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Susitna-Watana Hydroelectric Project Document
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Title:
A holistic framework for environmental flows determination in hydropower
contexts : 2013 project report SuWa 226
Author(s) – Personal:
Ryan A. McManamay, Mark S. Bevelhimer
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Oak Ridge National Laboratory
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Susitna-Watana Hydroelectric Project document number 226
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ORNL/TM-2013/159
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Oak Ridge, Tenn. : Oak Ridge National Laboratory, [2013]
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April 2013
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vii, 52 p.
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Oak Ridge National Laboratory ORNL/TM-2013/159
A Holistic Framework for Environmental
Flows Determination in Hydropower
Contexts
2013 Project Report
Submitted to
The United States Department of Energy (DOE)
April 2013
Submitted by
Ryan A. McManamay, Ph.D.
Postdoctoral Research Associate
ORNL Water Power Program
Mark S. Bevelhimer, Ph.D..
Senior Scientist
Oak Ridge National Laboratory
Oak Ridge National Laboratory ORNL/TM-2013/159
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Oak Ridge National Laboratory ORNL/TM-2013/159
A Holistic Framework for Environmental Flows Determination in
Hydropower Contexts
2013 Project Report
Ryan A. McManamay and Mark S. Bevelhimer
April 2013
Prepared by
OAK RIDGE NATIONAL LABORATORY
Oak Ridge, Tennessee 37831-6283
managed by
UT-BATTELLE, LLC
for the
U.S. DEPARTMENT OF ENERGY
under contract DE-AC05-00OR22725
*Corresponding Author:
Ryan A. McManamay
Oak Ridge National Laboratory
1 Bethel Valley Road
Bldg 1505
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Email: mcmanamayra@ornl.gov
Phone: 865-241-8668
Oak Ridge National Laboratory ORNL/TM-2013/159
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Oak Ridge National Laboratory ORNL/TM-2013/159
i
ABSTRACT
Among the ecological science community, the consensus view is that the natural flow regime
sustains the ecological integrity of river systems. This prevailing viewpoint by many environmental
stakeholders has progressively led to increased pressure on hydropower dam owners to change plant
operations to affect downstream river flows with the intention of providing better conditions for aquatic
biological communities. Identifying the neccessary magnitude, frequency, duration, timing, or rate of
change of stream flows to meet ecological needs in a hydropower context is challenging because the
ecological responses to changes in flows may not be fully known, there are usually a multitude of
competing users of flow, and implementing environmental flows usually comes at a price to energy
production. Realistically, hydropower managers must develop a reduced set of goals that provide the
most benefit to the identified ecological needs.
As a part of the Department of Energy (DOE) Water Power Program, the Instream Flow Project
(IFP) was carried out by Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory
(PNNL), and Argon National Laboratory (ANL) as an attempt to develop tools aimed at defining
environmental flow needs for hydropower operations. The application of these tools ranges from national
to site-specific scales; thus, the utility of each tool will depend on various phases of the environmental
flow process. Given the complexity and sheer volume of applications used to determine environmentally
acceptable flows for hydropower, a framework is needed to organize efforts into a staged process
dependent upon spatial, temporal, and functional attributes. By far, the predominant domain for
determining environmental flows related to hydropower is within the Federal Energy Regulatory
Commission (FERC) relicensing process. This process can take multiple years and can be very expensive
depending on the scale of each hydropower project. The utility of such a framework is that it can
expedite the environmental flow process by 1) organizing data and applications to identify predictable
relationships between flows and ecology, and 2) suggesting when and where tools should be used in the
environmental flow process. In addition to regulatory procedures, a framework should also provide the
coordination for a comprehensive research agenda to guide the science of environmental flows. This
research program has further reaching benefits than just environmental flow determination by providing
modeling applications, data, and geospatial layers to inform potential hydropower development.
We address several objectives within this document that highlight the limitations of existing
environmental flow paradigms and their applications to hydropower while presenting a new framework
catered towards hydropower needs. Herein, we address the following objectives: 1) Provide a brief
overview of the Natural Flow Regime paradigm and existing environmental flow frameworks that have
been used to determine ecologically sensitive stream flows for hydropower operations. 2) Describe a new
conceptual framework to aid in determining flows needed to meet ecological objectives with regard to
hydropower operations. The framework is centralized around determining predictable relationships
between flow and ecological responses. 3) Provide evidence of how efforts from ORNL, PNNL, and
ANL have filled some of the gaps in this broader framework, and suggest how the framework can be used
to set the stage for a research agenda for environmental flow.
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ACKNOWLEDGEMENTS
The authors would like to acknowledge and thank following individuals and programs for providing
comments and support of this report.
DOE Water Power Program:
Hoyt Battey
Thomas Heibel
Oak Ridge National Laboratory:
Brennan T. Smith
Shelaine C. Hetrick
Shih-Chieh Kao
Henriette I. Jager
Virginia Polytechnic Institute and State University
Emmanuel A. Frimpong
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TABLE OF CONTENTS
ABSTRACT ................................................................................................................................................................................... i
TABLE OF CONTENTS ......................................................................................................................................................... iii
LIST OF FIGURES ..................................................................................................................................................................... v
LIST OF TABLES .................................................................................................................................................................... vii
1. INTRODUCTION .................................................................................................................................................. 1
1.1 The Natural Flow Regime Paradigm and Relations to Hydropower ................................ 2
1.2 Existing Environmental Flow Frameworks .................................................................................... 3
2. AN ALTERNATIVE ENVIRONMENTAL FLOW FRAMEWORK FOR HYDROPOWER ........... 8
2.1 Context .............................................................................................................................................................. 11
2.1.1 Hydrologic Classes (D) ...................................................................................................... 12
2.1.2 Class Predictive Models (T) .............................................................................................. 12
2.1.3 Eco-Class Linkages (D,T) ................................................................................................... 12
2.1.4 Watershed Geomorphic Classes (D) ................................................................................ 15
2.1.5 Dam Operations (D) .......................................................................................................... 15
2.2 Assessment ..................................................................................................................................................... 18
2.2.1 Daily-Seasonal Flow Statistics (T) .................................................................................... 19
2.2.2 Sub-daily Flow Statistics (T) ............................................................................................. 19
2.2.3 Hydrologic condition (D) .................................................................................................. 19
2.2.4 Ecological Geospatial Data (T).......................................................................................... 26
2.2.5 Ecological condition (D) .................................................................................................... 26
2.3 Scoping.............................................................................................................................................................. 31
2.3.1 Ecological Targets (D) ....................................................................................................... 31
2.3.2 Eco-Evidence Tools (T) ..................................................................................................... 31
2.3.3 Key Hydrologic and Ecological Indicators (R) ................................................................ 32
2.3.4 Identify Information Gaps (R) .......................................................................................... 33
2.4 Prescription ................................................................................................................................................... 33
2.4.1 Alternative Flow Scenarios (D) ........................................................................................ 33
2.5 Feasibility Analysis .................................................................................................................................... 33
2.4.2 Instream Flow Tools (T) ................................................................................................... 32
2.4.3 Habitat Connectivity (D) ................................................................................................... 32
2.4.4 Reach Geomorphic Classes (D) ......................................................................................... 32
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2.4.5 Water Quality (D) .............................................................................................................. 33
3. APPLYING THE HEFLOW FRAMEWORK TO HYDROPOWER CONTEXTS ............................ 38
4. THE HEFLOW FRAMEWORK, MARKET ACCELERATION, AND ACCOMPLISHMENTS ... 39
5. REFERENCES ....................................................................................................................................................... 43
6. APPENDIX- ABSTRACTS OF DOE SUPPORTED INSTREAM FLOW PROJECT WORK ..... 51
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LIST OF FIGURES
Figure 1. The Ecological Limits of Hydrologic Alteration (ELOHA) framework. From Poff et al. (2010). 5
Figure 2. Five core elements make up the application steps of the HEFLOW framework and operate
across variable spatio-temporal scales . The core elements are synonymous with application steps with
increasing detail and finer spatial resolution. All elements can span the entire hydrologic resolution axis
(annual to sub-daily time-scales). ............................................................................................................... 10
Figure 3. The HEFLOW framework. Each of the core elements are steps or applications ranging from
national to site-specific spatial resolutions. Long vertical rectangular boxes represent data layers whereas
dashed-line white boxes represent tools that connect data layers. Orange boxes represent stakeholder
input information (e.g. ecological targets) or framework output (e.g. key hydrologic/ecological
indicators). The framework hinges upon the development of predictable flow-ecology relationships
(central arrow) which occurs in the scoping phase and connects the first half of the framework to the
second half. ................................................................................................................................................. 11
Figure 4. US hydrologic classes ranging from intermittent to highly stable flows. Variables within each
class can be represented by a typical range of variation or normal tendency (box and whisker plots). Box
and whisker plots represent distribution of mean annual runoff and daily variation according to 15
hydrologic classes. Modified from McManamay et al. (2013b). ................................................................ 13
Figure 5. Developing a predictive linkage between the hydrologic classification and fish traits applied to
8-digit hydrologic unit codes (top). Box and whisker plots of distributions of traits (proportion of
periodic species, left, and serial spawning index, right) within hydrologic classes. Hydrologic classes
were sorted from high runoff to highly intermittent. Modified from McManamay et al. (2013a). ............ 14
Figure 6. Physiographic provinces across the US that could be used in a geomorphic classification.
(Inset) An example of a stream-reach gradient classification conducted for the North Eastern US (data
from WMI 2012). ........................................................................................................................................ 16
Figure 7. Example of a simple hydropower project containing single dam and powerhouse (Kingsford
Project) and a complex project containing multiple developments, dams within a development, and
separate structures for dams and powerhouses (East Fork Project). Reaches A and B experience
extremely different flows despite being located within the same project. Reach A refers to the bypass
channel (flow-diverted reach) below Bear Creek Dam characterized by extreme low flow volumes.
Reach B is located below the Cedar Cliff Powerhouse tailrace and receives high flow pulses. ................. 18
Figure 8. Tri-point continuum of hydrologic and ecological condition for a given hydropower project. A
project may (A) have little modification to flow, (B) modify the timing and distribution of flows without
large losses to annual water budget, or (C) divert large quantities of water thereby reducing the water
budget thereby limiting the quantity and quality of habitat. Likewise a project may (D) have little
modification to natural biodiversity, (E) have highly modified river communities that support ecosystem
services, such as sportfisheries, or (F) have extensive losses to ecosystem services. Associations among
hydrologic and endpoints are likely to exist; however, ecological endpoints should not be viewed as
directly related to hydrologic endpoints (e.g. B does not necessarily result in E). ..................................... 21
Figure 9. Dam-regulated stream gages (n=1,180) assigned to hydrologic classes using the class-
predictive model (top). Outliers (red dots) detected using Mahalanobis distances calculated between each
regulated gage and the centroid of the unregulated gages within each hydrologic class (bottom). The size
of the dots represent the total upstream dam storage above each gage corrected for drainage area. From
McManamay et al. (2013b). ........................................................................................................................ 22
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Figure 10. A comparison of hydrologic conditions for peaking and run-of-river hydropower projects
assessed using sub-daily or daily statistics. The comparison yielded opposing results for subdaily and
daily statistics. Sub-daily statistics yield more consistent results than daily statistics when assessing
hydrologic conditions among operations. From Bevelhimer et al. (2013).................................................. 23
Figure 11. An assessment of the hydrologic condition of the Snake River below Hells Canyon Dam,
Idaho. (A) Using class prediction models, USGS gage 13290450 was classified as a Snowmelt 2 type
stream. (B) Comparisons of the current flow below Hells Canyon Dam to the Snowmelt 2 hydrologic
profile (10th to 95th percentile of standardized flows) reveal departures from the relative magnitude of
annual maxima and seasonal baseflows. Standardized flow calculated by dividing each year by maximum
flow. ............................................................................................................................................................ 24
Figure 12. (A) Assessing departures in a given variable for the Snake River below Hells Canyon Dam
from the central tendency represented as the inter-quartile range for the Snowmelt 2 Class (gray section of
box and whisker plot represent interquartile range (IQR) whereas error bars represent 95th percent
confidence interval). (B) Percent changes of USGS 13290450 from the IQR of the Snowmelt 2 class for
27 daily flow statistics. ............................................................................................................................... 25
Figure 13. Comparison of the Brule River for peaking conditions (Pre-relicensing, 1989-1995) and run-
of-river conditions (Post-relicensing, 1996-2013) with respect to the interquartile ranges of 23 daily
hydrologic statistics represented by the Super-stable Groundwater Class (Class 5) and the Snowmelt 2
Class (Class 8). Although the predictive model suggested shared membership between Class 5 and 8,
results of the hydrologic condition assessment suggest that Class 5 is more appropriate. Large changes in
most hydrologic variables from pre- to post- relicensing were not observed. However, for evidence
changes post-relicensing, hydrologic conditions were more similar to the normal tendency represented by
Class 5. ........................................................................................................................................................ 29
Figure 14. Examples of ecological geospatial data sets. (A) Fish sampling point locations provided by
two data sources for the U.S.. (B) Multiple sources can be combined to create composite datasets for
regions, such as fish sampling locations within the Appalachicola-Flint and Alabama Coosa Tallapoosa
River basins. (C) Combined datasets can be summarized to create localized ecological layers to support
assessing hydropower project ecological condition. ................................................................................... 30
Figure 15. An example of an Eco-Evidence approach (modified from McManamay et al. 2013d). Based
on literature compilation, work groups can develop databases representing a regional knowledge base.
