPTr Number of EPT taxa
collected at a site
Widely used by NCDENR
for site condition
assessments
Includes some tolerant forms
RichTol Average tolerance of
taxa in a sample
Ranks taxa by their
tolerance to pollution and
disturbance
Relatively narrow response
range, tolerance values can
vary by region and taxa level
Fast VelR
Number of taxa in a
sample that prefer fast
stream velocities
Closest benthic equivalent
to the riffle-run guild in
fish (see Section 3 for
further description)
Not well established in the
literature
Environmental Data
A large variety of mapable, statewide regional classification systems, and environmental and
physiographic attributes were evaluated for the BEC System to develop stream classes. Pre-existing
(hereafter referred to as “a priori”) regional classifications systems that were evaluated in the analyses
included Omernik Level III (Omernik, 1987) and IV (Griffith et al., 2002) ecoregions, Wolock hydrologic
landscape regions (HLR) (Wolock et al., 2004), Bailey ecoregions (McNab et al., 2005), Fenneman
(Fenneman, 1946) physiographic provinces, and The Nature Conservancy (TNC) Ecological Drainage
Units (EDU) (TNC, 2005). The classification systems represent distinct physiographic, ecological or
hydrologic regions at scales of 1,000 to 10,000 km2 (Higgins et al., 2005). At this scale, the interactions
among watershed boundaries, landscape features (e.g., elevation, geology), and climate (e.g.,
precipitation, temperature) influence broad patterns of aquatic ecosystems characteristics such as
channel morphology and hydrologic, temperature, and nutrient regimes (Higgins et al., 2005). Finer
scale differences across sample locations were also characterized using a range of environmental and
physiographic variables, watershed and stream channel characteristics, and climatic data regional
classification systems (Table 4). All data were assigned to the NHD+ catchments with benthic and/or fish
biological monitoring data.

11
Table 4. Environmental variables considered in the development of the BEC System
Variable Description Source
Elevation Elevation of sample site Digital Elevation Model
(DEM)
Channel Sinuosity NHD+ channel sinuosity of sample
reach
Calculated from NHD+
geometry
Slope Average slope of NHD+ catchment NHD+
Cumulative Upstream Drainage Total upstream drainage from sample
site NHD+
Average Precipitation 1 Year Lag Previous 1 year average precipitation
of climate grid NHD+
Average Precipitation Average precipitation of climate grid NHD+
Average Temperature 17 year average temperature NHD+
Percent Sand Percent soil sand in HLR unit Wolock et al. (2004)
Minimum Elevation Minimum elevation in HLR unit Wolock et al. (2004)
Relief Difference between min and max
elevation in HLR unit Wolock et al. (2004)
Percent Flat Total Total percent of zero-slope area in unit Wolock et al. (2004)
Percent Flat Upper Watershed Total percent of zero-slope area in
upper 50% of HLR unit Wolock et al. (2004)
Percent Flat Lower Watershed Total percent of zero-slope area in
lower 50% of HLR unit Wolock et al. (2004)

Analysis Approach
An iterative cluster-classification approach was applied to the biological and environmental data
from the NHD+ catchments with fish and/or benthic monitoring data. Clustering techniques can be
characterized as a form of ‘unsupervised learning;’ the user does not provide information regarding
expected relationships between observations. Instead, the algorithm tries to find the number of groups
within a data space such that differences among observations in the same group are minimized while
differences between groups are maximized. Because clustering algorithms are randomized, multiple
techniques and runs should be assessed so that the stability of results can be evaluated. The clustering
approaches tested in the development of the BEC System included: partitioning around medoids (PAM),
hierarchical agglomerative methods, and fuzzy clustering. In contrast, classification techniques are a
form of ‘supervised learning;’ the user specifies group membership at the start of the analysis and the
classification algorithm attempts to find combinations of predictor variables that best describe each
group. The result of a classification analysis is a series of unique threshold values that differentiate the
groups from one another. The classification techniques tested in the development of the BEC System
included: classification and regression trees, conditional inference trees, random forest classification,
and conditional inference forests.
The goal of the iterative analysis was to determine if the observed variability in the geographic
distribution of aquatic biologic assemblages could be explained by differences in site-specific
environmental and physiographic variables, or if the biological data itself could be partitioned in a way
that explained variation across the State. Prioritizing biology in this way ensures that classes are
characterized and/or tested by species data and that any significant relationships found between stream

12
classes and environmental variables are directly relatable to stream biology. In addition, biological
communities integrate physiochemical conditions across multiple temporal and spatial scales, which
allow them to be sensitive indicators to changes in environmental conditions (Brenden et al., 2008). Two
applications of the cluster-classification approach were attempted. In the first approach, several
different clustering algorithms were applied to the environmental data (Table 4) in order to generate
different numbers of classes. These classes were then tested against the associated biological data to
determine if the clusters explained the variability observed in species assemblages. Indicator species
analysis, classification, and non-parametric analysis of variance were used to assess the reasonableness
of the environmental groupings. In the second approach, clustering algorithms were applied to the
biological data itself, and the resulting classes were then grouped in terms of environmental variables
and physiographic regions and tested for significance. The biology-based clusters were also tested for
their ability to predict environmental and physiographic data.
Results 2.2.2
Fish
In general, the correspondence between independently derived environmental clusters and fish was
weak. A range of clustering techniques, class numbers, and environmental variables were tested; in all
cases, the ability of the resulting clusters to explain variability in the geographic distribution of aquatic
biology was inconsequential. In contrast, several of the a priori regional classification systems
demonstrated comparatively higher explanatory power, with the EDU and Omernik Level IV classes
showing the most promise for fish. However, the overall amount of unexplained variability remained
large (Figure 5).

Figure 5. Amount of fish biological variability explained by a priori classification systems
The a priori regional classifications were also examined for indicator species relevance (Dufrêne and
Legendre, 1997). Aquatic biology is known to be impacted by stream size. Therefore, the three
classification schemes with the largest explanatory power (Omernick Level III, Omernick Level IV, and
EDU) were partitioned by stream class to produce three additional classification systems for evaluation;

13
stream classes were determined by calculating the total upstream drainage area (Table 5. ), based on
the stream classification system developed by TNC for the northeastern United States (Olivero and
Anderson, 2008). Indicator species analysis considers species frequency, abundance, and fidelity relative
to a given classification scheme. An indicator species is one that occurs relatively frequently in only a
small number of classes. In the context of an environmental-biological classification system, indicator
species analysis provides another method of assessing how well a classification system describes unique
biological assemblages. The results of the indicator species analyses demonstrated that the EDU
classification and Omernik Level III ecoregions were best represented by unique species (
Table 6.); the larger number of classes in the Omernik Level IV classification made it more difficult to
identify unique species in individual levels since multiple levels occurred in relatively similar
environmental regions. For this reason, the inclusion of stream size, and the subsequent increase in
classification levels, did not improve the analysis results.
Table 5. Stream class definitions based on upstream drainage area
(adapted from Olivero and Anderson, 2008)
Stream Size Class Drainage Size
Headwater & creek < 100 km2
Small rivers > 100 km2 and <518 km2
Medium rivers > 518 km2 and < 2,590 km2
Mainstem rivers > 2,590 km2 and < 10,000 km2
Large rivers > 10,000 km2

Table 6. Results of riffle-run fish guild indicator species analysis for a priori classification systems
Classification System No. of Significant
Indicator Species
No. of Levels
Represented
% of Levels
Represented
EDU 92 11/11 100
EDU + Stream Size 24 8/43 19
Omernik Level III Ecoregions 114 4/4 100
Omernik Level III Ecoregions + Stream Size 13 7/18 39
Omernik Level IV Ecoregions 16 8/24 33
Omernik Level IV Ecoregions + Stream Size 28 6/74 8

Given these results, the EDU and Omernik Level III ecoregion classification systems were further
evaluated in terms of fish biological data. More specifically, the ability of the biological data to correctly
predict the membership of a sampling station was tested for each classification system. For this process,
a conditional inference tree model was fit using species abundance as the predictor variable and the
specific classes of a given regional classification as the response. In order to test the predictive power of
these models, the inference trees were fit many times to a randomly selected 80% of the data. The
resulting model was then used to predict the remaining 20% of the dataset that had not been utilized in
the model fitting process. A measure of classification accuracy, called a kappa statistic, was calculated
for each iteration. The results from these analyses further confirmed the EDU regional classification
(without stream size as an additional classification factor) as the best predictor of the riffle-run fish guild
abundance (Figure 6).

