River fragmentation and flow alteration metrics: a review of methods and directions for future research

Abstract

Background. Rivers continue to be harnessed to meet humanity's growing demands for electricity, water, and flood control. While the socioecological impacts of river infrastructure projects (RIPs) have been well-documented, methodological approaches to quantify river fragmentation and flow alteration vary widely in spatiotemporal scope, required data, and interpretation. Aim. In this review, we first present a framework to visualise the effects of different kinds of RIPs on river fragmentation and flow alteration. We then review available methods to quantify connectivity and flow alteration, along with their data requirements, scale of application, advantages, and disadvantages. Finally, we present decision-making trees to help stakeholders select among these methods based on their objectives, resource availability, and the characteristics of the project(s) being evaluated. Methods. Thematic searches of peer-reviewed literature using topic-relevant keywords were conducted on Google Scholar. The bibliography of selected papers was also reviewed, resulting in the selection of 79 publications. Papers that did not define or apply a specific metric were excluded. With respect to fragmentation, we selected papers focused on instream connectivity and excluded those dealing with overland hydrologic connections. For flow alteration, we selected papers that quantified the extent of alteration and excluded those aimed at prescribing environmental flows. Results. The expected hydrological consequences of various RIP types were 'mapped' on a conceptual fragmentation-flow alteration plot. We compiled 29 metrics of river fragmentation and 13 metrics to flow alteration, and used these to develop decision-making trees to facilitate method selection. Discussion. Despite recent advances in metric development, further work is needed to better understand the relationships between and among metrics, assess their ecological significance and spatiotemporal scale of application, and develop more informative methods that can be effectively applied in data-scarce regions. These objectives are especially critical given the growing use of such metrics in basin-wide conservation and development planning.

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Environ. Res. Lett. 15 (2020) 123009 https://doi.org/10.1088/1748-9326/abcb37
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TOPICAL REVIEW
River fragmentation and ow alteration metrics: a review of
methods and directions for future research
Suman Jumani1, Matthew J Deitch2, David Kaplan3, Elizabeth P Anderson4, Jagdish Krishnaswamy5,
Vincent Lecours6and Matt R Whiles1
1Soil and Water Sciences Department, University of Florida, Gainesville, FL 32611, United States of America
2Soil and Water Sciences Department, University of Florida, IFAS West Florida Research and Education Center, Milton,
United States of America
3Engineering School of Sustainable Infrastructure & Environment, University of Florida, Gainesville, FL, United States of America
4Department of Earth and Environment and Institute of Environment, Florida International University, Miami, FL,
United States of America
5Suri Sehgal Centre for Biodiversity and Conservation, Ashoka Trust for Research in Ecology and the Environment (ATREE),
Bengaluru, India
6School of Forest Resources & Conservation, University of Florida, Gainesville, FL, United States of America
E-mail: sumanjumani@ufl.edu and sumanjumani@gmail.com
Keywords: river connectivity, hydrologic connectivity, river fragmentation, flow regulation, dams, watersheds,
conservation and management
Abstract
Rivers continue to be harnessed to meet humanity’s growing demands for electricity, water, and
flood control. While the socioecological impacts of river infrastructure projects (RIPs) have been
well-documented, methodological approaches to quantify river fragmentation and flow alteration
vary widely in spatiotemporal scope, required data, and interpretation. In this review, we first
present a framework to visualise the effects of different kinds of RIPs on river fragmentation and
flow alteration. We then review available methods to quantify connectivity and flow alteration,
along with their data requirements, scale of application, advantages, and disadvantages. Finally, we
present decision-making trees to help stakeholders select among these methods based on their
objectives, resource availability, and the characteristics of the project(s) being evaluated. Thematic
searches of peer-reviewed literature using topic-relevant keywords were conducted on Google
Scholar. The bibliography of selected papers was also reviewed, resulting in the selection of 79
publications. Papers that did not define or apply a specific metric were excluded. With respect to
fragmentation, we selected papers focused on instream connectivity and excluded those dealing
with overland hydrologic connections. For flow alteration, we selected papers that quantified the
extent of alteration and excluded those aimed at prescribing environmental flows. The expected
hydrological consequences of various RIP types were ‘mapped’ on a conceptual fragmentation-
flow alteration plot. We compiled 29 metrics of river fragmentation and 13 metrics to flow
alteration, and used these to develop decision-making trees to facilitate method selection. Despite
recent advances in metric development, further work is needed to better understand the
relationships between and among metrics, assess their ecological significance and spatiotemporal
scale of application, and develop more informative methods that can be effectively applied in
data-scarce regions. These objectives are especially critical given the growing use of such metrics in
basin-wide conservation and development planning.