The database can be used to extract flow-ecology relationships, develop predicted responses to flow
restoration, or isolate key hydrologic/ecological indicators for a specific context. .................................... 32
Figure 16. (A) Compilation of regional hydrologic information (USGS gages and dam spillage) and fish
sampling locations for the Upper Tennessee River Basin. (B) Based on multivariate models, simulations
can yield predictive flow-ecology relationships to predict fish richness or riparian vegetation responses to
changes in flow. Modified from McManamay et al. (2013c). ................................................................... 31
Figure 17. Implementation of flows following application of HEFLOW framework. Adaptive
management should be used to monitor flows after implementation. However, for all parties to agree to
adaptive management, hydropower and environmental stakeholders should both have some level of
mutual perceived risk. Based on results of monitoring, collaborative decision making can be used to
determine final flow regime for the length of the new license. .................................................................. 38
Figure 18. Accomplishments made by ORNL, PNNL, and ANL during the course of the Instream Flow
Project (IFP) supported by DOE. Each cell represents a data set or tool that falls within a particular
element/or application within the HEFLOW framework (see Figure 2 for reference). Accomplishments
made related to- and unrelated to current DOE support are also provided. ................................................ 41
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LIST OF TABLES
Table 1. Mode-of-operation classes are listed along with the frequency of 432 power plants surveyed
within each class, the range in generation capacity in megawatts (MW), and the description of each class.
Generation capacity is the capacity of a facility to generate electricity given the flow volume, hydraulic
head, and number and type of generator-turbine units. ............................................................................... 17
Table 2. Seven sub-daily flow statistics and their descriptions (from Bevelhimer et al. 2013). ................ 20
Table 3. Examples of alternative flow scenario components to be tested during feasibility studies.
Alternative scenarios can represent one to many different flows within each component and/or one to
many different combinations of components. ............................................................................................. 31
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1. INTRODUCTION
Among the ecological science community, the consensus view is that the natural flow regime (i.e.
the dynamic quantity, timing, and variation of natural stream flows) sustains the ecological integrity of
river systems (Poff et al. 1997; Bunn and Arthington 2002; Poff et al. 2010). This prevailing viewpoint by
many environmental stakeholders has progressively led to increased pressure on hydropower dam
owners to change plant operations to affect downstream river flows with the intention of providing
better conditions for aquatic biological communities. These proposed changes often include moving
away from peaking operations to run-of-river operations, at the expense of energy losses, based on
the assumption that downstream biological communities will improve under more natural flow
regimes. It is our opinion that instream flows are the greatest obstacle to conventional hydropower
market acceleration.
Although many examples exist where project operations have moved from peaking to more
natural run-of-river conditions (Haas et al. 2013), a complete reinstatement of natural flow conditions is
unfeasible for many project operations due to large losses in energy or losses in services provided by
projects, e.g. recreational boating releases, tailwater fisheries. In an ideal world, a reasonable
compromise is found by identifying key characteristics of the flow regime that are amenable or non-
conducive to healthy aquatic communities and using that information to identify possible mitigation
opportunities. However, understanding the ecological needs of the aquatic community and using that
information to implement environmental flows for regulated rivers is a complex process, complicated by
the operational, socio-economic, physio-chemical, morphologic, and finally, ecological context of each
and every dam (McCartney 2009).
Defining these key elements of the flow-regime to improve downstream river communities is the
“golden nugget” of environmental flow science. The seemingly simple question, “How much flow does a
river need?”, remains to be answered (Richter et al. 1996) – in part, because it is not a simple question,
but a very complex issue plagued by varying spatio-temporal ecological and societal needs. One of the
greatest needs for environmental flow science to date is creating general and transferable relationships
between flow and ecology (Poff and Zimmerman 2010). These relationships cannot replace site-specific
knowledge, but provide grounds for streamlining the process of defining environmental flow needs for a
particular river system. In addition, tools that support the process of developing flow-ecology
relationships reduce the complexity of defining the critical aspects of the flow regime by focusing efforts
on key aspects of the river’s hydrograph and key ecological targets.
As a part of the Department of Energy (DOE) Water Power Program, the Instream Flow Project
(IFP) was carried out by Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory
(PNNL), and Argon National Laboratory (ANL) as an attempt to develop tools aimed at defining
environmental flow needs for hydropower operations (see Figure 18). The application of these tools
ranges from national to site-specific scales; thus, the utility of each tool will depend on various phases of
the environmental flow process. For example, tools at the national scale may provide a geographic
context to identify flow-related issues and organize environmental flow recommendations whereas tools
at the local, site-specific scale may provide an assessment of the feasibility of different alternative flow
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scenarios (Figure 18). Given the complexity and sheer volume of applications used to determine
environmentally acceptable flows for hydropower, a framework is needed to organize efforts into a staged
process dependent upon spatial, temporal, and functional attributes. By far, the predominant domain for
determining environmental flows related to hydropower is within the Federal Energy Regulatory
Commission (FERC) relicensing process. This process can take multiple years and can be very expensive
depending on the scale of each hydropower project. The utility of such a framework is that it can
expedite the environmental flow process by 1) organizing data and applications to identify predictable
relationships between flows and ecology, and 2) suggesting when and where tools should be used in the
environmental flow process. In addition to regulatory procedures, a framework should also provide the
coordination for a comprehensive research agenda to guide the science of environmental flows. This
research program has further reaching benefits than just environmental flow determination by providing
modeling applications, data, and geospatial layers to inform potential hydropower development.
We address several objectives within this document that highlight the limitations of existing
environmental flow paradigms and their applications to hydropower while presenting a new framework
catered towards hydropower needs. Our objectives include the following:
Provide a brief overview of the Natural Flow Regime paradigm and existing environmental flow
frameworks that have been used to determine ecologically sensitive stream flows for hydropower
operations.
Describe a new conceptual framework to aid in determining flows needed to meet ecological
objectives with regard to hydropower operations. The framework is centralized around
determining predictable relationships between flow and ecological responses.
Provide evidence of how efforts from ORNL, PNNL, and ANL have filled some of the gaps in
this broader framework, and suggest how the framework can be used to set the stage for a
research agenda for environmental flow (Figure 18).
1.1 The Natural Flow Regime Paradigm and Relations to Hydropower
The Natural Flow Regime is defined as the magnitude, frequency, duration, timing, and rate of
change of flow events that characterize the hydrology of natural river environments (Poff et al. 1997;
Bunn and Arthington 2002; Poff et al. 2010). Flow has been termed a “master-variable” that organizes
the physio-chemical template of river habitats that aquatic and riparian communities depend upon for
survival (Power et al. 1995; Poff et al. 1997). For example, flow variability has been shown to create and
maintain habitats (Trush et al. 2000), which are essential to supporting riverine communities (Poff and
Allan 1995; Bunn and Arthington 2002; Herbert et al. 2003; Pyron and Lauer 2004). Not surprisingly,
extensive literature suggests that losses to hydrologic variability can cause dramatic changes in river
communities (Pringle et al. 2000; Roy et al. 2005; Freeman and Marcinek 2006; Poff and Zimmerman
2010; Carlisle et al. 2011).
Many studies have assessed the hydrologic effects of dam operations, the majority of which have
documented reductions in natural flow variability among daily, seasonal, or annual time scales (Magillan
and Nislow 2001, 2005; Pyron and Neumann 2008; Poff et al. 2007). The ecological effects of unnatural
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subdaily flow variations, like that caused by hydropower peaking, has also received substantial attention
(for a review, see Cushman 1985). Because the overall habitat template within river systems is partially
organized by flow, deviation from naturally variable flow regimes affects multiple habitat factors
simultaneously. For example, reductions in the hydrologic connectivity of a river with its floodplain alter
the morphology of the channel that carries its flow. Altered timing, magnitude, frequency, and duration
of floods that provide pulse flows to riparian zones can induce changes in organic and sediment inputs
(Poff et al. 1997; Trush et al. 2000; Nislow et al. 2002). Likewise, losses in larger flood events tend to
decrease bankfull area, decrease sinuousity, and increase riparian vegetation due to encroachment
(Gordon and Meentemeyer 2006). Flow and temperature are also related (Caissie 2006). For example,
larger releases from dams result in greater thermal buffering capacity whereas reductions in discharge
below reservoirs result in lower thermal buffering capacity and generally, higher annual temperatures
(Caissie 2006). However, much of the deviation in temperature in regulated rivers from natural regimes
results from releases from stratified layers of the impoundment. For example, hypolimnetic releases can
cause dramatic reductions in temperature (Pozo et al. 1997; Krause et al. 2005) whereas releases from the
surface of the reservoir can lead to increases (Lessard and Hayes 2003; Caissie 2006).
1.2 Existing Environmental Flow Frameworks
Globally, a great deal of effort has been devoted to improving flows for ecological communities
in regulated river systems (Tharme 2003; Roni et al. 2008). During the late 1960s, the field of ‘instream
flows’ arose as an attempt to bring balance to the various uses of water flowing through river systems
(Annear et al. 2004). Instream flow (IF) is defined as the amount of water needed in a stream to
adequately support downstream uses, including sustaining ecological communities (Annear et al. 2004).
The term ‘environmental flows’ began being used in the 1990s as a more holistic description of the
quantity, timing, and quality of water flows required to sustain riverine ecosystems and human
livelihoods that depend on them. Determining the amount and timing of flows needed to sustain
ecosystems under a full array of competing uses is a daunting challenge. Not surprisingly, there is a full
spectrum of techniques used to assess IF needs, depending on objectives and the degree and type of
competing uses. The Instream Flow Council (IFC) recognizes over 30 different documented methods,
ranging from low effort (office only) to high effort (intensive field work and modeling). For
simplification, Annear et al. (2004) categorizes IF methods into one of three types: 1) Standard setting, 2)
Incremental, and 3) Monitoring/diagnostic. Standard setting is typically policy-driven and sets limits to
determine appropriate flow regimes (Stalnaker et al. 1995). Incremental methods are among the most
time-intensive and analyze stage-specific ecological/habitat responses within a stream channel to compare
alternative flow scenarios (Stalnaker et al. 1995; Annear et al. 2004). Monitoring/diagnostic methods
assess river conditions over time with respect to flow regimes and emphasize the importance of adaptive
management. The methods can be further categorized by the resource component being analyzed: 1)
hydrology, 2) biology, 3) geomorphology, 4) water quality, and/or 5) connectively (floodplain
inundation).