14

Figure 6. Kappa results for predicting a priori classifications with riffle-run fish guild data (a kappa
value > 0.4 can be considered ‘fair to good’ agreement)
Macroinvertebrates
For benthic macroinvertebrates, the PAM cluster method was used to investigate efficacy of dividing
the benthic macroinvertebrate data into 2 to 60 clusters. Similar to fish, the average silhouette widths
obtained from the PAM cluster analyses were substantially less than 0.25; the PAM analyses indicate
that the macroinvertebrate communities do not form discrete clusters. Instead, changes in community
structure (composition) are continuous across the State.
Similar to fish, the next step was to investigate the correspondence between a priori regional
classification systems (with and without stream size classes) and macroinvertebrate communities. This
correspondence was assessed using indicator species analysis (Dufrêne and Legendre, 1997) and
analysis of similarities (ANOSIM; Clarke and Gorley, 2006). ANOSIM is a nonparametric ANOVA
procedure that assesses the significance of a priori classes on the basis ofthe similarity in communities
among sites.
Indicator species analysis established that all Omernik Level III ecoregions (40 to 151 indicator taxa)
and EDU classes (3 to 72 indicator taxa) could be distinguised by the presence or absence of indicator
taxa. However, incorporating stream size (Table 7) into these classification systems dramatically reduced
the number of classes that had statistically significant indicator taxa. These results suggest that Omernik
Level III ecoregions and EDU may serve as the most suitablle classification systems for benthos and that
adding stream size classes does not improve the resolution of the regional classication systems.
ANOSIM (Clarke and Gorley 2006) tests the hypothesis that there are no assemblage differences
between groups of samples specified by the predefined classes as well as testing for differences
between pairs of classes. ANOSIM was used to analyzed the four classification systems (Omernik Level III

15
and EDU with and without stream size classes). The ANOSIM results showed that some classes were
significantly different (p = 0.001) within all four classification systems (Table 7). However, examination of
pair-wise comparisons indicated that only Omernik Level III ecoregions had classes that were all
significantly distinguishable. The number of distinquishable classes dropped with the inclusion of stream
size. Based on these analyses, Omernik Level III ecoregions (without stream sizes) provide the best
regional classification system for benthos in NC.
Table 7. ANOSIM showing the correspondence between invertebrate community structure and
regional classification systems with and without consideration of stream size (*p-value = 0.001)
Classification
Approach
Omernik Level III Ecoregion EDU
Overall
Correlation
% Significant
Pairs
Overall
Correlation
% significant
Pairs
Not Considering
Stream Size 0.475* 100 0.359* 95
Considering
Stream Size 0.440* 84 0.399* 75

A prior classification systems were also examined on the basis of benthic macroinvertebrate metrics.
Metrics aggregate individual taxa into ecologically meaningful groups based on ecological traits or
taxonomic characteristics. One hundred and forty-eight metrics were examined as part of the evaluation
of predefined classifications systems and for the examination of flow–biology relations. Classification
and Regression Tree (CART) analyses were used to determine the number of clusters that best
represented the macroinvertebrate assemblage metrics. The number of significant divisions generated
by the CART analyses was compared with the number of divisions (classes) in the four classification
systems to determine if the CART analyses supported the classification systems. Results from the CART
analyses showed that only the Omernik Level III classification system was consistent with the CART
analyses. The other systems defined more classes than could be supported using CART analysis.
2.3 Conclusions
Two stream classification approaches were explored to support the determination of ecological
flows in NC. The EFS Classification System, consistent with the ELOHA framework and based on
ecologically relevant flow metrics, was found to be too sensitive to changes in flow and not easily
transferred beyond catchments with USGS gage data. Similarly, a BEC System based solely on
physiographic, environmental, or biological data could not be easily developed. In all cases, the ability of
the developed classifications to explain the variability observed in the geographic distribution of fish and
benthic aquatic assemblages was weak. Statistically significant clusters could be derived, but the large
number of classes produced by the classification systems greatly lowered the management utility of
these approaches. The best approach for classifications appeared to be the use of a priori regional
classification systems without stream size classes. The EDU classification produced the best results for
the stream fish community dataset, while Omernik Level III ecoregions provided the best regionalization
of benthic macroinvertebrate assemblages. Section 3 discusses the utility of the EDU and Omernik Level
III ecoregion classes in the development of flow–biology relationships for the streams and rivers of NC.

16
3. FLOW–BIOLOGY RELATIONSHIPS
The purpose of flow–biology relationships is to quantify the change in ecological integrity associated
with flow alterations. Specific ecological flows or thresholds can then be determined from these curves
to maintain a level of desired ecological integrity. A project was conducted to empirically derive flow–
biology relationships using statewide spatially explicit aquatic biology databases paired with estimates
of flow alteration based on modeled flows under unaltered and current conditions. Using these data, a
cross-sectional analysis approach that replaces space for time was applied. In theory, with a large
enough dataset, the full range of flow alterations and corresponding biologic metrics will be well
represented and able to characterize aquatic communities and associated flow alterations (Poff and
Zimmerman, 2010; Carlisle et al., 2011). The following sections describe the methods, results, and
conclusions of the project work devoted to the development of flow–biology relationships to support
the determinations of ecological flows in NC.
3.1 Methods
The following sub-sections describe the methods adopted for each of the components of the flow–
biology relationships: biological metrics, hydrologic foundation, flow metrics, and statistical analysis
approach.
Biological Metrics 3.1.1
Biological metrics were used to characterize the biological condition of a stream and the response of
biology to changes in flow. As with the BEC System, fish and benthic macroinvertebrates were selected
to represent the biological condition of streams and rivers and to develop of flow–biology relationships
for NC. The diversity index of the riffle-run guild was chosen to represent the biological metric for fish
(Table 2), and EPTr was selected to represent the biological metric for benthic macroinvertebrates
(Table 3). As discussed in the previous section, the riffle-run fish diversity is hypothesized to be sensitive
to flow alterations and is well represented at monitoring stations across the State. Similarly, benthic
EPTr was found to have the best combination of response range, correlation with indicators of
hydrologic alterations, and relevance to water quality conditions. As outlined in Section 2, a total of 649
sites were used to develop the fish metrics and 1,227 sites were used to develop the benthic metrics.
Hydrologic Foundation 3.1.2
The hydrologic foundation consisting of baseline (i.e., unaltered) and current condition flows was
developed using the WaterFALL® model. This model enables interactive, quantitative investigation of
water availability at multiple geographic scales. It employs an enhanced version of a well-established
hydrologic model, the Generalized Water Loading Function (GWLF; Haith et al., 1992; Haith and
Shoemaker, 1987), which has been modified to run on EPA’s NHD+ stream network. WaterFALL®
functions as an intermediate-level, distributed hydrologic model that accounts for spatial variability of
the land surface as well as climatic forcing functions. The watershed model encompasses all major
components of the hydrologic cycle using the curve number method for computing runoff (SCS, 1986)
and a first-order depiction of infiltration loss to deep aquifer storage. Enhancements include the
representation of human interactions with the natural hydrologic system, thereby allowing for the
simulation of altered conditions and routing routines to transport water from upstream to downstream
through the catchment network.
WaterFALL® relies on national data sources, where available, for the climate, land use, and soils
parameters necessary to drive the rainfall-runoff simulation mechanisms. To produce the hydrologic
foundation for NC, daily climate data were developed by the Parameter-elevation Regressions on
Independent Slope Model (PRISM) Climate Group at Oregon State University for 46 years (1960–2006)

17
and provided by the U.S. Department of Agriculture (USDA). The data were formatted into 4 km grids of
daily precipitation totals and average temperatures (DiLuzio et al., 2008). Soils data were obtained from
the Soil Survey Geographic database (SSURGO) and the U.S. General Soil Map where SSURGO data were
unavailable. Landcover data were obtained for two different time periods in order to model unaltered
and current conditions. Unaltered landcover were obtained from the Potential Natural Vegetation (PNV)
landcover developed by the Conservation Biology Institute as a proxy for landcover prior to human
presence (Kuchler, 1964). The unaltered landcover for NC consisted predominantly of different forest
types, wetlands, and barren cover. Current landcover were obtained from the National Land Cover
Dataset (NLCD) provided by the USGS for the year 2006. Landcover types include forest, wetlands,
agriculture, grasslands, and different levels of development. Water system discharges and withdrawals
were obtained from NC databases on public and non-public systems. These data account for permitted
human alterations to natural flow regime from industry, public water supply, wastewater treatment,
and agriculture. In order to account for temporal variation in each dataset, data were aggregated by
month and averaged across recent years (2000–2011) to represent current conditions. Human
alterations from major regulated control structures were also included in the model. There were eleven
major control structures in NC that were located directly upstream from USGS gages with time series
flow data. WaterFALL® was available for use in non-tidal streams in NC (Figure 7).