1. Introduction
Spurred by growing human populations, rapid
urbanisation, and expanding industrial and com-
mercial activities, rivers continue to be harnessed and
regulated to meet humanity’s growing demands for
electricity, irrigation, water supply, and flood con-
trol (Nilsson 2005, Lehner et al 2011). With more
than 58 400 large dams (ICOLD 2019) and 82 891
small hydropower dams (Couto and Olden 2018)
worldwide, it is estimated that humans have appro-
priated more than half the global accessible freshwater
© 2020 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
runoff, creating a cumulative reservoir storage capa-
city of about 6197 km3(Lehner et al 2011). These
dams have fragmented and affected most rivers glob-
ally, leaving only an estimated 23% of the world’s
large rivers (>1000 km in length) flowing uninter-
rupted into the ocean (Grill et al 2019). While these
dams and reservoirs have significantly contributed
to human development (WCD 2000), they have fun-
damentally altered riparian ecosystems that depend
on the dynamics of streamflow and the movement of
water and the materials longitudinally and laterally
through the drainage network from head-waters to
estuaries and deltas (Poff et al 1997).
Despite these adverse impacts, hydropower con-
tinues to be the world’s largest source of renewable
electricity, with a 50% expected increase in produc-
tion by 2030 (IRENA 2016). Ongoing and future
hydropower developments are largely concentrated
in developing countries and emerging economies of
Asia, South America, Africa and the Balkan region
of Europe (Zarfl et al 2015, Tockner et al 2016,
Winemiller et al 2016). Within these regions, sub-
sistence communities may be especially dependent
on the provisional services that aquatic ecosystems
provide (Beck et al 2012). Moreover, hotspots of
existing and proposed dam development often over-
lap with areas of high freshwater biodiversity and
endemism. Examples include the Amazon, Mekong,
Congo, Zambezi, Yangtze, Himalayan, and Western
Ghats river basins (Tockner et al 2016, Winemiller
et al 2016, Jumani et al 2018). In 2018 alone, an addi-
tional 21.8 GW of hydropower capacity was installed
worldwide (Hydropower Status Report 2019). Con-
servative estimates suggest over 3700 hydropower
dams (>1 MW) are under construction or proposed
for further development across the globe (Zarfl et al
2015). This is in addition to the proliferation of other
river infrastructure projects (RIPs) such as small
dams, water abstraction schemes, inter-basin trans-
fers or river interlinking projects, flood control struc-
tures, and navigation schemes that could cause major
alterations in flow and sediment regimes (Grant et al
2012, Bagla 2014, Dey et al 2019). Furthermore, even
within affected basins, previously untapped head-
water streams, characterised by low discharge and
high gradient, are increasingly being dammed by the
proliferation of small dams and diversion schemes
(Couto and Olden 2018).