Among the simplest standard-setting IF techniques is the Tennant or Montana method. The
Tennant method evaluates estimated habitat quality (i.e. biological response) at various flows using
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limited field measurements, hydrologic records, and photographs of the stream channel (Tennant 1976).
This method can be used as a reconnaissance level tool for determining acceptable seasonably various
flow magnitudes in situations where there is little or no heavy competing uses (Annear et al. 2004). Other
simple reconnaissance level approaches may be used to assess and characterize complex hydrologic
conditions of river systems in order to provide ecologically meaningful metrics and to prioritize various
aspects of the hydrograph for future emphasis. Richter et al. (1996) originally developed the Indicators of
Hydrologic Alteration (IHA) and followed with the Range of Variability Approach (RVA) (Richter et al.
1997), both monitoring/assessment approaches. The methods are dependent upon the presence of pre and
post-dam-regulation discharge data or nearby unregulated comparable stream gages. Multiple
ecologically relevant hydrologic indices are calculated for pre and post-regulation periods of record and
then compared. However, the IHA and RVA methods are limited in that they typically have no
quantitative relationship to in-stream ecological needs, unless accompanied by biological monitoring data.
Of the most complex IF techniques, Instream-Flow-Incremental-Methodology (IFIM) approaches assess
hydrology, biology, sediment transport, and water quality under various flow regime alternatives (Bovee
et al. 1998). Intensive field work and modeling is used to predict habitat over a range of given flows.
IFIM approaches can range from simple relations between hydrologic indices and aquatic habitats to more
complex hydrodynamic models, which can be linked to multiple components of the river ecosystem
(Tharme 2003).
Although even the most complex IFIM approaches can be scientifically sound and provide
assessments of management alternatives, IFIM approaches may only be applicable to the reach under
study (Moir et al. 2005) and may not consider the full complexity of all ecosystem components (Anderson
et al. 2006). Recently, more tools have been developed as holistic alternatives to traditional IF techniques
to provide information in complex management situations. For example, King and Louw (1998)
developed the Building Block Methodology (BBM) to address all riverine ecosystem component needs
(including societal) using existing knowledge and expert opinion in a structured workshop process.
Brown et al. (2000) developed the Downstream Response to Imposed Flow Transformations (DRIFT),
which builds upon the BBM approach but develops a quantitative database of biophysical and
sociological linkages to flow regimes and then evaluates biophysical, social, and economic responses
under various flow scenarios.
Within most holistic approaches, making predictable assessments of potential ecological
responses to changes in flow regimes requires quantitative information on the relationships between
hydrology and key components of river ecology; however, many environmental flow assessments proceed
without the ideal knowledge base required (Arthington et al. 2003). Recent environmental flow
management has been thwarted, at least to some degree, by the absence of quantitative and transferable
relationships between flow and ecology (Poff et al. 2010; Poff and Zimmerman 2010). Flow-ecology
relationships represent a relationship between changes in flow (e.g. 25% decrease in daily flow
magnitude) and changes in some ecological response (e.g. 30% decrease in fish richness) (Poff et al.
2010; Poff and Zimmerman 2010). Isolating these general, widely-applicable, flow-ecology relationships
has been proposed as the template needed to inform water policy negotiations, including roundtable
discussion among environmental and hydropower stakeholders.
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Flow-ecology relationships, once developed, can provide ‘rules of thumb’ for streams within a
given region or of a particular type (Poff et al. 2010). The need for standardized flow-ecology
relationships provided the motivation for creating flow classifications where streams are grouped
according to similar hydrologic properties (e.g. Poff and Ward 1989; Poff 1996; Kennard et al. 2010);
therefore, instead of managing for every individual river, classes of rivers with similar hydrologic
properties can be used to develop and expedite the process of determining environmental flow
recommendations (Arthington et al. 2006). The need for quantitative information to support
environmental flow development led to the development of a process known as the Ecological Limits of
Hydrologic Alteration (ELOHA) (Figure 1, Poff et al. 2010). ELOHA is the product of a consensus view
of 19 international scientists and leaders in the field of environmental flow science (Poff et al. 2010) and
has been considered the most holistic environmental flow framework to date (Richter et al. 2012).
ELOHA has been partially applied in at least six states and three interstate river basins to determine
environmental flow needs at the regional scale (Kendy et al. 2012); however, its applicability to
hydropower operations has received little attention (except see McManamay et al. 2013c).
Figure 1. The Ecological Limits of Hydrologic Alteration (ELOHA) framework. From Poff et al. (2010).
Within the ELOHA framework, river classes provide a stratified approach to assess hydrologic
alterations and flow-ecology relationships (Arthington et al. 2006; Poff et al. 2010). The five specific
steps of the ELOHA procedure include: 1) building a hydrologic foundation of baseline or “natural”
conditions, 2) classifying river types based on natural hydrology along with potential geomorphic
subclassification), 3) assessing flow alterations within each river class in relation to baseline conditions,
4) determining flow-ecology relationships for each river class, and 5) determining socially acceptable
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ecological limits, implementing water policies, and using adaptive management to adjust policies (Figure
1, Poff et al. 2010). Flow-ecology relationships (Step 4) represent univariate percent changes in ecology
and flow from natural or baseline conditions to the current or altered condition (Poff et al. 2010; Poff and
Zimmerman 2010). Although Poff et al. (2010) admit that other environmental variables (e.g.
temperature) may confound relationships between flow and ecology, very little quantitative basis is
provided for incorporating more specific context (e.g. geomorphology or dam operation) into determining
environmental flows.
To our knowledge, the only assessment of the ELOHA procedure for regulated rivers is provided
by McManamay et al. (2013c) who tested the utility of ELOHA in informing environmental flow
applications for hydropower dams in the Upper Tennessee River Basin. Although ELOHA sufficiently
provided a template to construct a database of baseline hydrologic information, McManamay et al.
(2013c) suggested that ELOHA was insufficient in guiding environmental flow applications for specific
hydropower contexts for the following reasons: 1) Univariate relationships between flow and ecology
exclude morphology, temperature, and fragmentation and produce results insufficient to develop
quantitative and predictable relationships. 2) ELOHA relies on social roundtable discussion to identify the
relative consequences of scientific uncertainty in implementing environmental flows based on univariate
flow-ecology relationships. McManamay et al. (2013c) argued that this uncertainty is too high for
regulated river contexts and should be decreased through more scientific assessments, such as
multivariate model-building. 3) The ELOHA framework uses baseline conditions to formulate flow-
ecology relationships, which then inform the process of developing environmental flow standards (Figure
1). In many situations, baseline ecologic information may underrepresent the variation found in regulated
rivers because it is usually absent or is only available for smaller-sized streams. In addition, baseline
targets may be inappropriate for regulated systems because the baseline is not desired or only addresses
specific ecological needs compared to many users of regulated rivers (e.g. tailwater fisheries, etc). 4)
ELOHA is a preventative framework whereas most US hydropower contexts need a proactive framework.
Specifically, ELOHA sets socially acceptable “ecological limits” on hydrologic alterations so that water
policy standards can be implemented. While ELOHA is a very flexible framework, its strengths lie in the
fact that it predicts the ecological consequences of hydrologic alteration prior to the hydrologic alteration
taking place. Thus ELOHA may perform well in a development planning context. Rivers that are
currently regulated, however, need a restoration framework, i.e. one that assesses the current condition
and then suggests alternative flows for improving habitat conditions. 5) The process of using ELOHA to
inform management on a case-by-case basis may be limited given the scope and context of each river
system. US dam facilities are typically managed on a site-by-site basis within very specific contexts
based on dam operations, ecological and socio-economic needs, and physio-chemical considerations
(McCartney 2009). Thus, any framework will not remove the need for individual attention in regulated
river contexts, but should provide a template to expedite information assimilation needed to address more
specific needs.
According to Richter et al. (2012), the ELOHA framework is the best “available balance between
scientific rigor and cost of application for setting environmental flow standards.” However, the
associated costs of applying ELOHA frameworks to specific jurisdictions range from $100k to $2M,
depending on jurisdiction size and the extent of available biologic and hydrologic information (Richter et
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al. 2012). Even more so, time constraints are proving to be a larger obstacle to implementing the ELOHA
framework, especially in areas of intense competing water uses (Richter et al. 2012). Although there are
specific needs that remain to be addressed, the ELOHA framework provides some useful elements that we
build upon to create robust framework for use in hydropower situations. Rather than re-construct
frameworks for individual jurisdictions, U.S. hydropower operations could benefit from a universal
continental-wide framework.
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2. AN ALTERNATIVE ENVIRONMENTAL FLOW FRAMEWORK FOR HYDROPOWER
Over 1000 hydropower projects across the United States are subject to FERC relicensing
procedures (which typically re-occur on a 30-50 year basis; FERC 2013). Relicensing procedures
typically take 5-10 years. While relicensing ensures assessments of all project-related resources,
assessments of environmental flow issues typically require the most attention and time. Part of the
relicensing process involves evaluating how project operations have negatively impacted natural
resources, including stream flows and associated aquatic organisms. In addition, after many studies and
roundtable discussion, recommendations for habitat improvement (e.g. increased minimum flows, re-
establishing peak flows) and monitoring are approved. Although pre-project conditions (e.g. pre-dam
hydrology) can be useful in informing the relicensing and recommendation process, re-establishing the
full spectrum of pre-disturbance natural flows while maintaining some semblance of energy production is
unlikely. Hence, the baseline condition in a hydropower context cannot nor should not be fully dependent
upon pre-disturbance conditions, but dependent upon current conditions and how those conditions can be
improved. In other words, US hydropower regulations require a restorative rather than preventative
environmental flow framework.
Another challenge is that many frameworks base environmental flow recommendations solely on
hydrologic conditions (Richter et al. 1996) or regional flow-ecology relationships (Poff et al. 2010)
without taking into account hydrologic interactions with the stream channel. Separating a river channel
from the streamflow it carries is problematic (Trush et al. 2000). Complex ecohydraulics result from the
dynamic interactions between flow and channel morphology, which, in part, determine the ability of an
organism to carry out its life history requirements and exist in a given riverscape (Fausch et al. 2002). In
addition, water quality conditions can change dramatically depending on flow conditions (Caisse 2006).
Coarse assessments of flow-ecology relationships can be informative in isolating key ecological and
hydrologic indicators for site-specific studies; however, reach-specific feasibility analyses are still
required to develop suitable environmental flow recommendations.
As a part of the Integrated Licensing Process (ILP), scoping is used to identify and determine
issues related to each hydropower project that should be addressed by FERC (FERC 2013). Within the
scoping process, stakeholders can request applicants to conduct studies and gather information to address
issues and information gaps, such as the degree of biological improvement potentially provided by
alternative flow scenarios. Studies outlined in the scoping process, if justified and executed, can be
expensive in terms of time and money. For example, even after studies are completed, applicants must
hold meetings with stakeholders to discuss results with the potential that study plans may be further
modified. Thus, hydropower and environmental stakeholders would benefit from an organized approach
to expedite the preliminary application and scoping process by identifying information gaps in the
knowledge base, creating studies that more accurately address ecological needs, and prioritizing future
efforts. In relation to environmental flows, a framework advantageous to the ILP could expedite the
assessment of current project conditions, the identification and prioritization of relevant hydrologic and
ecologic indicators, and the process of developing alternative flow scenarios based on ecological needs;
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thus, more time could be allocated to creating and conducting feasibility studies rather than assessment
studies.
Identifying the neccessary magnitude, frequency, duration, timing, or rate of change of stream
flows to meet ecological needs in a hydropower context is challenging because the ecological responses
to changes in flows may not be fully known; there are usually a multitude of competing users of flow and
implementing environmental flows usually comes at a price to energy production. In addition, selecting
the most appropriate ecologically-sensitive alternative stream flows is a daunting task, plagued by an
over-abundance of hydrologic metrics (Olden and Poff 2003) and competing objectives. Realistically,
hydropower managers must develop a reduced set of goals that provide the most benefit to the identified
ecological needs. Reducing the uncertainty about which flow characteristics are most important from
an ecological perspective could likely lead to compromise solutions that provide both valuable
ecological services and load-following capabilities. While we suggest a modified approach to
determining environmental flows for hydropower needs, we are not proposing a full paradigm shift.