Figure 7. Availability of WaterFALL® hydrologic data in NC by River Basin
WaterFALL® was calibrated at 61 locations where long-term USGS stream gages were present.
WaterFALL® parameters were optimized with the goal of minimizing the differences in log-transformed
daily flows. This objective function gives equal weight to differences in streamflows at the low and high
end of the hydrograph, which often results in better representations of low flows at the expense of
potentially underestimating peak streamflows. Model performance was evaluated at the location of
USGS gages by an overall volume error (OVE) measure or percent bias and by the Nash-Sutcliff Efficiency
(N-S). Using watersheds ranging in size from 39 km2 to 3,181 km2 across both reference and non-
reference conditions, the majority of the calibrated sites had N-S > 0.35 and OVE within 10% on a daily
time step.
To validate the performance of WaterFALL®, a suite of hydrologic metrics generated by WaterFALL®
were compared to metrics calculated using hydrologic data from 55 USGS gages. These gages were
chosen to characterize both reference and altered streams, over a range of stream sizes (drainage area
ranged from 9 to 6,223 km2), and across the State. For each month, five metrics were compared: the
10th, 25th, 50th, 75th, and 90th percentiles of flows to cover the spectrum of extreme low to very high

18
flows. A high degree of correlation (within 30% bounds) between WaterFALL® model predictions and
USGS observations for these five metrics were found across the majority of sites. Out of the 55 streams
represented, there was only one stream that consistently fell outside of the 30% range and two
additional sites that fell outside of the range during some months. These results demonstrated that
using WaterFALL® to model flow conditions and flow metrics in ungaged catchments is suitable for the
purpose of determining flow–biology relationships.
In summary, a hydrologic foundation consisting of flows under unaltered and current conditions was
produced. Unaltered hydrologic flows were modeled with the WaterFALL® model using the climate data
from 1966–2006, PNV landcover, and no instream flow alterations (i.e., no withdrawals, discharges, or
impoundments). Current hydrologic flows were modeled using the same 40-year climate period (1966–
2006) with 2006 NLCD landcover and instream human water uses (i.e., water withdrawals, discharges,
and impoundments). The 40-year climate record was selected and held constant between model
simulations to mute natural climate variability and characterize the changes in streamflow attributable
to human influences.
Flow Metrics 3.1.3
The objectives of flow metrics within the flow–biology relationships are to represent components of
the flow regime that directly or indirectly influence aquatic biology. The hydrologic regime has been
widely recognized as an important factor governing ecological integrity in streams (Poff and Ward,
1989). Physical habitat structure is of paramount importance in determining both the abundance and
species composition of fish (Peeters, and Gardeniers, 1998), and the most important physical habitat
variables include components of flow (Geist et al., 2002; Ahmadi-Nedushan et al., 2006). For this reason,
flow is considered to be a “master variable”, and ecologists have translated flows into five main
ecologically relevant components of flow: magnitude, timing, frequency, duration, and rate of change of
events (Poff et al., 2010).
For the purposes of developing flow–biology relationships for streams and rivers in NC, flow metrics
were determined at each NHD+ catchment with a fish or benthic monitoring station. The Indicators of
Hydrologic Alteration (IHA) framework developed by TNC and the ecosurplus/ecodeficit framework
developed by Vogel et al. (2007) served as the sources of the initial metrics considered in the flow–
biology analyses (Table 8).

19
Table 8. Flow metrics initially considered for the development of fish and benthic flow–biology
relationships (n = 94)
Time Step Type Flow Metric Flow Criterion No. of Metrics
Monthly Flow
(Magnitude) IHA
Extreme Low Flow 10th percentile 12
Low Flow 25th percentile 12
Median Flow 50th percentile 12
High Flow 75th percentile 12
Seasonal Flow
Winter (Dec–Mar)
Spring (Apr–Jun)
Summer (Jul–Sep)
Fall (Oct–Nov)
(Magnitude)
IHA
Extreme Low Flow 10th percentile 4
Low Flow 25th percentile 4
Median Flow 50th percentile 4
High Flow 75th percentile 4
Annual Flow
(Magnitude &
Duration)
IHA Minimum Flow 3, 7, 30, and 90 days 4
Seasonal Flow
Winter (Dec–Mar)
Spring (Apr–Jun)
Summer (Jul–Sep)
Fall (Oct–Nov)
IHA Minimum Flows 3, 7, 30, and 90 days 12
Annual Flow
Winter (Oct–Jun)
Summer (Jul–Sep)
(Events)
IHA
Extreme Low Flow
Magnitude 10th percentile 3
Extreme Low Flow Events Number of Events 3
Extreme Low Flow
Duration Maximum Duration 3
Annual Flow
Seasonal Flow
Winter (Dec–Mar)
Spring (Apr–Jun)
Summer (Jul–Sep)
Fall (Oct–Nov)
EcoDeficit EcoDeficit Magnitude and Timing 5

The IHA metrics are composed of 67 ecologically relevant statistics (Richter et al., 1996) and are
widely accepted for assessing hydrologic alterations and ecological flows (Gao et al., 2009). From these,
a total of 89 IHA-based flow metrics were developed and evaluated in the development of flow–biology
curves (Table 8). These consisted of a sub-set of the IHA metrics outlined by Richter et al. (1996) and IHA
metrics split by month and season. The date ranges of the seasons were selected for consistency with
flow patterns and the Physical Habitat Simulation (PHABSIM) analyses conducted for NC and consisted
of winter (December to March), spring (April to June), summer (July to September), and fall (October
and November). Changes in streamflow were calculated as the percent alteration from unaltered to
current flow conditions (Eq. 2).
(
) (Equation 2)
where A is the altered hydrologic indicator averaged from 1960 to 2006, and U is the unaltered
hydrologic indicator averaged from 1960 to 2006.

20
Vogel et al. (2007) addressed the high degree of autocorrelation among IHA metrics by developing
generalized indices to capture the magnitude and timing of hydrologic alterations. These indices were
coined “ecosurplus” and “ecodeficit”, and are calculated by taking the difference between the median
flow duration curve (FDC) of unaltered (or baseline) and altered conditions, and normalizing that
difference by the area beneath the unaltered FDC (Figure 8). The median annual FDC reduces the noise
presented by high levels of inter-annual variation in flow due to variable climatic conditions. Thus, the
median annual FDC better reflects the variability of daily streamflow within a typical year (Vogel et al.,
2007). Ecosurplus is the total area located above the unaltered FDC and below the altered FDC, divided
by the total area beneath the unaltered FDC. Ecodeficit is the ratio of the area below the unaltered FDC
and above the altered FDC. The timing component of changes in hydrologic flow can be taken by
segmenting the FDC into seasons. Five ecodeficit-based metrics were considered in the development of
the flow–biology curves for NC (Table 8).

Figure 8. Schematic illustrating annual ecodeficit and ecosurplus for a site in the Roanoke River Basin
The final selection of flow metrics used in the flow–biology relationships was determined through an
iterative process that considered the following objectives. The flow metrics, to the degree possible, had
to be (1) ecologically relevant and represent as many of the five components of the flow regime as
possible, (2) focused on low flows to be consistent with NCDENR management objectives, (3) recognize
the seasonality of flows and management objectives, (4) accurately modeled by WaterFALL®, and (5)
have a low degree of inter-correlation.
Analyses of the 94 original metrics found that little to no change occurred in the IHA annual and
seasonal extreme low flow events metrics. These metrics were therefore dropped from the remaining
analyses. The monthly and seasonal IHA metrics were also found to be highly correlated with one
another (Spearman Rank: average = 0.76 ± 0.15 for monthly and Spearman Rank: average = 0.75 ± 0.12
for seasonal), a trend which has been reported in other studies (Gao et al., 2009; Arthington et al., 2006;
Olden and Poff, 2003). In contrast, IHA minimum flow durations (i.e., 3-, 7-, 30-, and 90-day annual
average minimum flows) had a lower average correlation of 0.58 ± 0.18 with the seasonal magnitude

21
metrics. Similar to the IHA metrics, high degrees of correlation among annual and seasonal ecodeficit
variables (0.84–0.96; Spearman Rank Correlation) were found. However, these metrics have been
reported to explain the majority of hydrologic variation captured by IHA metrics and offer a
comprehensive, integrated alternative. Therefore, ecodeficit metrics were selected over IHA metrics for
comparable measures of flow.
In summary, annual and seasonal ecodeficits and average annual 30-day minimum flows were the
flow metrics that met the five main objectives and were selected for the development of the flow–
biology relationships for the riffle-run fish guild and benthic EPTr in NC (Table 9).
Table 9. Flow metrics selected to develop fish and benthic flow–biology relationships (n = 6)
Time Step Flow Metric Flow Regime Component No. of Metrics
Annual
Winter (Dec–Mar)
Spring (Apr–Jun)
Summer (Jul–Sep)
Fall (Oct–Nov)
EcoDeficit Magnitude and Timing 5
Annual Reduction in annual average
30 day minimum flow Magnitude and Duration 1