The hydrological consequences of RIPs on riv-
erine ecosystems are frequently framed in terms of
primary effects: reduced river network connectivity
(or increased river fragmentation) and flow alter-
ation (Nilsson 2005). Physical structures such as
dams, weirs, barrages, and levees fragment the river
network, impeding the free movement of water, sed-
iment, organic matter, nutrients, energy, and organ-
isms across space and time (Pringle 2003). The dis-
ruption of these water-mediated connections further
influences crucial ecosystem processes and functions
within river networks (Vannote et al 1980, Wiens
2002, Hermoso et al 2011). The loss of this con-
nectivity can be considered along a temporal dimen-
sion (seasonality of flows over time) and three spa-
tial dimensions—longitudinal (connectivity along
the length of a river channel from the source to
the mouth), lateral (connectivity between the flood-
plain, riparian areas, and the river channel), and
vertical (connectivity of stream water column with
groundwater) (Ward 1989). Physical structures may
also store, divert, and abstract water from the river
channel, and hence alter one or more characterist-
ics of the natural flow regime (Richter et al 2003).
Flow regulation describes alteration of the natural
flow regime, characterised by variability of flow mag-
nitudes, frequencies, durations, timing, and rates
of change within the year and over multi-annual
periods. Streamflow directly influences stream water
quality and physical habitat characteristics of the river
channel and floodplain, thereby maintaining the hab-
itat diversity required to support native biotic com-
munities and ecosystem functions (Richter et al 1996,
Poff et al 1997). Flow regulation may be caused by
the active or passive management of water in rivers;
some infrastructure can reduce or augment down-
stream discharge through specific dam operations or
abstraction points, while other forms passively hold
water or reduce flows based on the size of the infra-
structure and the dynamics of discharge.
Whereas methods to assess connectivity in ter-
restrial landscapes have long been developed and
applied (Tischendorf and Fahrig 2000, Calabrese and
Fagan 2004, Kindlmann and Burel 2008), assess-
ments of connectivity in riverine systems is a relat-
ively recent topic of study (Fagan et al 2002, Wiens
2002, Cote et al 2009, Wohl 2017). Unlike ter-
restrial systems, where landscape connectivity is two-
dimensional with numerous connectivity pathways,
connectivity in river networks is water-mediated and
largely driven by river flows (Pringle 2001). On the
basis of their hierarchical branched structure, frag-
mentation in river networks can yield more vari-
able fragment sizes compared to two-dimensional
systems (Fagan 2002). Consequently, river fragment-
ation more severely impacts connectivity due to
the existence of fewer possible pathways for water-
mediated dispersal and recolonization (Fagan 2002).
Furthermore, similar habitat patches that may be geo-
graphically proximate to each other in a river net-
work, may be separated by longer stream lengths.
This can significantly reduce the potential for recol-
onization and decrease metapopulation persistence
(Fagan 2002, Fullerton et al 2010). These unique
characteristics of aquatic dendritic networks and their
inherent spatiotemporal complexities pose a chal-
lenge to applying measures of landscape connectivity
to river networks (Fagan et al 2002, Wiens 2002, Cote
et al 2009). However, being able to effectively assess
and predict the impacts of RIPs is crucial to inform
2

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
project-specific and basin-wide conservation, restor-
ation, and development plans. Recognising this gap,
numerous methodological advancements have been
made to better assess metrics of river fragmentation
and flow alteration based on several types of remotely
sensed and field-based data (Nilsson 2005, Cote et al
2009, Grill et al 2014).
Understanding the suite of tools available to char-
acterize river connectivity and flow regulation is
important because these metrics can be used in a
descriptive manner to quantify impacts of RIPs on
both connectivity and streamflow dynamics. These
tools can also be used in a prescriptive manner to
develop and assess scenarios and environmental flow
methodologies to aid in basin-wide conservation and
development planning. In places where RIP devel-
opment trajectories are tending towards prolifera-
tion of smaller projects along upstream drainage net-
works (Zarfl et al 2015, Couto and Olden 2018),
there is a growing need to adequately assess reach-
and catchment-scale fragmentation and flow regu-
lation to account for these impacts (Athayde et al
2019). Further, recognising that countries with the
most aggressive RIP development plans are often
data-limited (Auerbach et al 2016), there is a need
to compile relevant methods that can be applied in
such data-limited environments so that stakehold-
ers in these regions can assess the effects that RIPs
might have on aquatic ecosystems and the services
they provide.