Indeed, we utilize many of the ideas proposed in current environmental flow frameworks, such as
assessments of regional patterns in natural hydrology, hydrologic alterations, and flow-ecology
relationships. For example, pre-disturbance hydrologic condition can be useful for determining the
hydrologic context of a given hydropower facility. However, rather than environmental flow
recommendations be based on these coarse assessments, we suggest that these assessments aid in
identifying information gaps and isolating specific hydrologic and ecologic elements to guide site-specific
analyses. An alternative framework could organize how these elements inform the process of narrowing
down environmental flow needs for hydropower situations.
In order to develop ecologically relevant stream flows to inform hydroelectric operations at the
national, regional, or site-specific scales, we have constructed a framework that provides five core
elements: 1) context, 2) assessment, 3) scoping, 4) prescription, and 5) feasibility. These five core
elements represent different stages of applications in hydropower relicensing/impact assessment and
inform management across various spatio-temporal scales (Figure 2). Specifically, context is provided at
larger scales to characterize the hydrologic, geomorphic, physio-chemical, and operational setting around
each hydropower project. Assessment can be conducted at national or regional scales and includes fully
describing the current hydrologic/ecologic conditions in relation to ecological objectives, obtaining
predictive flow-ecological needs relationships, and identifying key hydrologic and ecological indicators.
Scoping is used to isolate and prioritize information gaps at regional or site-specific scales. Based upon
best available knowledge at site-specific scales, prescription presents a series of alternative flow scenarios
based the assessment and scoping stages. Lastly, analyses are conducted to determine the feasibility (i.e.
ecological benefit versus impacts to project economics) of alternative flows at the site-specific scale.
Each of the five core elements make up the steps of the Hydropower Environmental Flow (HEFLOW)
framework (Figure 3). Each step is represented by foundational data layers or data sets (D). Specific
tools (T) utilize information from data layers and link core elements as they move from one stage to the
next (Figure 3). Similar to the ELOHA framework, the central theme of the process requires creating
flow-ecology relationships (arrow within the Scoping element, Figure 3); however, a much larger degree
of relevant information must be compiled and assimilated prior to developing these relationships. In
addition, these flow-ecology relationships help identify information gaps and structure site-specific
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analyses, rather than replace them. Ultimately, following the last stage of the framework (i.e. feasibility
analysis), flows are implemented and followed by adaptive management to monitor and make
adjustments.
Realistically, a framework cannot be, nor should it be, a step-by-step guide to implement
environmental flows in regulated river systems. While the framework organizes information and
planning processes, it requires input and roundtable discussion. Specifically, the context and assessment
elements, along with associated tools, may be available in conventional background datasets and
packages. However, the scoping, prescription, and feasibility elements will require individual attention
and input based on each specific hydropower project. For example, context and assessment will inform
the scoping process based on larger scale scientific process; however, determining the ecological targets
is a social element that requires stakeholder input.
In the sections that follow, we provide a brief description of tools and datasets that ORNL,
PNNL, and ANL have developed during the course of the IFP. Each of these datasets (D) and tools (T)
has begun to fill in the gaps of this larger proposed framework. Ultimately, we envision that each of these
elements will require adequate research to fully support the framework structure. In a tangible sense, the
core elements provide the structure for a larger centralized data repository and application capacity that
can serve both hydropower and environmental stakeholders.
Figure 2. Five core elements make up the application steps of the HEFLOW framework and
operate across variable spatio-temporal scales . The core elements are synonymous with
application steps with increasing detail and finer spatial resolution. All elements can span the
entire hydrologic resolution axis (annual to sub-daily time-scales).
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2.1 Context
A common trend in the development of frameworks for broad-scale management is creating
classification systems (Rosgen 1994; Poff 1996; Brandt 2000; Wehrly et al. 2003; Wollock et al. 2004;
Sowa et al. 2007). Because classification systems consolidate variability, they provide a context to
organize and generalize management actions at the national scale. Classes also provide an approach to
stratify analyses. For example, a dam in the Pacific Northwest is unlikely to have the same hydrologic,
geomorphic, and biophysical effect as a dam in the Southeast. Different hydro-geomorphic settings as
well as the type of dam operations will determine the extent and nature of hydrologic and geomorphic
impacts associated with hydropower development. Likewise, hydro-geomorphic contexts also provide a
preliminary estimate of what restoration or mitigation measures may be required, given the type of dam
operation.
Figure 3. The HEFLOW framework. Each of the core elements are steps or applications ranging
from national to site-specific spatial resolutions. Long vertical rectangular boxes represent data
layers whereas dashed-line white boxes represent tools that connect data layers. Orange boxes
represent stakeholder input information (e.g. ecological targets) or framework output (e.g. key
hydrologic/ecological indicators). The framework hinges upon the development of predictable
flow-ecology relationships (central arrow) which occurs in the scoping phase and connects the first
half of the framework to the second half.
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2.1.1 Hydrologic Classes (D)
Poff et al. (2007) proposed that dam-induced hydrologic effects could be generalized and further
suggested that losses in regionally-distinct hydrologic types resulted in a homogenization of fluvial
habitats across continental scales. Indeed, there are some general hydrologic elements that show
somewhat universal responses to dams. For example, typically, peak flood magnitudes are diminished
and minimum flows are inflated in river systems below dams (Richter et al. 1996; Magillan and Nislow
2001and 2005; Pyron and Neumann 2008; Poff et al. 2007; Fitzhugh and Vogel 2011). Likewise,
increases in reversals, i.e. abrupt positive/negative changes in flow, are common results of dam operation
(Richter et al. 1996; Mathews and Richter 2007). Although some generalities exist, McManamay et al.
(2012) showed that regulated streams within the Southeastern US were not homogeneous and displayed
highly variable responses in monthly, seasonal, and baseflows. Furthermore, McManamay et al. (2012)
found that hydrologic responses to dams were more predictable when stratified by hydrologic class
membership.
In order to provide a hydrologic setting for hydropower operations, McManamay et al. (2013b)
developed a hydrologic classification for the continental U.S. using daily discharge information from
2,618 USGS stream gages unregulated by dams (Figure 4). Fifteen distinct hydrologic classes were
isolated representing a full spectrum of hydrologic types from unstable intermittent to very stable, high
runoff systems (Figure 4). For a given hydrologic metric, each class provides a range of variation, i.e.
normal tendency (Figure 4). Thus, hydrologic classes provide a template to assess departures from the
normal tendency. Although the normal tendency represents the natural flow regime, it is not meant to
override or place an agenda on specific flow objectives. However, understanding departures from normal
tendencies provides a relative comparison of where project operations fit into a larger picture.
2.1.2 Class Predictive Models (T)
Many times, stream flow information is not available for a particular hydropower project prior to
dam construction. Thus, landscape and climate information may be critical to determining class
membership. Two types of class-predictive models were created using hydrologic metrics and
landscape/climate variables depending on the availability of hydrologic data (McManamay et al. 2013b).
If pre-dam regulation stream flow information is readily available, a hydrologic classification tool can be
used. In the absence of adequate stream flow information, the landscape/climate predictive tool can be
used.
2.1.3 Eco-Class Linkages (D,T)
The utility of classification systems in ecological management lies in their ability to consolidate
substantial information into digestible units thereby providing a more efficient means to achieve
conservation objectives. Specifically, classification systems are valuable in that they can be used to group
sites with similar character (Frimpong and Angermeier 2010), stratify analyses for monitoring and/or
experimentation (Wolock et al. 2004), prioritize aquatic conservation areas (Snelder et al. 2007), and
generalize ecological responses to disturbances (Bailey 1983). Sokal (1974) suggests that although
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classifications have many practical and applied outcomes, hypothesis generation is the greatest
determinant of success.
Figure 4. US hydrologic classes ranging from intermittent to highly stable flows. Variables within
each class can be represented by a typical range of variation or normal tendency (box and whisker
plots). Box and whisker plots represent distribution of mean annual runoff and daily variation
according to 15 hydrologic classes. Modified from McManamay et al. (2013b).
In order to provide templates for developing and testing ecologically relevant hypotheses,
classification systems created using environmental variables must be linked to ecological patterns.
Associations between the U.S. hydrologic classification and fish traits were developed in order to form a
template for generating flow-ecology hypotheses and supporting environmental flow standard
development (Figure 5). Developing linkages between hydrology and fish assemblages is advantageous
for three main reasons. First, many studies have shown that flow variability organizes fish assemblage
structure (e.g. Poff and Allan 1995; Jackson et al. 2001; Herbert et al. 2003; Pyron and Lauer 2004).
Secondly, spatially contiguous distributions for all freshwater fish species were readily available for the
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conterminous US (NatureServe 2004), thereby providing an effective means to apply a multi-regional
hydrologic classification to entire fish assemblages. Lastly, trait information (ecological, life history,
behavioral, physiological adaptations to the environment) for the majority of freshwater fish species in
North American was also available (Frimpong and Angermeier 2009). Tradeoffs in adaptive strategies
for fish (reproductive and life history traits) were observed across a spectrum of stable, perennial flow to
unstable intermittent flow, which suggests that fish traits vary predictably along hydrologic gradients
(Figure 5). In accordance with theory, periodic strategists were associated with stable, predictable flow
whereas opportunistic strategists were more affiliated with intermittent, variable flows (Winemiller and
Rose 1992; Winemiller 1995). Linkages between the uniqueness of hydrologic character and ecological
distinction among classes were developed, which may translate into predictions between losses in
hydrologic uniqueness and ecological community response.
Figure 5. Developing a predictive linkage between the hydrologic classification and fish traits
applied to 8-digit hydrologic unit codes (top). Box and whisker plots of distributions of traits
(proportion of periodic species, left, and serial spawning index, right) within hydrologic classes.
Hydrologic classes were sorted from high runoff to highly intermittent. Modified from
McManamay et al. (2013a).
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2.1.4 Watershed Geomorphic Classes (D)
Although geomorphic (stream channel morphology) responses to dam-altered flow have been
widely studied (Ligon et al. 1995; Grant et al. 2003; Gordon et al. 2004), broad frameworks for assessing
channel responses to dams are not as prevalent as flow. Most likely, this is due to unique individual
needs for specific river systems, but also to a lack of comprehensive datasets needed to create a
geomorphic classification data layer (compared to discharge information used to create hydrologic
classes). Grant et al. (2003) created a conceptual and quantitative framework for assessing the effect of
dams on morphology; however, application of the framework still depends on local site knowledge.
Providing a geomorphic context for hydropower operations is also very important as the existing
ecological community and ecological responses to changes in flow may be highly dependent upon
channel morphology and underlying geology. For example, within the same physiographic province, a
low-gradient gravel-bed river will have far different channel responses to dam-altered flow than a high-
gradient boulder-dominated river system, despite similarities in hydrology. Coarse classifications, such
as the presence of constrained/unconstrained floodplains or the ability of streams to migrate may increase
the predictive accuracy of assessing ecological responses to flow variation (Liermann et al. 2011,
McCargo and Peterson 2010).
Datasets have become increasingly available to address the potential geomorphic character of
hydropower locations at the national scale (Figure 6). Geomorphic classification frameworks could easily
incorporate hierarchical structure to provide applications at the landscape, watershed, or reach scale
(Figure 6). As an example, the range of sediment/bedload transport within geomorphic classes could
provide a normal tendency.