Statistical Analysis Approach 3.1.4
A comprehensive, iterative statistical analysis approach was adopted in the development of flow–
biology relationships for the streams and rivers of NC. The main objective of the approach was to
determine the “best” model to describe the relationships between the rigorously selected riffle-run fish
diversity and benthic EPTr metrics and flow metrics, with “best” being defined by a combination of
statistical strength and ease of implementation by water resource managers. The following sub-sections
describe the individual steps in the analysis approach.
Step 1: Quantile or Upper-Limit Regressions
The first step was to select the statistical analysis approach to best describe the relationship
between flow alteration and biological response. Two modeling approaches were considered: quantile
and upper-limit regressions. Both of these approaches develop the flow–biology relationship with a
focus on the upper 80th or 90th percent of the data and are based on the assumption that these data
represent the upper limit of the response attributable to flow alteration (Armstrong et al., 2011; Cade
and Noon, 2003). In other words, the 80th or 90th percentile can be viewed as the upper limit of
influence that a change in flow can have on the biology, with the remainder of the variation in biological
response being a function of other stresses such as water quality, habitat structure, sampling season,
etc.
Quantile Regression
Quantile regression is a univariate method for estimating the relationships between variables at any
portion of the probability distribution, such as the 80th or 90th percent of the data (Cade and Noon,
2003; Koenker and Basset, 1978). This allows for the modeling of rates of change in all parts of the
distribution, whereas traditional regressions are fitted only to the mean of the data. Quantile
regressions are solved using the simplex method in linear programming that optimizes the partitioning
of the data into groups by breaking up the x-axis in such a way that the points fall within similarly sized

22
groups. The quantile of interest is calculated within each partition and a line function is fitted to all
points such that the line runs between the points within the quantile of interest and points outside the
quantile (Figure 9). The quantile method is more robust against outliers than traditional regression and
has been well established for assessing ecological and flow–biology relationships (Armstrong et al.,
2011; Cade and Noon, 2003). A main disadvantage of the method is that it is heavily influenced by the
distribution of the data, particularly in data scarce partitions.
For the purposes of the development of flow–biology relationships, an 80th quantile analysis
approach was tested. The 80th percentile was selected to maintain the upper limit of the response while
incorporating a larger range of flow alteration in the upper 20% than was included in a 90th quantile
regression.

Figure 9. Process of calculating quantile regressions for flow–biology relationships: (A) Flow–biology
data, (B) Algorithm partitioning data for quantile regression, (C) Straight line fit to quantile

23
Upper-Limit Regression
Upper-limit regression, as termed in this report, refers to sub-setting the data to contain the upper
percentile of the X–Y relationship and running a regression on that subset of data. It involves
partitioning the x-axis in fixed increments to allow for equal representation of the range of values along
the axis. For example, the upper 80th percentage of data within each x-axis increment is selected and a
regression line is fit to these data (Figure 10). The advantage of this method is that it models the top 20th
percentile of the distribution of points within each partition and will therefore closely fit the data
distribution across the x-axis. A disadvantage of the upper-limit method is that the number of data
points included in the model varies between flow metrics, making it difficult to compare models. Similar
to the quantile method, this method is also sensitive to the distribution of points along the x-axis,
placing greater emphasis on more data sparse areas (e.g., the number of data points included in the
regression may not represent the upper 80th percent if less than 10 points are present in an x-axis
increment). Upper-limit regression is also more sensitive to outliers since the model fitting process
minimizes the distance between data points and the averaging value of the response variable.
For the purposes of the development of flow–biology relationships, an 80th upper-limit regression
was tested. The x-axis was partitioned into 0.5% increments and the 80th percentile of data within each
increment was included in the regression analysis.

Figure 10. Process of calculating the upper-limit regression for flow–biology relationships: (A) Flow–
biology data, (B) Partition x-axis into evenly spaced increments and select 80th percentile of data
points, (C) Linear regression of selected data

24
It was not possible to statistically compare the goodness of fit of the quantile and upper-limit
regressions. Both methods consistently resulted in statistically significant flow–biology relationships for
fish and benthos and both exhibited similar flow-biology response. Based on the prior application of
quantile regressions in flow–biology analyses, the 80th quantile regression analysis approach was
adopted to characterize the response for riffle-run fish guild diversity and benthic EPTr to flow
alterations in NC.
Step 2: Linear or Non-Linear Regressions
Once the 80th quantile regression was selected, the flow–biology relationships were fitted with
linear (Eq. 3) and non-linear (Eq. 4) exponential decay regressions to see which regression best fit the
data. It was hypothesized that the non-linear exponential decay model would be the best fit because the
conditions of fish and benthos decreased rapidly in response to flow alterations less than 5% prior to
leveling out at higher degrees of flow alterations.
Linear: Y = A + BX (Equation 3)
Non-Linear: Log(Y+1) = A + BX (Equation 4)

where, Y is the biologic metric, X is the hydrologic metric, A is the Y-intercept, and B is the slope.
The linear and non-linear models were found to produce similar flow–biology relationship for fish
and benthos that were comparable in statistical strength. Linear regressions were selected for the flow–
biology relationships for riffle-run fish guild diversity and benthic EPTr because a linear response across
the full range of flow alteration is easier to understand and implement.
Step 3: Relationships by Regional Classification or State
The next step in the analysis approach was to assess the robustness of flow–biology relationships
developed for each a priori regional class determined in the BEC System project versus flow–biology
relationships determined for the whole State. The EDU classification system was found to best describe
the assemblages of fish species across NC, and the Omernik Level III ecoregions best described benthos.
According to the ELOHA framework and flow–biology theory, developing flow–biology relationships for
each class should reduce the variability in the relationships; flow–ecology relationships may vary by
stream type with respect to flows and the biological community. The main objectives of this step in the
analysis approach were therefore to (1) confirm the availability of sufficient fish and benthic data within
each class to determine class-specific flow–biology relationships, (2) determine if the biological
communities in each stream class differ in their responses to changes in flow, and (3) determine how
regional flow–biology relationships compare with statewide flow–biology relationships.
The responses of fish and benthos to summer ecodeficits were compared at the regional class and
statewide levels. Table 10 and Table 11 present a summary of the number of sites located in each
regional class, the linear flow–biology relationship by regional class, and the statistical significance of
each relationship for riffle-run fish guild diversity and benthic EPTr, respectively. For fish, the number of
sites in each region varied from 2 to 170 (Figure 11), with only two of the flow–biology relationships
being statistically significant. However, the two significant flow–biology relationships in the Upper Pee
Dee and Upper Santee rivers shared very similar slopes, suggesting similar response functions in the two
basins. For benthos, the number of sampling sites in each region was higher (13 to 655) (Figure 12).
Similar to fish, however, not all regional flow–biology relationships were significant. In contrast, for both
fish and benthos the statewide flow–biology relationships were significant. There were sufficient
biological data, and the statistical strength of the statewide fish and benthos response models was high.
Therefore, flow–biology relationships determined at the statewide level were adopted to characterize

25
the responses of riffle-run fish guild diversity and benthic EPTr to flow alterations. The benefits of
statewide analyses are they incorporate the maximum amount of available data available and simplify
implementation of the flow–biology relationships by water resource managers.
Table 10. Summary of the number of sites, flow–biology relationships and statistical significance of
the flow–biology relationships at the EDU and statewide levels for riffle-run fish guild diversity in
response to summer ecodeficits (significant flow–biology relationships at the p<0.05 are in bold)
EDU No. of Sites Flow-biology Equation p-value
Upper Savannah River 2 NA NA
New River 31 Y=100+3.2X 0.46
Albemarle/Pamlico-Piedmont 90 Y=100-0.86X 0.25
Upper Roanoke River 18 Y=100-1.68X 0.67
Cape Fear River-Coastal Plain 9 Y=100-3.84X 0.32
Cape Fear River-Piedmont 63 Y=100+1.54X 0.62
Pee Dee River-Coastal Plain 17 Y=100-3.35X 0.21
Upper Pee Dee River 131 Y=100-2.57X 0.05
Upper Santee River 109 Y=100-2.04X 0.02
Albemarle/Pamlico-Coastal Plain 9 Y=100-4.08X 0.54
Tennessee River-Blue Ridge 170 Y=100-0.24X 0.92
Statewide 649 Y = 100-2.76X 0.00

Figure 11. NCDDWR Stream Fish Community Assessment Program sample sites with riffle-run fish
guild species by EDU