Within this context, the goals of this paper are to
(1) present a conceptual framework for characteriz-
ing the effects of RIPs on river fragmentation and flow
alteration; (2) review published methods to assess
river fragmentation and flow regulation, including
metric descriptions, data requirements, output, scale
of application, advantages and disadvantages; and
(3) present a decision-making tree to help managers
and stakeholders select the most appropriate methods
based on resource availability and objectives. We con-
clude by identifying existing data and methodological
gaps and discussing important directions for future
research, in the context of current global trends of RIP
development.
2. Understanding river fragmentation and
flow alteration
On the basis of their branching structure,
stream networks comprise functional habitats
that are hierarchically nested across spatial scales
(Rodríguez-Iturbe and Rinaldo 1997, Fullerton et al
2010). Consequently, the relative importance of vari-
ous connectivity dimensions and drivers of ecolo-
gical processes varies across spatiotemporal scales
(Vannote et al 1980, Ward 1989). The effects of RIPs
on connectivity and flow alteration are thus not
only influenced by the extent of impact, but also
on its location (i.e. headwaters versus tributaries
versus the mainstem) and timing (i.e. coincident
with high versus low flows) (Fagan 2002, Diebel
et al 2015). RIPs can influence stream hydrology,
biophysical characteristics, and ecological and func-
tional integrity at many scales (figure 1). Together,
these changes impact stream biophysical and chem-
ical characteristics, which further influence aquatic
and riparian habitat availability and quality, fresh-
water biodiversity, and associated ecosystem pro-
cesses and functions such as nutrient cycling regimes,
sediment redistribution, and ecosystem productivity
(Dudgeon 2000, Rosenberg et al 2000, Vorosmarty
et al 2000, Poff and Hart 2002, Pringle 2003, Nel
et al 2009, Anderson et al 2015). These changes
can have serious consequences on the livelihoods,
food security, and the physical, cultural, and spir-
itual well-being of river-dependent communities
(Richter et al 2010).
While most RIPs influence both connectivity and
flow regimes, they may disproportionately affect one
or the other depending on the project type and/or loc-
ation (Farah-Perez et al 2020). Projects can be classi-
fied based on size (large, medium, or small based on
installed capacity or dam height, though these clas-
sifications vary widely by region; Couto and Olden
2018), purpose (hydropower generation, irrigation,
water supply, flood control, navigation), and design
(with or without diversion/abstraction, storage capa-
city, and operating regimes). Nevertheless, each pro-
ject can be expected to influence connectivity and the
natural flow regime differently, and their impact can
be visualised on a fragmentation-flow alteration plot
(figure 2). Since the basin-level impact of these dis-
turbances can be expected to vary from headwaters to
the mainstem, the location of these projects will also
influence their relative impact. While the specifics of
each RIP dictate its actual position on this concep-
tual plot, it is instructive to ‘map’ different RIP types
according to their likely impacts on these two axes
(figure 2).
Medium and large dams that aim to impound
water, stabilize low flows and eliminate peak flows,
such as those built for flood control, water stor-
age, and hydropower generation, are often character-
ised by high barriers and substantial reservoir storage
capacities. These projects are expected to significantly
impact both flow regulation and network fragment-
ation (Grill et al 2014). When such large RIPs are
coupled with water abstraction (e.g. for irrigation and
water supply projects), their impact on flow altera-
tion can be expected to increase further (figure 2).
Since these projects are larger, in terms of capa-
city and/or size, they tend to occur on higher-order
streams. Barriers located further downstream can
isolate greater proportions of available upstream hab-
itat and significantly impact metapopulation dynam-
ics such as dispersal and recolonization abilities
(Fagan et al 2002, Nilsson 2005, Fullerton et al 2010).