2.1.5 Dam Operations (D)
The way in which dams harness water for energy has implications for energy production, project
economics, and downstream hydrology. Dams that operate in a run-of-river mode (i.e. harnessing energy
soley based on incoming flows) will likely have far less influence on hydrology than those that operate in
a peaking mode (i.e. storing and releasing water to generate during peak demand). In reality, dam
operations do not fall neatly into one of these two broader categories, but represent a spectrum of
operations. Rather than characterizing the mode of operation on a project-by-project basis, a national
classification that groups projects by similarities in operational context can already provide a great
deal of information concerning context but also provides insight into assessments .
Two different sources were used to determine mode of operation for hydropower dams: 1) FERC
orders issuing new licenses for hydropower projects, and 2) internet sources and associated
documentation for US Army Corps of Engineers (USACE), US Bureau of Reclamation (USBOR), or
Tennessee Valley Authority (TVA) developments. FERC elibrary searches were conducted to compile
orders issuing new licenses. Within each order, project operation and facilities descriptions were
reviewed to obtain information on mode of operation. Each project may contain multiple dams and
associated powerhouses (Figure 7).
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Overall, 232 FERC orders and multiple internet sites for federal projects were reviewed. In total,
documentation for 432 power plants was searched and revealed a continuum of seven operation modes
from run-of-river (ROR) to strictly peaking (Table 1). Each mode-of-operation type, frequency of
power plants in each type, range in megawatt generation capacity, and descriptions are provided in Table
1. The majority of power plants operate as ROR or peaking facilities (Table 1). Approximately 50% of
power plants are non-integral to the dam, i.e. they harness energy from water being diverted from the dam
to the powerhouse and bypassing the natural river channel (special note: bypassed reaches vary
considerably in length, e.g. meters to kilometers). Operation types were subjectively ranked in order
from those with the least hypothetical flow impacts to those with the greatest hypothetical impacts. Thus,
based on the definitions for classes given in Table 1, ROR operations should cause the least amount of
alteration in daily and subdaily flows relative to peaking and storage release facilities (Table 1).
However, operations that involve diversions create another level of impacts to environmental flows as
they typically dewater bypassed channels leading to losses in flow volume (Figure 7).
Figure 6. Physiographic provinces across the US that could be used in a geomorphic classification.
(Inset) An example of a stream-reach gradient classification conducted for the North Eastern US
(data from WMI 2012).
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Table 1. Mode-of-operation classes are listed along with the frequency of 432 power plants surveyed
within each class, the range in generation capacity in megawatts (MW), and the description of each
class. Generation capacity is the capacity of a facility to generate electricity given the flow volume,
hydraulic head, and number and type of generator-turbine units.
Mode-of-Operation
Class
No. of
Power
Plants
MW
Capacity
Range
Description/Purpose
Run-of-river 154 0.01 - 436 Discharges from the project tailrace or dam
approximate the sum of inflows to the project
reservoir at any given time. Hydroelectric
generation is dependent upon natural incoming
flows. Minimize the fluctuation of the
reservoir surface elevation.
Reregulating 11 0.70 - 49.0 Stores and releases water to stabilize flow
fluctuations from upstream peaking or storage
release facilities and generates electricity.
Mitigation facility.
Run-of-river/Peaking 23 0.08 – 28.8 Operates as run-of-river for periods of time or
seasons (e.g. during fish spawning) and then
operates as a peaking facility the remainder of
time.
Reregulating/Peaking 1 9.6 Operates as reregulating facility for periods of
time or seasons (e.g. during fish spawning) and
then operates as a peaking facility the
remainder of time.
Intermediate Peaking 42 0.10 - 6809 Stores limited amounts of water for occasional
releases or moderates the intensity of peaking
for hydroelectric generation.
Run-of-
river/Upstream
Peaking
48 0.68 - 162 Operates as a run-of-river facility but harnesses
the energy from upstream storage releases or
peaking operations to generate electricity.
Peaking 153 0.46 - 912 Stores and releases water (high flow releases)
for hydroelectric generation. Typically large
reservoir fluctuations due to seasonal
drawdowns.
Total Number of
dams
432
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Figure 7. Example of a simple hydropower project containing single dam and powerhouse
(Kingsford Project) and a complex project containing multiple developments, dams within a
development, and separate structures for dams and powerhouses (East Fork Project). Reaches A
and B experience extremely different flows despite being located within the same project. Reach A
refers to the bypass channel (flow-diverted reach) below Bear Creek Dam characterized by extreme
low flow volumes. Reach B is located below the Cedar Cliff Powerhouse tailrace and receives high
flow pulses.
2.2 Assessment
Management strategies for addressing issues of flow alterations focus on getting stakeholders to
examine metrics of flow variability between unaltered and altered conditions in order to determine
environmental flow recommendations to support concurrent human and natural uses of rivers. The first
step involves the quantification of flow variability (both altered and unaltered conditions), and currently a
few widely-used tools are available to provide these assessments. However, another need is to address
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the current ecological condition e.g., does the system support high biodiversity or a recreationally
valuable sportfishery? The contextual databases inform this process by providing the groundwork to
make comparisons among projects that operate similarly and between projects, affected streams, and their
unregulated counterparts. Assessing a project’s hydrologic and ecological condition is essential to
eventually developing predictable relationships between flow and ecology.
2.2.1 Daily-Seasonal Flow Statistics (T)
Currently the most commonly used approaches for quantifying flow variability are based on
statistical analyses of daily-averaged flow records like the metrics computed by the Indicators of
Hydrologic Alteration (IHA) or Hydrologic Index Tool (HIT) software packages (Richter et al. 1996,
Henriksen et al. 2006). Again, this process typically ignores sub-daily hydrologic metrics.
2.2.2 Sub-daily Flow Statistics (T)
Hydropower peaking operations have the potential to alter downstream flows above natural
variations that occur over the course of a day, which are not captured by flow metrics based on daily-
averaged statistics. The majority of available contemporary tools to assess hydrology utilize daily data to
provide daily and seasonal hydrologic metrics (Richter et al. 1996, Olden and Poff 2003). Sub-daily flow
metrics and tools to calculate them have seen far less attention (Zimmerman et al. 2010; Meile et al.
2011). In order to fully address the impacts of hydropower operations with respect to naturally occurring
flow variability, it is necessary to quantify flow metrics at the sub-daily scale. Additionally, it is important
to evaluate sub-daily flow metrics with respect to their ability to correlate with changes in downstream
geomorphic processes and biologic responses that occur over a wide-range of temporal and spatial scales.
Although the hydrologic classification was created using daily and seasonal metrics, sub-daily metrics
provide a finer scale context related to dam operations and specific ecological responses. Bevelhimer et
al. (2013) summarized several sub-daily statistics that can be used to assess hydrology related to
hydropower projects (Table 2, Appendix).
2.2.3 Hydrologic condition (D)
Assessing hydrologic alterations is critical to understanding where a project sits in the spectrum
of natural to artificial environments. An unbiased assessment considers both types of environments
equally suitable as long as they both meet social and ecological demands. The hydrologic condition of a
project can be conceptualized as a tri-point continuum, which provides a baseline for moving towards
idealized conditions depending on ecological objectives (Figure 8). A project may modify the timing and
distribution of flows without large losses to annual water budget (endpoint B, Figure 8). However,
diversions reduce the water budget thereby limiting the quantity and quality of habitat (endpoint C, Figure
8). Both daily/seasonal and sub-daily flow metrics can be utilized to capture a project’s hydrologic
condition.
McManamay et al. (2013b) developed an approach to determine hydrologic condition in dam-
regulated gages based on hydrologic class membership. In short, the approach consisted of 1) assigning
dam-regulated gages to appropriate hydrologic classes based on class predictive models, 2) using
multivariate measures to assess deviation from the ‘normal tendency’ of hydrologic class, and 3 ) and
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determining outliers (river systems outside the normal tendency). Dam-regulated streams (1,180 gages)
were assigned to one of the expanded hydrologic classes using the class predictive model (Figure 9). Post-
dam construction hydrologic data for each regulated gage was obtained and daily statistics were
calculated. Mahalanobis distances (multivariate distance measure) were calculated between each
regulated gage and the centroid of the unregulated gages within each hydrologic class. Outliers were
detected as breaks (i.e. abnormalities) from the chi-squared distribution of Mahalanobis distances
according to Filmoser and Gschwandtner (2012) (Figure 9). The approach can be useful in determining
whether regulated streams are functioning within the bounds of the normal tendency.
Table 2. Seven sub-daily flow statistics and their descriptions (from Bevelhimer et al. 2013).
Metric Full Name Description
Daily CV Daily Coefficient of
Variation
The common statistical calculation of standard deviation dividing be
the mean of the 24 hourly flow values. Like daily range, daily
standard deviation is an indicator of degree of habitat and behavior
change.
DeltaDaily Standardized daily delta1 A variation of the percent of total flow metric, this metric is calculated
as the daily delta (i.e., difference between minimum and maximum)
divided by the daily mean for each day (adapted from Meille et al.
2011). This value is twice the standardized daily range as defined by
Lundquist and Cayan (2002) as the ratio of the amplitude (half of
daily range) of the diurnal cycle to total daily discharge over the
analysis period (e.g., 24 hr).
DeltaAnnual Standardized daily delta2 Same as DeltaDaily except the difference between the daily
minimum and maximum is divided by the mean annual daily flow.
HrlyRamp Standardized maximum
ramp rate
Greatest hourly change in flow during a 24-hr period (Halleraker et
al. 2003, Meille et al. 2011) divided by the daily mean.
Reversals Reversals Number of changes between rising and falling periods of the
hydrograph; adapted from similar metric derived with daily data (The
Nature Conservancy 2007). Counting reversals with hourly data can
be a bit misleading since even the slightest change in either direction
could produce a reversal count that is insignificant relative to general
trends in the hydrograph. Therefore, calculation of reversals should
be qualified such that only reversals of a certain minimum magnitude
are counted. For this study we used 10% of each day’s mean flow
as the threshold. Computationally this is more challenging but it
provides a better metric.
RichBak Richards-Baker flashiness
index (Baker et al. 2004)
The path length of flow oscillations (sum of the absolute values of
hour-to-hour changes in hourly flows) calculated as the geometric
distance of the daily hydrograph of flow versus time (adapted from
Baker et al. 2004). Daily path length was divided by the daily mean
over each 24-hr period. Higher values indicate greater stream
flashiness or more rapid variation in flow.
RiseFall Difference in rise and fall
counts
Difference between the number of hours of rising and falling flow as
determined with each pair of consecutive flow values. Over a 24-hr
period, the difference between rise and fall counts can range from
+24 to -24. Continuous rising flows throughout a day would produce
a score of +24, while all falling flows would produce a score of -24;
an equal number of rising and falling counts would produce a score
of 0. Over a longer period, the difference between the rise and fall
counts reveals whether flows take longer to rise toward a maximum
or fall toward a minimum. For example, flood flows often take long er
to subside than to rise.
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Figure 8. Tri-point continuum of hydrologic and ecological condition for a given hydropower
project. A project may (A) have little modification to flow, (B) modify the timing and distribution of
flows without large losses to annual water budget, or (C) divert large quantities of water thereby
reducing the water budget thereby limiting the quantity and quality of habitat. Likewise a project
may (D) have little modification to natural biodiversity, (E) have highly modified river communities
that support ecosystem services, such as sportfisheries, or (F) have extensive losses to ecosystem
services. Associations among hydrologic and endpoints are likely to exist; however, ecological
endpoints should not be viewed as directly related to hydrologic endpoints (e.g. B does not
necessarily result in E).
Bevelhimer et al. (2013) assessed the hydrologic condition of examples of run-of-river projects
and peaking projects compared to natural streams across the U.S. (Figure 10, Appendix). A comparison of
sub-daily and daily flow yielded different patterns and suggested that sub-daily statistics may do a better
job than daily statistics in illuminating differences among streams regulated by various dam operations
(Figure 10, Appendix). Furthermore, the ability to explain generalized ecological responses to dam-
altered flow may depend upon the widespread use of sub-daily rather than daily statistics.