26
Table 11. Summary of the number of sites, flow–biology relationships and statistical significance of
the flow–biology relationships at the Omernik Level III ecoregion and statewide levels for benthic EPTr
in response to summer ecodeficits (significant flow–biology relationships at the p<0.05 are in bold)
Omernik Level III Ecoregion No. of Sites Flow-biology Equation p-value
Blue Ridge 363 Y = 100 - 0.47X 0.26
Piedmont 655 Y = 100 - 1.83X 0.00
Southeast Plains 196 Y = 100 - 0.64X 0.30
Mid Atlantic Coastal 13 Y = 100 - 2.82X 0.14
Statewide 1,227 Y = 100 - 2.43X 0.00

Figure 12. Benthic Macroinvertebrate Biological Assessment Unit sampling sites by Omernik Level III
ecoregion
Step 4: Normalized or Raw Biological Data
Following the decision to determine flow–biology relationships at the statewide level, an evaluation
of the magnitudes and ranges of fish and benthic metrics across NC became important. If riffle-run fish
guild species richness, counts, and diversity and benthic EPTr were similar in all regions of the State, the
raw biological metrics could be used in the flow–biology relationships. However, if the metrics differed
by physiographic region, it may be necessary to normalize the biological data by region prior to
conducting the flow–biology analyses. Summaries of riffle-run fish guild are presented in Table 12, and
summaries of benthic EPTr are presented in Table 13. For the analyses of benthic EPTr, the Mid Atlantic
Coastal Plain ecoregion was combined with the Southeastern Plain Ecoregion and analyzed as the
Coastal Plains due to a low number of sampling sites in the Mid Atlantic Coastal Plain Ecoregion (Table
11). The species richness of the riffle-run guild was found to be higher in the mountain regions than in
the coastal plain (Figure 13). Benthic EPTr follows a similar trend with higher EPTr in mountain regions
than in the coastal plain (Figure 14). Similarly, there was considerable variation in the species richness,
counts, and diversity of the riffle-run guild and benthic EPTr by drainage basin or Omernik Level III
ecoregion. These results suggest either inherent differences in aquatic biology by basin or region, which
would warrant data normalization, or differences in aquatic biology due to location-specific stresses. For
example, differences in flow alteration may account for the differences in the riffle-run fish diversity by
drainage basin and benthic EPTr by Omernik Level III ecoregion, as suggested by Figure 15.
Step 4 in the analysis approach evaluated the need to normalize the biological data and whether the
flow-biology relationships developed using raw biological data were better fits than those developed

27
using normalized data. For this comparison, the fish and benthic metrics were normalized by dividing
each value by the 80th percentile of the respective metric within each respective physiographic region.
For fish, the metrics were normalized by river basin and for benthos the metrics were normalized by
Omernik Level III ecoregion. Although EDU was found to be the most significant physiographic predictor
of fish assemblages (see Section 2), the fish metrics were normalized by river basin because EDU and
basin boundaries are similar, fish cannot cross river basin boundaries and are hydrologically isolated
from each other, and river basins have more management relevance. Benthic EPTr was normalized by
Omernik Level III ecoregions (Blue Ridge Mountains, Piedmont, and Coastal Plains [Southeastern Plain
and Mid Atlantic Coastal Plain]) because this classification system was the most significant physiographic
predictor of benthic EPTr species assemblage. The 80th percentile was selected as the value to normalize
the metrics because it was seen to represent the highest standard of biological potential in the
physiographic region. For fish, the 80th percentile value within each river basin was applied directly
(Table 12). For benthos, NCDENR classifies the condition of each monitoring sites as excellent, good,
good-fair, fair, and poor based on the biological community composition. Monitoring sites with an
excellent rating were deemed to be the highest-quality sites, i.e., those sites that have experienced
minimum disturbance and should have the greatest EPTr. Therefore, benthic EPTr was normalized by
the 80th percentile of EPTr with excellent site conditions (Table 13).
Table 12. Maximum and 80th percentile of riffle-run fish guild metrics by river basin
River Basin No. of
Sites
Maximum Value 80th Percentile
Abundance Species
Richness
Diversity
Index Abundance Species
Richness
Diversity
Index
French Broad 72 1,554 13 2.15 527 9 1.56
Hiwassee 19 1,016 10 1.68 605 8 1.50
Little Tennessee 60 1,373 10 1.73 401 8 1.45
New 31 1,870 13 1.91 1,243 11 1.56
Savannah 2 120 3 0.43 110 3 0.41
Watauga 19 776 9 1.43 430 4 0.85
Broad 43 448 6 1.52 93 4 1.14
Cape Fear 72 163 6 1.30 73 3 0.73
Catawba 66 724 7 1.56 211 5 1.33
Lumber 12 21 2 0.39 10 1 0.00
Neuse 47 597 4 1.25 140 4 0.90
Roanoke 36 578 14 2.20 363 11 1.69
Tar 34 218 6 1.52 100 5 1.13
Yadkin 136 1,645 9 1.81 227 6 1.29
TOTAL 649

28

Figure 13. Riffle-run fish guild species richness within each river basin and EDU

Table 13. Maximum and 80th percentile of benthic EPTr by Omernik Level III ecoregion
Omernik Level III Ecoregion
No. of
Sites
No. of Excellent
Condition Sites
Maximum
Value
80th Percentile of
Excellent Condition Sites
EPTr EPTr
Blue Ridge 363 159 61 48
Piedmont 655 49 54 45
Southeast Plains & Mid
Atlantic Coastal 209 28 40 33
Total 1,227 236

29

Figure 14. Average benthic EPTr (25th, 50th, and 75th percentiles) within each Omernik Level III
ecoregion (open circles indicate all sites, closed circles indicate sites with excellent (E) site condition).

Figure 15. Summer ecodeficit (25th, 50th, and 75th percentiles) at benthic monitoring sites with
excellent condition within each Omernik Level III ecoregion
To evaluate the performance of flow–biology relationships developed using normalized versus non-
normalized data, responses of the riffle-run fish diversity and benthic EPTr to the annual and four
seasonal ecodeficits and reductions in 30-day average annual minimum flows were compared. Models
were evaluated on the basis of the degree to which differences in fish and benthic biology between
basins/ecoregions could be accounted for by differences in flow alteration between the same
basins/ecoregions. In other words, to what degree can the differences in flows at the benthic excellent
condition sites by ecoregion (Figure 15) account for the differences in benthic EPTr by ecoregion (Figure
14)?

30
This procedure involved determining the “reference” condition in each ecoregion or river basin. The
80th percentile of benthic EPTr at the excellent condition monitoring sites was used to represent the
“reference” condition for benthos in each Omernik Level III ecoregion. To be comparable with benthos,
the reference condition for fish was determined by taking the 80th percentile of the 80–100th percentile
riffle-run fish guild diversity within each river basin. To be comparable to benthos, river basins were
grouped into Omernik Level III ecoregions (Piedmont Ecoregion = Broad, Catawba, Roanoke, and
Yadkin/Coastal Plains Ecoregion = Neuse, Tar, and Cape Fear). The fish and benthic “reference” sites
within ecoregions with higher amounts of flow alteration, as reflected by ecodeficits (i.e., Piedmont and
Coastal Plains ecoregions) (Figure 15) were then predicted using the normalized versus non-normalized
regression equations. These predicted values were subsequently compared to the observed values
within the ecoregions to determine the degree of biological variation not explained by the normalized
versus non-normalized models.
For both fish and benthos, the model comparisons indicated that the statewide flow–biology
relationships developed using raw biological data were able to describe a larger proportion of the
biological response to flow alteration; the deviations between observed and predicted benthic EPTr and
riffle-run fish guild diversity were, in general, less for the non-normalized than normalized flow–biology
models (Table 14 and 15). More specifically, for benthos, normalization by ecoregion increased errors in
estimates of EPTr in the Piedmont and Coastal Plains ecoregions by 0.4–22%. Errors were particularly
large in the Coastal Plain (17–22%) which had much larger ecodeficits at excellent condition sites than
did the Piedmont ecoregion. Although less distinct and consistent for fish, normalization increased
errors in the riffle-run fish diversity by as much as 10%. Based on these results, normalization of the
biological data appears to reduce the sensitivity of the analyses because differences in biological
condition by ecoregion are largely accounted for in the fish and benthic models. Therefore, for both fish
and benthos, non-normalized data were used to develop the statewide linear 80th quantile flow–biology
relationships for NC. To facilitate a comparison of the responses of benthos and fish to the different
metrics, the biological responses (i.e., y-axis) were scaled to 100%.
Table 14. Deviation between the observed and predicted benthic EPTr in the Piedmont and Coastal
Plains ecoregions using flow–biology relationships developed with normalized and non-normalized
benthic data (Deviation = (predicted-observed)/observed*100%)
Flow Metric Non-normalized Data Data Normalized by Ecoregion
Piedmont Coastal Plains Piedmont Coastal Plains
Annual Ecodeficit 5.6 13.7 6.0 27.7
Winter Ecodeficit 5.2 11.2 5.9 27.9
Spring Ecodeficit 2.4 5.8 4.1 21.9
Summer Ecodeficit 2.2 3.0 3.8 18.0
Fall Ecodeficit 1.4 3.0 3.6 20.4
Reduction in average annual
30-day minimum flow 3.2 21.2 4.2 28.0