Assessing hydrologic condition also applies to specific hydropower operations. For example, the
hydrologic classification dataset, class predictive models, and daily-seasonal flow statistics were used in
concert to analyze the hydrologic condition of the Snake River below Hells Canyon Dam, Idaho (Figure
11). Hells Canyon Dam is owned and operated by Idaho Power Company as a peaking operation.
Because Jackson Lake Dam was built in 1911 in the upper mainstem, very little pre-regulation
information was available for the lower Snake River. Thus, landscape and climate variables were
assembled and used in the class predictive model to predict the appropriate hydrologic class. Based on
model predictions, the Snowmelt 2 hydrologic class (class 8) was determined as the appropriate
hydrologic type. Unregulated streams within the Snowmelt 2 class provide a typical range of variation.
A random subset of gages (n=6) from the Snowmelt 2 class was selected and ~20 years of hydrologic data
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were obtained for each gage (Figure 11). For each year, each average daily discharge was divided by the
maximum daily discharge for that year in order to standardize flow information from gages of varying
contributing basin sizes.
Figure 9. Dam-regulated stream gages (n=1,180) assigned to hydrologic classes using the class-
predictive model (top). Outliers (red dots) detected using Mahalanobis distances calculated
between each regulated gage and the centroid of the unregulated gages within each hydrologic class
(bottom). The size of the dots represent the total upstream dam storage above each gage corrected
for drainage area. From McManamay et al. (2013b).
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Figure 10. A comparison of hydrologic conditions for peaking and run-of-river hydropower
projects assessed using sub-daily or daily statistics. The comparison yielded opposing results for
subdaily and daily statistics. Sub-daily statistics yield more consistent results than daily statistics
when assessing hydrologic conditions among operations. From Bevelhimer et al. (2013).
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Figure 11. An assessment of the hydrologic condition of the Snake River below Hells Canyon Dam,
Idaho. (A) Using class prediction models, USGS gage 13290450 was classified as a Snowmelt 2 type
stream. (B) Comparisons of the current flow below Hells Canyon Dam to the Snowmelt 2
hydrologic profile (10th to 95th percentile of standardized flows) reveal departures from the relative
magnitude of annual maxima and seasonal baseflows. Standardized flow calculated by dividing
each year by maximum flow.
By combining over 100 gage-years of hydrologic information, a hydrological class profile could
be developed for the Snowmelt 2 class (Figure 11). The hydrologic class profile represents the 10th to 95th
percentile range of variation of standardized flow for each day of the year. Discharge data from the Hells
Canyon gage (USGS 13290450) was standardized in a similar fashion. A comparison on hydrologic
profiles very quickly reveals departures from the relative magnitude of annual maxima and seasonal
baseflows (Figure 11).
Hydrologic classes provide a template to assess departures from the normal tendency. Although
the normal tendency represents the natural flow regime, it is not meant to override or place an agenda on
specific flow objectives. However, understanding departures from normal tendencies provides a relative
comparison of where project operations fit into a larger picture. The inter-quartile-range (IQR) of a
particular hydrologic metric represents the central tendency within a hydrologic class and a quantitative
approach to measuring hydrologic alteration (Figure 12). Using hydrologic classes as a measure of
central tendency is advantageous for a number of reasons. First, as mentioned previously, pre-dam
regulation information may not be available. Second, hydrologic classes represent a range of values
rather than a single metric – thereby providing a measure of uncertainty. Lastly, magnitude-related
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metrics within hydrologic classes can be standardized by the median daily flow value as to remove the
effect of river size and diversions. Because of substantial irrigation withdrawals within the Snake River
Basin, standardized flow metrics provide a robust method to evaluate the relative magnitude of seasonal
variation (Figure 12). Comparing flows below Hells Canyon Dam to the full magnitude of expected
flows would result in less useful results since the full historic magnitude is not available for improving
ecological conditions. Again, this suggests that using baseline information to inform hydropower
operations may be misleading. In contrast, assessing hydrologic alterations based on the magnitude of
available flows produces more realistic options.
Figure 12. (A) Assessing departures in a given variable for the Snake River below Hells Canyon
Dam from the central tendency represented as the inter-quartile range for the Snowmelt 2 Class
(gray section of box and whisker plot represent interquartile range (IQR) whereas error bars
represent 95th percent confidence interval). (B) Percent changes of USGS 13290450 from the IQR of
the Snowmelt 2 class for 27 daily flow statistics.
* indicates that the statistic was standardized by mean daily flow.
As a similar example, the hydrologic classification dataset, class predictive models, and
hydrologic statistics were used to evaluate changes in hydrologic conditions for the Brule River following
a FERC relicensing agreement for Brule River Dam (Figure 13). Historically, Wisconsin Electric Power
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Co. operated Brule River Dam (built 1919) in a peaking mode until 1995 when the company switched to
operating the project as run-of-river. A series of sub-daily statistics were also calculated and compared
with daily statistics between the two periods (Haas et al. 2013, Appendex). Based on the predictive
model, there was a 50-50% chance that the Brule River could either be classified Snowmelt 2 stream
(class 8) or a Super-stable Groundwater stream (class 5). The hydrologic record for the Brule River gage
(USGS 04062011) was split into two periods: pre- and post-FERC relicensing (1989-1995 and 1996-
2013, respectively). Daily statistics were calculated for each period. For reference, Snowmelt 2 and
Super-stable Groundwater streams within a 100-km radius were selected and daily statistics were
calculated. Standardized daily flow statistics for each period were compared to the central tendency
found within the reference gage classes (Figure 13). Again, these results can rapidly assess the
hydrologic condition of a given hydropower project.
2.2.4 Ecological Geospatial Data (T)
Multiple geospatial datasets providing local and regional ecological datasets have become
increasingly available (Figure 14A). A very simple yet advantageous tool would provide quick access to
biological information from basins neighboring a hydropower project. Building ecological datasets at
finer resolutions will require statistical modeling to accurately represent the composition of river
communities (Figure 14B-C).
2.2.5 Ecological condition (D)
The ecological condition of a hydropower project provides an indication of hydrologic, as well as
general environmental conditions. For example, if a fish community below a hydropower facility in the
southeastern U.S. is dominated by warm-water-intolerant species, then this suggests a likely hypolimnetic
release from the dam regardless of the hydrologic condition. Hence, in some ways, the ecological
condition provides as much context as it does assessment. Similar to hydrologic conditions, the
ecological condition can also be conceptualized within a tri-point continuum (Figure 8). Eco-class
linkages can provide a range of variation representative of a given hydrologic class. As a coarse
approach, linkages between the uniqueness of hydrologic character and ecological distinction can be used
to predict associations between hydrologic change and ecological community response. For higher
resolution analyses, utilizing geospatial tools can provide more accurate distributions of species. A
comparison of fish assemblages below a hydropower project to that of local sites within the same
hydrologic class can provide a rapid assessment of shifts in community structure as well as missing
species.
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Figure 13. Comparison of the Brule River for peaking conditions (Pre-relicensing, 1989-1995) and run-of-river conditions (Post-
relicensing, 1996-2013) with respect to the interquartile ranges of 23 daily hydrologic statistics represented by the Super-stable
Groundwater Class (Class 5) and the Snowmelt 2 Class (Class 8). Although the predictive model suggested shared membership between
Class 5 and 8, results of the hydrologic condition assessment suggest that Class 5 is more appropriate. Large changes in most hydrologic
variables from pre- to post- relicensing were not observed. However, for evidence changes post-relicensing, hydrologic conditions were
more similar to the normal tendency represented by Class 5.
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Figure 14. Examples of ecological geospatial data sets. (A) Fish sampling point locations provided by two data sources for the U.S.. (B)
Multiple sources can be combined to create composite datasets for regions, such as fish sampling locations within the Appalachicola-Flint
and Alabama Coosa Tallapoosa River basins. (C) Combined datasets can be summarized to create localized ecological layers to support
assessing hydropower project ecological condition.
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2.3 Scoping
Context and assessment represent conventional datasets and tools that inform the remainder of the
environmental flow determination process, regardless of the hydropower situation. Scoping, prescription,
and feasibility elements, however, require stakeholder inputs as they are individual to each hydropower
project. For example, the scoping process requires stakeholders to identify ecological objectives specific
to each project. Based on the individualistic needs, the previous elements and tools can be used to isolate
and prioritize information gaps, a reduced set of key hydrologic metrics, and predictive relationships
between proposed flow changes and ecological response. A major part of the scoping process is
identifying studies that will inform the impact of current and future operations and aid in determining
environmental flows; thus, substantial time and cost savings can benefit from information amassed prior
to the scoping process.
2.3.1 Ecological Targets (D)
Successful environmental protection or restoration depends on developing appropriate goals and
considering the context of each management situation (Roni et al. 2002); however, appropriate goals must
be substantiated by establishing measurable objectives (Tear et al. 2005). Based on the current and
desired ecological conditions, stakeholders must identify measurable objectives or ecological targets, such
as increases in native fish richness, increases in sportfish biomass, or increases in the frequency and
duration of floodplain inundation. The basis for each ecological target can be substantiated by the
information provided in the context and assessment.
2.3.2 Eco-Evidence Tools (T)
Many novel approaches are now available to assist in environmental flow decision making, which require
developing predictable relationships between changes in flow and ecological responses. For example,
Norris et al. (2012) developed a form of causal criteria analysis, called Eco Evidence, which uses
published literature to support a priori developed cause-effect hypotheses. For example, if increasing
salmonid spawning success was selected as an ecological target, an example hypotheses might be as
follows: decreasing daily flow fluctuations (range in flows) will increase salmonid redd success.
Extensive reviews of the stream flow and ecology literature and associated database compilation can
provide support for hypotheses, but also quantitative predictions at the regional scale. For example,
McManamay et al. (2013d) developed a literature review, compiled a database, and conducted a
quantitative meta-analysis of ecological responses to changes in flow in the South Atlantic region of the
U.S. (Figure 15). The approach can yield quantitative and directional relationships between changes in
flow and ecological responses (Figure 15). These relationships can help guide the development of
recommended environmental flows.
Geospatial analyses can also provide regional quantitative/predictive flow-ecology relationships
to inform the scoping element. Mims et al. (2013) used paired fish sampling data from river locations
below dams and in nearby unregulated locations to form quantitative relationships between changes in
flow variation and fish assemblages. Geospatial analyses can harness the context and assessment data
layers to generate quantitative flow-ecology relationships.
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Figure 15. An example of an Eco-Evidence approach (modified from McManamay et al. 2013d).
Based on literature compilation, work groups can develop databases representing a regional
knowledge base. The database can be used to extract flow-ecology relationships, develop
predicted responses to flow restoration, or isolate key hydrologic/ecological indicators for a
specific context.
2.3.3 Key Hydrologic and Ecological Indicators (R)
Depending on current ecological conditions, identified ecological targets, and predictive
relationships (eco-evidence toolbox), the scoping process can yield a subset of hydrologic and ecological
indicators to inform the flow prescription and feasibility elements. Ideally, ecological indicators are
predictably linked to hydrologic indicators through quantitative relationships. The previous application
steps yield the data and framework required to create predictive flow-ecology relationships. For example,
McManamay et al. (2013c) compiled/collected hydrologic information, fish assemblage information, and
riparian information for regulated and unregulated streams falling within the Stable High Baseflow class
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within the Upper Tennessee Basin (Figure 16). After constructing multivariate models to control for
habitat fragmentation, gradient, and elevation, predictive relationships between flow and ecology (fish
richness/riparian cover) were developed (Figure 16). Relationships developed for flow classes and
regions could guide the scoping process by identifying key hydrologic and ecological indicators and their
relationships with each other. These relationships can guide the prescription of alternative flow scenarios.
2.3.4 Identify Information Gaps (R)
Identifying key indicators outlines the objectives for environmental flows. However, all the
information required to generate predictable linkages between each hydrologic indicator and ecological
responses for each individual hydropower context is unlikely. Thus, the scoping process can identify
gaps in knowledge and thereby, solidify relicensing studies to fill those gaps. Collaborative and
interdisciplinary workshops can be used to fill in the missing information gaps (Richter et al. 2006).
2.4 Prescription
Prescribing environmental flow alternatives should not be confused with the outcome of a FERC
order. In contrast, the prescription of potential alternative flow scenarios should come well within the
licensing process. These prescriptions should be evaluated and tested in feasibility analyses (section 2.5).
2.4.1 Alternative Flow Scenarios (D)
Alternative flow prescriptions (e.g. Table 3) should be developed using the available knowledge
base (Context, Assessment, Scoping processes) with the intent of filling information gaps. Thus,
prescription is also a stakeholder led process that utilizes best available science to identify a spectrum of
scenarios of varying risk for hydropower and environmental stakeholders. Multiple alternative flow
scenarios can be developed that address key hydrologic indicators identified in the scoping process.
2.5 Feasibility Analysis
Developing alternative flow scenarios and assessing those scenarios through monitoring has been
conducted in very few settings (e.g. Krause et al. 2005; Richter et al. 2006). For example, environmental
flow recommendations for the Savannah River below Thurmond Dam (USACE) were developed through
a collaborative process, implemented on a trial basis, and then monitored to make adaptive adjustments
(Richter et al. 2006). Modeling approaches, such as flow optimization and habitat simulation techniques,
provide an assessment of the feasibility of alternative flows prior to implementation. Models can aid in
determining whether ecological outcomes can be achieved in light of existing constraints. For example,
stakeholders may desire alternative flows that compromise power generation required to support local
economies. Other constraints may include factors outside the realm of control by project operations, such
as substantial losses in total flow due to irrigation requirements, structural limits to providing optimal
conditions, or limiting habitat factors (e.g. channel morphology, habitat connectively, water quality).
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Figure 16. (A) Compilation of regional hydrologic information (USGS gages and dam spillage) and fish sampling locations for the
Upper Tennessee River Basin. (B) Based on multivariate models, simulations can yield predictive flow-ecology relationships to predict
fish richness or riparian vegetation responses to changes in flow. Modified from McManamay et al. (2013c).
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Table 3. Examples of alternative flow scenario components to be tested during feasibility studies.
Alternative scenarios can represent one to many different flows within each component and/or one
to many different combinations of components.
Flow Scenario
Component
Description Potential Ecological/Societal Benefit
Baseflow
Minimum flow Constant baseflow supplied year-
round between generation.
Entire channel perimeter remains inundated and
reduces fish stranding following generation.
Creates more stable environment.
Seasonally variable
baseflow
Baseflow magnitude varies
according to season.
Seasonally fluctuating flow provides enhanced
flows during different spawning times for fish and
habitat refugia to support varying life stages of
macroinvertebrates and riparian vegetation.
Flood Pulses
Frequent small flood
(rafting release)
Scheduled releases of small flood
events periodically during year (5
to 10 times) during appropriate
seasons.
Provides channel maintenance such as scouring or
flushing sediment, inundating roots, removing
encroaching vegetation, and redistributing
spawning substrates. Also could provide
recreational boating opportunities.
Annual large flood
(riparian pulse)
Scheduled large flood event (per
1.5 years)
Creates new habitats by shifting large amounts of
substrates, provides organic matter inputs from
floodplain, inundates backwater habitats, and
provides nursery habitats for fish.
Special-events
Attractant flow Pulsed flows attract upstream
migrating fish to ladders
Enhances fish passage, reproduction, and
population viability.
Passage flow Pulsed flows to enhance/protect
outmigration
Enhances fish survival, recruitment, and population
viability.
Sub-daily
Ramping restriction Restrictions in the rate of change
of the rising limb of generation
pulse
Creates less disturbance by reducing square-
shaped hydrograph. Allows time for behavioral
responses to initiation of peak generation.
Down-ramping
restriction
Restrictions in the rate of change
of the falling limb of generation
pulse
Prevents fish stranding by providing time for
behavioral responses to flow recession.
Daily range restriction Restrictions in range of min/max
flows during day
Reduces disturbance and creates more stable
environment to enhance feeding and spawning
habitats.
Diurnal variation in
generation
Shifiting the timing of generation
within a day
Generating during different times of the day may
provide more temporal overlapp of hydrologic
stability and peak feeding times.
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2.4.2 Instream Flow Tools (T)
In section 1.2, multiple environmental flow frameworks were briefly summarized with the
majority falling into the category of instream flow tools. In contrast to holistic environmental flow
strategies (such as DRIFT, ELOHA, etc), instream flow tools are typically used to assess specific
ecological targets under varying flow conditions within a particular reach (e.g. segment of a tailwater).
Although they are only applicable to a reach of interest, individual attention is required to provide the
most accurate ecological predictions within alternative flow scenarios given that channel morphology,
temperature, and habitat connectivity can be dramatically different depending on the hydropower project.
The required time and resource commitment vary substantially depending on the instream flow approach
taken. These tools can provide variable outputs such as estimated biological habitat, bioenergetic,
population, or geomorphic responses.
2.4.3 Habitat Connectivity (D)
Obviously, dams lacking passage facilities block the migration and dispersal of organisms among
populations and habitats required for various life stages (Vaughn and Taylor 1999; Han et al. 2008;
Hoagstrom et al. 2008; Reid et al. 2008). Depending on the context, feasibility analyses should take local
colonization, extinction, and meta-population dynamics into account. Environmental flows may be
implemented for organisms whose distribution is poorly known. Thus, understanding the distribution of
various species may help determine whether migratory potential is inhibited by physical barriers or poor
habitat quality inducing barrier-type effects. Implementing environmental flows to support species
recolonization without assessing the ability of species to colonize is a poor management practice.
2.4.4 Reach Geomorphic Classes (D)
Local channel depth, channel complexity, and substrate conditions interact with flow to determine
the habitat template for a particular organism. Reach or habitat classifications become advantageous in
stratifying locations for instream flow studies, assessing the presence/absence of species, and prioritizing
areas for morphological restoration. For example, Vernon et al. (2013) developed a geomorphic
classification framework in conjunction with a two-dimensional hydraulic model to assess optimal
juvenile salmonid rearing habitat under varying flow regimes in the lower Snake River Basin. Reach-
scale geomorphic classifications can increase the predictive accuracy of flow-habitat models and provide
a template to assess alternative mitigation strategies. For example, the output from the geomorphic
classification and two-dimensional hydraulic model (Vernon et al. 2013) can be used to determine the
feasibility of flow restoration compared to morphological restoration. Specifically, gravel addition may
provide more ecological benefits than flow restoration. Restoring bedload transport and spawning
habitats below impoundments through gravel and sediment augmentation has been documented largely in
salmonid rivers in the western U.S. (Kondolf et al. 1996; Merz and Setka 2004; Merz and Chan 2005;
Sarriquet et al. 2007) and to a lesser extent in the eastern U.S. (McManamay et al. 2010). Bunte (2004)
provides a detailed guide to gravel mitigation and augmentation below dams and provides a conceptual
framework in determining the amount, location, and timing of gravel additions.
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2.4.5 Water Quality (D)
The standard protocol for IFIM includes modeling temperature with variable discharge releases
(Bovee et al. 1998; Krause et al. 2005). However, according to Olden and Naiman (2010) incorporating
thermal regimes into environmental flow assessments is rarely done. They indicate that some of the
challenges of incorporating assessments of thermal regimes into flow assessments are due to a lack of
understanding of the impact of dams on temperature, the ecological consequences of an
altered temperature, and limited knowledge of the availability and success of temperature management
strategies. Releases from stratified layers of an impoundment can result in water quality regimes
drastically different from natural conditions and may constrain or complicate the ecological benefits of
environmental flows (Pozo et al. 1997; Hamblin and McAdam 2003; Lessard and Hayes 2003; Krause et
al. 2005; Olden and Naiman 2010). Price and Meyer (1992) provide a guide to operational and structural
water quality management techniques for reservoirs and tailwaters.
Water quality conditions may pose such stringent constraints on tailwater conditions, that
alternative flow scenarios provide little ecological benefit. Thus, water quality modeling should be
included in feasibility analyses. For example, Krause et al. (2005) simulated temperature scenarios using
a hydrodynamic model coupled with temperature modeling to assess the influence of various flow
releases on the thermal regime below a hypolimnetic-release hydropower dam in Virginia. Despite 15
different alternative flow-scenarios, stream temperatures remained well-below suitable growth thresholds
for brown trout. Likewise, Bevelhimer et al. (1997) modeled temperature responses to proposed
management alternatives for the Madison River below Madison Dam. Interestingly, model results
suggested that none of the proposed alternatives was likely to produce a significant decrease in water
temperature. Conversely, restoration of the river to natural flow conditions would have actually caused
downstream temperatures to be higher thereby negatively impacting a blue-ribbon trout fishery. Sherman
(2000) provides a review of various temperature mitigation strategies considering dam structure and the
use of stratification layers to mimic natural thermal conditions.
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3. APPLYING THE HEFLOW FRAMEWORK TO HYDROPOWER CONTEXTS
The utility of frameworks used in conservation management is largely based on their broad
applicability. Frameworks that provide a template for creating measurable objectives and operate across
larger scales are needed. In addition, frameworks should provide the context for restoring aspects of river
systems while considering current regulatory procedures. Stakeholders are applying increasing pressure
on many hydropower dam owners to change plant operations to affect downstream river flows with the
intention of providing better conditions for aquatic biological communities. Given that many tailwaters
affected by hydropower operations provide acceptable (and in some cases exceptional) ecological
services, there is a need for better understanding of the characteristics of flow variati on that are amenable
to healthy aquatic communities and those that may not be as conducive. These proposed changes should
be assessed within a framework that provides balanced decision making for both hydropower and
environmental stakeholders. Ensuring a truly sustainable outcome and the most optimal conditions for
multiple stakeholders requires the establishment of a common framework that is utilized by all
participants in the energy-water nexus.
Figure 17. Implementation of flows following application of HEFLOW framework. Adaptive
management should be used to monitor flows after implementation. However, for all parties to
agree to adaptive management, hydropower and environmental stakeholders should both have
some level of mutual perceived risk. Based on results of monitoring, collaborative decision making
can be used to determine final flow regime for the length of the new license.
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The HEFLOW framework provides a template to organize tools, applications, and datasets useful
in the process of identifying key hydrologic and ecologic indicators and identifying environmental needs
based on ecological targets. The advantage of HEFLOW is that it expedites much of the information
needs for the scoping element of the FERC process while efficiently organizing tools for the prescription
and feasibility portions of the process. The actual implementation of new flow regimes follows the
HEFLOW process (Figure 17). Although feasibility analyses provide some assessment of potential
ecological benefit provided by alternative flow regimes, predictive analyses cannot and should not replace
monitoring. Many times, population and ecosystem responses to flow regimes may take many years
(sometimes decades) to come to full fruition; thus, monitoring becomes extremely important. Given the
uncertainty of the ecological effectiveness of new flow regimes, we suggest an adaptive management
strategy as follows: a 5-10 year ‘grace period’ of monitoring is conducted to evaluate ecological
responses to the order-approve flow regime and that information be used within a collaborative process to
adjust the final flow implementation (Figure 17). However, for the adaptive management design to be
successful, both hydropower and environmental stakeholders must embrace some level of mutual
perceived risk. For example, if monitoring suggests that the ecological benefits of the implemented flow
regime are limited by water quality issues or by sustained peaking activity, then proposed modifications
to the flow regime may include changes to the facility (e.g. O2 diffusers, intake structure) or reductions to
peaking activities. Conversely, if monitoring suggests that the implemented flow regime results in no
environmental improvement, then modifications may include reinstating aspects of the pre-licensing flow
regime. For true adaptive management to work, participants must build a partnership based on trust,
which requires both sides to accept risk.