31
Table 15. Deviation between the observed and predicted riffle-run fish guild in the Piedmont and
Coastal Plains ecoregions using flow–biology relationships developed with normalized and non-
normalized fish data (Deviation = (predicted-observed)/observed*100%)
Flow Metric Non-normalized Data Data Normalized by River Basin
Piedmont Coastal Plains Piedmont Coastal Plains
Annual Ecodeficit 17.6 36.7 17.4 35.0
Winter Ecodeficit 14.7 28.7 17.7 35.0
Spring Ecodeficit 14.2 27.9 17.2 34.3
Summer Ecodeficit 13.3 21.8 12.4 22.2
Fall Ecodeficit 12.7 23.1 17.1 32.8
Reduction in average annual
30-day minimum flow 12.1 23.1 17.3 33.0

3.2 Results
The twelve statewide flow–biology relationships characterizing the responses of riffle-run fish
diversity and benthic EPTr to flow alteration were statistically significant (Table 16 (A and B) and Table
17 (A and B)), supporting previously documented findings that any human-induced flow alterations can
negatively impact aquatic biology (McManamay et al., 2013). In addition, the responses of fish and
benthos to all six flow metrics were found to be similar in magnitude and direction. For each 10%
decrease in flow, there is an average decrease of 18.2 ± 5.3% in the riffle-run fish guild diversity and
22.8 ± 4.1% in Benthic EPTr (Figure 16, 17, 18, 19, 20, and 21). Benthos, in general, appears to be more
consistently sensitive to changes in flow than fish; benthic EPTr responses to four of the six flow metrics
were greater than the responses of riffle-run fish diversity. However, the largest reductions in biological
condition were seen in the fish response to summer ecodeficits. With each 10% increase in summer
ecodeficit, riffle-run fish diversity is predicted to decrease by 27.6%. Benthic EPTr was found to be most
responsive to spring ecodeficits. With each 10% increase in spring ecodeficit, Benthic EPTr is predicted
to decrease by 26.6%.

32
Table 16. Statewide quantile regression models (Y = A + BX) relating ecodeficit (X) to biological
responses (Y) for riffle-run fish guild diversity (the smaller number of sites for the Reduction in
Average Annual 30-Day Minimum Flow flow-biology relationships is due to only including sites with
reductions in flow)
(A) Non-scaled relationship (i.e., raw data)
Flow Metric No. of
Sites
Intercept (A) Slope (B)
Value Standard
Error p-value Value Standard
Error p-value
Annual Ecodeficit 649 1.41 0.035 <0.001 -0.020 0.006 <0.001
Winter Ecodeficit 649 1.40 0.035 <0.001 -0.019 0.006 <0.001
Spring Ecodeficit 649 1.42 0.034 <0.001 -0.024 0.006 <0.001
Summer Ecodeficit 649 1.48 0.028 <0.001 -0.041 0.007 <0.001
Fall Ecodeficit 649 1.47 0.032 <0.001 -0.031 0.007 <0.001
Reduction in Average Annual
30-Day Minimum Flow 361 1.30 0.057 <0.001 -0.021 0.006 <0.001
(B) Relationship scaled to 100% y-axis intercept
Flow Metric No. of
Sites
Intercept (A) Slope (B)
Value Standard
Error p-value Value Standard
Error p-value
Annual Ecodeficit 649 100 2.580 <0.001 -1.429 0.429 <0.001
Winter Ecodeficit 649 100 2.383 <0.001 -1.353 0.530 0.011
Spring Ecodeficit 649 100 2.365 <0.001 -1.653 0.332 <0.001
Summer Ecodeficit 649 100 1.797 <0.001 -2.761 0.469 <0.001
Fall Ecodeficit 649 100 2.326 <0.001 -2.093 0.444 <0.001
Reduction in Average Annual
30-Day Minimum Flow 361 100 4.434 <0.001 -1.606 0.459 <0.001

33
Table 17. Statewide quantile regression models (Y = A + BX) relating ecodeficit (X) to biological
responses (Y) for benthic EPTr (the smaller number of sites for the Reduction in Average Annual 30-
Day Minimum Flow flow-biology relationships is due to only including sites with reductions in flow)
(A) Non-scaled relationship (i.e., raw data)
Flow Metric No. of
Sites
Intercept (A) Slope (B)
Value Standard
Error
p-value Value Standard
Error
p-value
Annual Ecodeficit 1227 38.47 0.850 <0.001 -0.902 0.149 <0.001
Winter Ecodeficit 1227 38.55 0.790 <0.001 -0.935 0.129 <0.001
Spring Ecodeficit 1227 38.59 0.775 <0.001 -1.025 0.118 <0.001
Summer Ecodeficit 1227 38.47 0.772 <0.001 -0.936 0.099 <0.001
Fall Ecodeficit 1227 39.83 0.689 <0.001 -0.932 0.066 <0.001
Reduction in Average Annual
30-Day Minimum Flow 764 32.26 0.811 <0.001 -0.474 0.049 <0.001

(B) Relationship scaled to 100% y-axis intercept
Flow Metric No. of
Sites
Intercept (A) Slope (B)
Value Standard
Error
p-value Value Standard
Error
p-value
Annual Ecodeficit 1227 100 2.210 <0.001 -2.344 0.387 <0.001
Winter Ecodeficit 1227 100 2.050 <0.001 -2.427 0.334 <0.001
Spring Ecodeficit 1227 100 2.009 <0.001 -2.657 0.307 <0.001
Summer Ecodeficit 1227 100 2.005 <0.001 -2.433 0.257 <0.001
Fall Ecodeficit 1227 100 1.730 <0.001 -2.341 0.166 <0.001
Reduction in Average Annual
30-Day Minimum Flow 764 100 2.713 <0.001 -1.469 0.153 <0.001

34

Figure 16. Responses of riffle-run fish guild diversity and benthic EPTr to annual ecodeficit (fish and
benthic biological condition on y-axis are scaled to 100%)

Figure 17. Responses of riffle-run fish guild diversity and benthic EPTr to winter ecodeficit (fish and
benthic biological condition on y-axis are scaled to 100%)

35

Figure 18. Responses of riffle-run fish guild diversity and benthic EPTr to spring ecodeficit (fish and
benthic biological condition on y-axis are scaled to 100%)

Figure 19. Responses of riffle-run fish guild diversity and benthic EPTr to summer ecodeficit (fish and
benthic responses on y-axis are scaled to 100%)

36

Figure 20. Responses of riffle-run fish guild diversity and benthic EPTr to fall ecodeficit (fish and
benthic biological condition on y-axis are scaled to 100%)

Figure 21. Responses of riffle-run fish guild diversity and benthic EPTr to decreases in annual average
30-day minimum flow (fish and benthic biological condition on y-axis are scaled to 100%)

37
3.3 Conclusions
In summary, through a comprehensive, iterative, step-wise evaluation approach a scientifically
defendable method was developed to characterize the responses of riffle-run fish diversity and benthic
EPTr to flow alteration. This method includes the use of (1) six flow metrics which are amenable to
management and address magnitude, timing, and duration components of flow (annual and seasonal
ecodeficits, reductions in average annual 30-day minimum flows), and (2) linear 80th percentile flow–
biology relationships developed at the statewide level using non-normalized biological data. Application
of this approach produced 12 statistically significant flow–biology relationships that could support the
determination of ecological flows in NC; both benthos and fish showed negative responses to flow
alteration. The responses of benthic EPTr to reductions in flow were consistent and generally greater
than that of riffle-run fish diversity. However, the riffle-run guild showed the greatest reductions in
biological condition in response to summer ecodeficits. The relative consistency of sedentary benthic
macroinvertebrates to flow alterations and the higher sensitivity of fish that rely on riffle-run habitats to
summer ecodeficits highlights the need for water managers to consider ecological flows in a seasonal
context.