4. THE HEFLOW FRAMEWORK, MARKET ACCELERATION, AND ACCOMPLISHMENTS
The HEFLOW framework addresses some of the needs of identifying environmental flow
requirements in light of regulatory constraints, which other frameworks fail to address (see Section 2,
page 8-9). However, as discussed in Section 2, the HEFLOW framework is not a complete paradigm
shift. For example, HEFLOW uses a similar conceptual approach as ELOHA in that hydrologic
classifications, hydrologic alterations, and flow-ecology relationships are developed. However,
HEFLOW is different in the intent behind creating the structure, the elements that make up the
framework, and how they are applied in implementing environmental flows. Specifically, HEFLOW
differs from ELOHA in the following ways:
HEFLOW addresses the importance of geomorphology and dam operation in providing context.
Assessing the hydrologic and ecological condition within HEFLOW is meant to provide a relative
comparison among projects and not suggest that return to pre-disturbance conditions is needed.
Flow-ecology relationships are not univariate relationships. Rather, flow-ecology relationships
within HEFLOW are developed within specific contexts using model building that addresses
multiple factors (geomorphology, dam operations, habitat connectivity, etc).
Flow-ecology relationships within HEFLOW include predicted ecological responses to flow
improvements not just departures from pre-disturbance conditions.
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Environmental flow recommendations are not solely dependent upon flow-ecology relationships.
In contrast to ELOHA, environmental flow recommendations within HEFLOW are the result of
prescription and feasibility analyses based on site-specific needs.
The importance of water quality and reach geomorphology is explicitly addressed in feasibility
assessments within HEFLOW. Streamflow should not be separated from river channel dynamics.
HEFLOW is not meant to replace effective instream flow tools (e.g. IFIM). Rather, HEFLOW
organizes how and when they should be applied.
A less obvious feature of HEFLOW is that it has the potential to expedite aspects of the FERC process,
thereby creating efficiency and reducing costs for hydropower stakeholders. A large amount of time of
the relicensing process is developing studies and analyses (i.e. scoping) that address information gaps. If
poorly informed, expensive studies may be misguided and in turn, may result in environmental flows that
are inadequate to improve ecological targets. HEFLOW expedites this process by providing a
standardized approach to identifying the key hydrologic and ecological indicators of importance and
isolating flow-ecology relationships that inform prescriptions and feasibility. It forces scoping and
studies to be relevant and answer the most pressing questions. If the information and tool packages to
implement HEFLOW are provided in central repositories and online interfaces, then these applications
become the standardized means to mainstream relicensing and environmental impact associated with
existing and new hydropower development. Another important facet of HEFLOW is that is establishes a
comprehensive and coordinated research agenda to further the science of environmental flows.
Associated with HEFLOW are the production of large tabular and geospatial datasets that can inform and
aid in coordinating existing and proposed research at DOE Laboratories.
Under the current scoped work by DOE, many accomplishments in research have been made that
fill in various gaps associated with application steps of the HEFLOW framework (Figure 18). Most
scoped elements have been researched (Figure 18). Some elements have been applied in case study
settings (partial blue shading, Figure 18); however, wide-spread applicability requires building datasets
for the entire U.S. to mainstream environmental flow determination. In other situations, research has
been conducted at DOE-supported labs unrelated to IFP project, such as flow optimization, and can
support the environmental flow determination process (full blue shading, Figure 18). In other situations,
no research has been conducted for elements unrelated to the IFP project, such as building watershed
geomorphic classifications, developing alternative flow scenarios for US hydropower facilities, and
developing habitat fragmentation datasets, but technology exists to construct these resources.
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Figure 18. Accomplishments made by ORNL, PNNL, and ANL during the course of the Instream Flow Project (IFP) supported by DOE.
Each cell represents a data set or tool that falls within a particular element/or application within the HEFLOW framework (see Figure 2
for reference). Accomplishments made related to- and unrelated to current DOE support are also provided.
Resource Context Assessment Scoping Prescription Feasibility
Data
Hydrologic Classification ORNL DOE LAB Researched during IFP
Watershed Geomorphic Classification DOE LAB Partially researched during IFP
Dam Operation Classification ORNL DOE LAB Partially researched during IFP; Full development unscoped
Hydrologic Condition ORNL; ANL DOE LAB Researched by DOE LAB; unrelated to IFP
Ecological Condition Researched by outside unrelated source
Ecological Targets PNNL Not researched (Unscoped under current IFP)
Key Hydrologic/Ecological Indicators ORNL Not Applicable to Element
Alternative Flow Scenarios ** IFP = Instream Flow Project (DOE-funded)
Habitat Connectivity
Reach Geomorphic Classes PNNL
Water Quality ORNL
Tools
Class Predictive Models ORNL
Eco-Class Linkage ORNL
Daily Statistics
Subdaily Statistics ORNL; ANL
Ecological Geospatial Datasets ORNL
Eco-Evidence Approaches ORNL
Flow-Ecology Relationships ORNL
Instream Flow Tools
Flow Optimization Toolbox ORNL
Legend
Applications in HEFLOW framework
Resources partially researched but full development unscoped
(partial blue shading) -refers to resources that have been built for
specific case studies to provide examples but not fully functional
for the entire US scale .
Resources researched by DOE LAB; unrelated to IFP (blue
shading) -refers to resources built under other DOE supported
work besides IFP.
Resources with very little research (partial red shading) -
Unscoped work that benefits from existing technology developed
through other DOE-support.
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6. APPENDIX- ABSTRACTS OF DOE SUPPORTED INSTREAM FLOW PROJECT WORK
Bevelhimer, M., R. A. McManamay, B. O’Connor (2013), Characterizing sub-daily flow regimes:
implications of hydrologic resolution on ecohydrology studies, (in review – River Research and
Applications).
Haas, N.A., B.L. O’Connor, J.W. Hayse, M.S. Bevelhimer, and T.A. Endreny (2013), Analysis of daily-
peaking and run-of-river dam operations on flow variability metrics considering subdaily to seasonal time
scales, (in review – Journal of the American Water Resources Association)
Environmental flows are an important consideration in licensing hydropower projects because operational
flow releases can result in adverse conditions to downstream ecological communities. Flow variability
assessments have typically focused on pre- and post-dam conditions using metrics based on daily-
averaged flow values. This study focused on examining flow variability metrics using subdaily flow data
to assess environmental flow conditions resulting from changes in hydropower operations from daily-
peaking to run-of-river. An analysis tool was developed to quantify subdaily to seasonal flow variability
metrics and was applied to four hydropower projects that underwent operational changes based on
regulatory requirements. Results indicate that the distribution of flows is significantly different between
daily-peaking and run-of-river operations, that daily-peaking operations are flashier than run-of-river
operations, and that these differences are seen using hourly-averaged flow datasets and are less
pronounced or not noticeable using daily-averaged flow datasets. Of the flow variability components
examined, the use of daily versus subdaily flow data impacted the analysis of rise and fall rates the most.
This outcome has implications for the development of flow-ecology relationships quantify effects of rate
of change on processes such as fish stranding and displacement, along with habitat stability. The
quantification of flow variability statistics should be done using subdaily datasets and metrics as this
accurately represents the nature of hydropower operations.
McManamay, R.A., M.S. Bevelhimer, E.A. Frimpong (2013), US hydrologic classification applied to fish
traits: a framework for developing flow-ecology hypotheses (in review – Ecohydrology).
Classification systems are valuable to ecological management in that they organize information into
consolidated units thereby providing efficient means to achieve conservation objectives. Of the many
ways classifications benefit management, hypothesis generation has been discussed as the most important.
However, in order to provide templates for developing and testing ecologically relevant hypotheses,
classification systems created using environmental variables must be linked to ecological patterns.
Herein, we develop associations between two recent US hydrologic classifications and fish traits in order
to form a template for generating flow-ecology hypotheses and supporting environmental flow standard
development. We observed tradeoffs in adaptive strategies for fish (reproductive and life history traits)
across a spectrum of stable, perennial flow to unstable intermittent flow. In accordance with theory,
periodic strategists were associated with stable, predictable flow whereas opportunistic strategists were
more affiliated with intermittent, variable flows. Comparisons of the predictive capacity of hydrologic
classifications with other frameworks suggested that spatially contiguous classifications with higher
numbers of classes and in turn, higher regionality will tend to explain more information in ecological
patterns. We developed linkages between the uniqueness of hydrologic character and ecological
distinction among classes, which may translate into predictions between losses in hydrologic uniqueness
and ecological community response. Ultimately, our results provide a template to develop and test flow-
Oak Ridge National Laboratory ORNL/TM-2013/159
52
ecology hypotheses and support the presumption that environmental flow standards should be catered
towards stream classes and ecological communities, therein.
McManamay, R.A., M.S. Bevelhimer, S-C. Kao (2013), A new US hydrologic classification: a tool to
stratify analyses in Ecohydrology (in review – Ecohydrology).
Hydrologic classifications unveil the structure of relationships among groups of streams with differing
stream flow and provide a foundation for drawing inferences about the principles that govern those
relationships. Hydrologic classes provide a template to describe ecological patterns, generalize hydrologic
responses to disturbance, and stratify research and management needs applicable to ecohydrology. We
developed two updated hydrologic classifications for the continental US using two streamflow datasets of
varying reference standards. Using only reference-quality gages, we classified 1715 stream gages into 12
classes across the US. By including more streamflow gages (n=2618) in a separate classification, we
increased the dimensionality (i.e. classes) and hydrologic distinctiveness within regions at the expense of
decreasing the natural flow standards (i.e. reference quality). Greater numbers of classes and higher
regional affiliation within our hydrologic classifications compared to that of the previous US hydrologic
classification (Poff, 1996) suggested that the level of hydrologic variation and resolution was not
completely represented in smaller sample sizes. Part of the utility of classification systems rests in their
ability classify new objects and stratify analyses. We constructed separate random forests to predict
hydrologic class membership based on hydrologic indices or landscape variables. In addition, we provide
an approach to assessing potential outliers due to hydrologic alteration based on class assignment.
Departures from class membership due to disturbance take into account multiple hydrologic indices
simultaneously; thus, classes can be used to determine if disturbed streams are functioning within the
realm of natural hydrology.
Vernon, C.R., E.V. Arntzen, M.C. Richmond, R.A. McManamay, T.P. Hanrahan, C.L. Rakowski (2013),
GIS framework for large river classification to aid in the evaluation of flow-ecology relationships, PNNL-
xxxx Report. Prepared for the U.S. Department of Energy under Contract DE-AC05-76RL01830,
February 2013.
Providing a means to quantitatively define flow-ecology relationships is integral in establishing flow
regimes that are mutually beneficial to power production and ecological needs. This paper presents a
geographic information system (GIS) framework for large river classification that is fl exible, accurate,
and easily integrated with Ecological Limits of Hydrologic Alteration (ELOHA) initiatives. A case study
was conducted integrating the base geomorphic aspect of this framework with the Modular Aquatic
Simulation System two-dimensional (MASS2) hydraulic model and field collected data to establish
optimal juvenile salmonid rearing habitat under varying flow regimes throughout an impounded portion
of the lower Snake River, USA. Defining regions of optimal juvenile salmonid habitat at varying flows
was used to distinguish areas that have a high potential for the creation of additional shallow water
habitat. Findings indicated that the potential to create additional shallow water habitat does exist for
juvenile salmonid rearing regardless of the flow scenario (exceedence levels of 1, 25, 50, 75, and 99
percent) for the sample time frame (May – June 2011). The left-bank habitat of the lower Snake River
was also found to be preferable for juvenile salmon rearing compared to right-bank habitat. The results
from the case study suggest that the GIS framework is a capable tool when used to diagnose flow-ecology
relationships. Additionally, an alternative hydrologic classification system is explored that couples well
with the geographically independent nature of this GIS framework. Future applications of this framework
are to utilize it in other large river systems throughout the contiguous United States. The framework also
allows for the organization of large river data to be quickly accessed and used for multi-river comparison
and analysis. The development of a backend database accompaniment within an interactive web platform
would be highly beneficial to create a readily available and standardized mechanism to facilitate
nationally spread classification efforts.