38
4. ECOLOGICAL FLOW FRAMEWORK
4.1 Proposed Ecological Flow Framework
Having selected ecodeficits and reductions in the annual average 30-day minimum flow as the most
indicative and management-relevant flow parameters and the diversity of the riffle-run fish guild and
benthic EPTr as the best indicators of biological condition, it was necessary to design a strategy for
relating these parameters and indicators to determine when ecosystem integrity would be threatened
by flow alterations. Within the bounds of our data and for all 12 flow–biology relationships, any
measurable ecodeficit or decrease in flow produces some measurable decline in biological condition.
The responses are linear with no obvious precipitous change in biology associated with a certain amount
of flow alteration. In addition, it is not possible to identify a flow alteration threshold beyond which the
biological condition noticeably degrades. Therefore, it was not possible to identify upper or lower
biology-based thresholds of flow alteration from the relationships.
As humans increasingly use more water, flows will continue to be altered to support their
enterprises. Therefore, a strategy is needed for determining which rivers and streams can tolerate more
alteration. Two strategies were considered. First, a distributed impacts strategy encourages society to
get its water from the rivers and streams in the best ecological condition, so that all rivers and streams
move toward a medium level of biological alteration which will deteriorate over time. Second, a working
rivers strategy encourages society to meet its water needs mainly from rivers and streams that are
already showing more biological alteration (Connecticut DEP, 2009). The working rivers strategy was
selected because it is the only one that can maintain some rivers and streams in good enough condition
for the most sensitive species and to serve as biological benchmarks for understanding aquatic
ecosystems.
A number of studies in the literature indicate that species losses in relatively small percentages
produce significant reductions in ecosystem resilience (e.g., Rockstrӧm et al., 2009). Acknowledging that
all flow alterations appear to cause a decline in fish and benthic biological condition, human society
needs and will continue to need water for its enterprises, relatively small biological condition reductions
can be problematic for ecosystem resilience, and a working rivers strategy is preferable, the following
Ecological Flow Framework is proposed for translating the fish and benthic flow–biology relationships
into ecologically based flow management.
The proposed Ecological Flow Framework consists of Ecological Flow Categories and Biological
Response Thresholds. The Ecological Flow Category for a given river, stream, gage or modeled node is
the amount of biological alteration associated with the current level of flow alteration.
Characterization of the current degree of flow alteration is based on a comparison of flows under
potential natural vegetation cover (PNV) and current conditions, as described in Section 3. This flow
alteration is then converted to a measure of biological alteration based on the respective flow–biology
relationships. The Biological Response Threshold associated with each Ecological Flow Threshold refers
to the change in biological condition that indicates a future flow condition that may be beyond an
acceptable level of change and warrants further investigation. Table 18 provides the proposed values for
the Ecological Flow Categories and the corresponding Biological Response Thresholds. Note that the
more pristine the flow condition is at the place in question, the smaller the amount of biological
alteration required to flag the alteration for further evaluation, which is in keeping with the working
rivers strategy.

39
Table 18. Ecological Flow Categories and Biological Response Thresholds for the proposed Ecological
Flow Category Framework to support the determination of ecological flows in NC
Ecological Flow Category– (%) Biological Response Threshold
100–80 (Excellent) 5%
80–60 (Good) 10%
60–35 (Fair) 15%
<35 (Poor) Alternative flow standard

The Biological Response Thresholds are based on the average range of biological condition
represented by each NCDENR benthic site condition class (Table 19). Through an evaluation of EPTr by
site condition class at each of 1,227 benthic monitoring stations, it was found that each site condition
class averaged a 19% change in biological condition. Using this 19% change in biology as the foundation
of the Biological Response Thresholds, a half-class change in site condition (i.e., 10%) was deemed a
tolerable level of change in the fish or benthic biological condition for a river in good Ecological Flow
condition. In keeping with the working rivers approach, a more stringent or protective Biological
Response Threshold (i.e., one-quarter class change – 5%) was seen as an acceptable level of change in
the biological condition of fish or benthos for a river in an excellent Ecological Flow condition. For rivers
with a fair Ecological Flow condition, a three-quarter change in site condition class– 15%) was assigned
as an acceptable level of change in the fish or benthic biological condition. For rivers with a poor
Ecological Flow condition, an alternative flow standard is recommended (e.g., 7Q10 or September
monthly median flow).
Table 19. NCDENR Benthic Site Condition Classes and range of benthic EPTr biological condition within
each class
Benthic Site
Condition Classes
Benthic Condition (EPTr normalized by 80th
percentile value in each Omernik Level III
Ecoregion)
Change in Benthic EPTr Condition
within Site Condition Class
Excellent > 77% 23%
Good 56–76% 20%
Good-Fair 34–55% 21%
Fair 16–33% 17%
Poor < 16% 16%
MEAN 19%

4.2 Example Application of the Ecological Flow Framework
This section provides two examples of the application of the Ecological Flow Framework to benthic
and fish flow–biology relationships, using the responses of benthic EPTr and riffle-run fish diversity to
summer ecodeficit as the example scenarios. Figure 22 and Figure 23 display the application of the
Ecological Flow Categories and associated Biological Response thresholds to the benthic and fish
relationships, respectively.
To determine the amount of flow alteration that can occur within a stream without triggering a
caution flag with respect to change in benthic biological condition (i.e., exceedance of the Biological
Response Threshold), the current Ecological Flow Category of the stream has to be determined. For
example, a stream under evaluation may currently have a summer ecodeficit of 6% compared to the

40
unaltered baseline. Using the summer ecodeficit–benthic EPTr flow–biology equation ((Y=100-2.4X), this
6% ecodeficit is calculated to be associated with a benthic biological condition of 85.4% (Figure 22) and
puts the stream in the excellent Ecological Flow Category (Table 18). The Biological Response Threshold
for streams in this Category is 5%. Therefore, an acceptable amount of change in the stream benthic
condition would be from 85.4% to 80.4%. Based on the same the summer ecodeficit–benthic EPTr flow–
biology equation, this 80.4% biological condition is associated with a summer ecodeficit of 8%.
Therefore, a future condition that increases summer ecodeficit by more than 2% (8– 6%) would trigger a
caution flag.

Figure 22. Ecological Flow Framework applied to summer ecodeficit–benthic EPTr flow-biology
relationship
As a second example, the same procedure is applied to a riffle-run fish guild flow–biology
relationship. In the stream in this example, an 8% summer ecodeficit is estimated relative to the
unaltered condition (Figure 23). Using the summer ecodeficit - riffle-run fish guild flow–biology
equation, a current biological condition of 78.2% is determined and the stream is in the Good Ecological
Flow Category (Table 18). A stream within this Category has a Biological Response Threshold of 10%,
thereby allowing the condition of riffle-run fish to be reduced from 78.2% to 68.2%. This 68.2%
biological condition corresponds to a summer ecodeficit of 11.7%. Therefore, flow alterations from a
proposed future scenario resulting in an additional summer ecodeficit of 3.7% (11.7–8%) would not raise
a caution flag.

41

Figure 23. Ecological Flow Framework applied to summer ecodeficit–riffle-run fish guild diversity
flow–biology relationship
4.3 Implementing the Ecological Flow Framework in the Upper Neuse River
This next section provides an example of the application of the Ecological Flow Framework in two
NC stream/river segments and translation of flow metrics into volumes of flow. Two sites in the Neuse
River BasinEno River State Park and the Neuse River at Goldsborowere selected to serve as case
studies. The two sites differ in drainage area, and both have hydrologic data based on flow records from
the nearest adjacent node modeled by HydroLogics Inc. Operational Analysis and Simulation of
Integrated Systems (OASISTM) reservoir modeling tool. The Eno River State Park watershed is
approximately 270 km2 and the Neuse River at Goldsboro is approximately 2,000 km2 (Figure 24). In this
example, WaterFALL® was used to model the current and acceptable levels (i.e., hydrologic alteration
associated with Biological Response Threshold) of hydrologic alteration, and OASISTM was used to
project future levels of hydrologic alteration. The process of combining these two models for water
management purposes is described below.

42

Figure 24. The Eno River State Park and the Neuse at Goldsboro catchments used in the example
applications of the Ecological Flow Framework
First, the unaltered and current flow conditions modeled by WaterFALL® are used to calculate the
current summer ecodeficits. The Eno River State Park stream segment has a summer ecodeficit of 5.3%
and the Neuse River at Goldsboro has a current summer ecodeficit of 17.1%. (Tables 20, 21, 22, and 23).
Next, the summer ecodeficit flow–biology relationships (Figure 19) are used to estimate the
respective biologic condition for fish and benthos at each location. Based on the summer ecodeficit
relationships, Eno River State Park stream segment has a current biologic condition of 87.1% for benthos
and 85.4% for fish (Tables 20 and 21). The Neuse River at Goldsboro has a biologic condition of 58.4% for
benthos and 52.8% for fish (Tables 22 and 23). With respected to Ecological Flow Category, Eno River
State Park stream segment is in the Excellent Ecological Flow Category for both benthos and fish and
given a Biologic Response Threshold of 5% (Table 18). Using the respective flow–biology equations, this
5% change in biological condition corresponds to summer ecodeficits of 7.8% and 7.1% for benthos and
fish, respectively. Therefore, an additional summer ecodeficit of 2.5% in the Eno River State Park stream
segment would be tolerable for benthos and 1.8% would be acceptable for fish. In contrast, the current
summer ecodeficits of the Neuse River at Goldsboro classify the river segment in the Fair Ecological Flow
Category for both fish and benthos. This Ecological Flow Category corresponds to a Biologic Response
Threshold of 15% (Table 18), and additional summer ecodeficits of 6.2% (23 – 17.1%) and 5.9% (22.5 –
17.1%) for benthos and fish, respectively.
The OASISTM model simulates current and future hydrologic conditions out to 2050. The OASISTM
2050 projections therefore provide an opportunity to evaluate whether future water uses may threaten

43
ecological integrity and flows (i.e., exceed the Biological Response Thresholds for individual rivers).
Based on the 2050 projections, the OASISTM model estimates additional summer ecodeficits of 5.4% for
the Eno River State Park stream segment and 3.1% for the Neuse River at Goldsboro (Table 20, 21, 22,
and 23). Applying the flow–biology equations for benthos and fish, the biologic condition associated the
projected 2050 summer ecodeficits in the Eno River State Park stream segment is 70.6% for fish and
74.1% for benthos. For the Neuse River at Goldsboro, the projected biologic conditions are 44.1% and
50.8%, respectively. In an evaluation of the projected changes in biological condition, the Eno River State
Park stream segment is projected to exceed the Biologic Response Thresholds for both fish and benthos
(Figure 25 (A and B) and Figure 26). In contrast, the already highly altered Neuse River at Goldsboro is
not projected to exceed the Biologic Response Thresholds established for the Fair Ecological Flow
Category. This example highlights the working river approach of the proposed Ecological Flow
Framework; rivers that are in excellent hydrologic condition are awarded a higher level of protection to
ensure these high quality waters are preserved, whereas rivers that are already hydrologically altered
are allowed to undergo further alteration.
Table 20. Current (WaterFALL®) and OASISTM 2050 projected hydrologic condition and associated
benthic EPTr biological condition at the Eno River State Park stream segment (MGD = Million Gallons
per Day)
Flow Metric No. of
Days
Current
EcoDeficit
(WaterFALL®)
OASISTM
Projected 2050
EcoDeficit
Benthic EPTr Biologic Condition
(%)
% MGD % MGD Current 2050 Difference
Annual Ecodeficit 365 2.5% 1.32 3.8% 2.00 94.1 85.2 9.0
Winter Ecodeficit 121 2.5% 2.04 4.2% 3.35 93.9 83.8 10.1
Spring Ecodeficit 91 3.7% 1.62 2.8% 1.36 90.2 82.6 7.5
Summer Ecodeficit 92 5.3% 0.88 5.4% 1.09 87.1 74.1 13.0
Fall Ecodeficit 61 3.5% 1.13 3.0% 0.51 91.8 84.7 7.0
Reduction in Annual
Average 30-Day
Minimum Flow
30 1.7% 0.04 13.1% 0.95 97.5 78.3 19.2

44
Table 21. Current (WaterFALL®) and OASISTM 2050 projected hydrologic condition and associated
benthic EPTr in the Neuse River at Goldsboro (MGD = Million Gallons per Day, “NA” indicates an
increase in the annual average 30-day minimum flow)
Flow Metric No. of
Days
Current
EcoDeficit
(WaterFALL®)
OASISTM
Projected 2050
EcoDeficit
Benthic EPTr Biologic Condition
(%)
% MGD % MGD Current 2050 Difference
Annual Ecodeficit 365 12.8% 203.75 1.7% 25.45 69.9 65.8 4.1
Winter Ecodeficit 121 12.8% 277.82 2.5% 57.35 69.0 62.9 6.2
Spring Ecodeficit 91 12.5% 141.74 2.5% 32.14 66.8 60.2 6.6
Summer Ecodeficit 92 17.1% 174.54 3.1% 27.35 58.4 50.8 7.6
Fall Ecodeficit 61 21.1% 219.76 4.1% 22.87 50.5 40.9 9.6
Reduction in Annual
Average 30-Day
Minimum Flow
30 NA NA NA NA NA NA NA
Table 22. Current (WaterFALL®) and OASISTM 2050 projected hydrologic condition and associated riffle-
run fish guild diversity in the Eno River State Park stream segment (MGD = Million Gallons per Day)
Flow Metric No. of
Days
Current
EcoDeficit
(WaterFALL®)
OASISTM
Projected 2050
EcoDeficit
Riffle-run Fish Guild Diversity
Biological Condition (%)
% MGD % MGD Current 2050 Difference
Annual Ecodeficit 365 2.5% 1.32 3.8% 2.00 96.4 91.0 5.5
Winter Ecodeficit 121 2.5% 2.04 4.2% 3.35 96.6 91.0 5.7
Spring Ecodeficit 91 3.7% 1.62 2.8% 1.36 93.9 89.2 4.7
Summer Ecodeficit 92 5.3% 0.88 5.4% 1.09 85.4 70.6 14.8
Fall Ecodeficit 61 3.5% 1.13 3.0% 0.51 92.7 86.4 6.3
Reduction in Annual
Average 30-Day
Minimum Flow
30 1.7% 0.04 13.1% 0.95 97.3 76.3 21.0

45
Table 23. Current (WaterFALL®) and OASISTM 2050 projected hydrologic condition and associated riffle-
run fish guild diversity in the Neuse River at Goldsboro (MGD = Million Gallons per Day, “NA”
indicates an increase in the annual average 30-day minimum flow)
Flow Metric No. of
Days
Current
EcoDeficit
(WaterFALL®)
OASISTM
Projected 2050
EcoDeficit
Riffle-run Fish Guild Diversity
Biological Condition (%)
% MGD % MGD Current 2050 Difference
Annual Ecodeficit 365 12.8% 203.75 1.7% 25.45 81.7 79.2 2.5
Winter Ecodeficit 121 12.8% 277.82 2.5% 57.35 82.7 79.3 3.4
Spring Ecodeficit 91 12.5% 141.74 2.5% 32.14 79.3 75.3 4.1
Summer Ecodeficit 92 17.1% 174.54 3.1% 27.35 52.8 44.1 8.7
Fall Ecodeficit 61 21.1% 219.76 4.1% 22.87 55.9 47.4 8.6
Reduction in Annual
Average 30-Day
Minimum Flow
30 NA NA NA NA NA NA NA

46

Figure 25 (A and B). Step-by-step application of Ecological Flow Framework (using the summer
ecodeficit – benthic EPTr flow-biology relationship) applied to stream segment at Eno River State Park
and the Neuse River at Goldsboro.
(A) Step 1 - Plot current and projected summer ecodeficits and associated biological condition, using the regression
equation (see Table 17 for equation slope and intercept values) and WaterFALL and OASIS flow records,
respectively. These % ecodeficits can be translated into MGD by relating the areas under the curves during the
defined time period (i.e., JulySeptember) at the Eno River State Park and Neuse River at Goldsboro locations (see
Gao et al. (2009) for additional information). (B) Step 2 - Based on the Ecological Flow Category of each stream or
river segment, determine the corresponding Biological Response Threshold and calculate the associated summer
ecodeficit using the regression equation. In this example, the stream segment at the Eno River State Park and the
Neuse River at Goldsboro are in the Excellent and Fair Ecological Flow categories which correspond to 5% and 15%
change in biological condition Biological Response Thresholds, respectively. A “caution flag” is triggered for the
stream segment at the Eno River State Park because the 2050 projected summer ecodeficit may cause a change in
biological condition that is greater than 5%.

(A)
(B)

47

Figure 26. Ecological Flow Framework (using the summer ecodeficit – riffle-run fish guild diversity
flow-biology relationship) applied to stream segment at the Eno River State Park and the Neuse River
at Goldsboro (MGD = Millions of Gallons per Day). The values in this figure are determined using the
same step-by-step approach described in Figure 25 (A and B).

48
5. ACKNOWLEDGEMENTS
RTI and USGS would like to acknowledge the contributions, in alphabetical order, of Rebecca Benner
(TNC), Tom Cuffney (USGS), Mary Davis (SALCC/SARP), Michele Eddy (RTI), Robert Dykes (RTI), Chris
Goudreau (NR WRC), Phillip Jones (RTI), Jim Mead (EDF volunteer), Kimberly Meitzen, (TNC), Lauren
Patterson (RTI), Sam Pearsall (EDF), Jennifer Phelan (RTI), and Fred Tarver (NCDENR).

49
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