Hence, dams farther downstream in the river network
3

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Figure 1. Schematic model illustrating morphological and water quality responses (orange boxes) to river infrastructure project
(RIP) induced altered stream hydrology (blue boxes), and their influence on biotic integrity (green boxes) and ecosystem function
(purple boxes). Arrows indicate pathways or directionality of influence. Dashed boxes represent distinct levels of impact, and solid
coloured boxes within them represent the main components pertaining to that theme (adapted from Poff et al 1997).
create larger fragment sizes and greater basin-wide
fragmentation.
Small hydropower projects (SHPs), frequently
touted as green alternatives to larger projects (Couto
and Olden 2018), tend to be built across small and
medium sized streams (Kibler and Tullos 2013).
Usually defined by their power generation capacity,
SHPs vary tremendously in definition across coun-
tries (from up to 1 MW to up to 50 MW), in size
(i.e. variable dam heights, reservoir areas and stor-
age capabilities), and in mode of operation (with or
without storage and diversion) (Couto and Olden
2018). Hence, the impact of a single SHP on frag-
mentation and flow alteration can vary considerably
based on the attributes of individual projects and
their location in the river network (figure 2). Addi-
tionally, due to fewer regulations, numerous SHPs
are often commissioned along a single river, leading
to substantial cumulative impacts (Kibler and Tullos
2013). SHPs impede river longitudinal connectivity
due to the barrier effect, which is exacerbated by the
clustering of numerous SHPs on the same river chan-
nel. Although SHPs tend to have smaller storage capa-
cities relative to large dams, their impact on the extent
of flow alteration can vary based on their location,
design, and operating regimes (Timpe and Kaplan
2017). In terms of design, SHPs that store and divert
water from a weir to a downstream powerhouse res-
ult in the creation of dewatered river stretches, which
reduce longitudinal, lateral, and vertical connectivity
(Anderson et al 2006, Jumani et al 2018). Compar-
atively, SHPs that do not store and divert water may
have a smaller impact on flow alteration. In terms of
operations, continued storage and release operations
(commonly employed by SHPs with storage) result in
rapidly fluctuating/flashy flows downstream.
Low-head dams and other small RIPs built to
facilitate infiltration or water diversion usually cluster
closer to the headwater tributaries and result in
smaller fragment sizes. While the impact of indi-
vidual projects might be low, the cumulative frag-
mentation effects of numerous small RIPs can be
significant (Januchowski-Hartley et al 2013). Often
designed with very little active storage, these struc-
tures often allow for some movement of water and
sediment and are expected to have lower individual
impacts on flow alteration. Furthermore, their impact
on flow regulation can be expected to vary based
on the presence or absence of water abstraction
(figure 2).
4

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Figure 2. Expected location and impact of an individual project across different RIP types (medium/large storage dams, small
hydropower projects, and low head dams) on river network fragmentation and flow alteration.
Figure 2illustrates the major axes of hydrologic
fragmentation and alteration, allowing us to coarsely
map the expected impacts of different RIPs. However,
moving from this conceptual model to a quantitative
understanding of connectivity and flow regime alter-
ation requires an understanding of the types of tools
and methods available to do so, as well as their spe-
cific outputs and data requirements. In the following
section, we review the metrics and tools available for
quantifying river fragmentation and flow alteration,
and in section 4we provide guidance for selecting
the most appropriate tool as a function of the study
objective and data availability.
3. Methods to assess river fragmentation
and flow alteration
We compiled key readings on the theory, con-
cepts, and methods associated with river network
connectivity and the natural flow regime. Them-
atic searches of published, peer-reviewed literature
using topic-relevant keywords were conducted on
Google Scholar. Key words used included ‘river con-
nectivity’, ‘river fragmentation’, ‘dendritic connectiv-
ity’, ‘hydrologic connectivity’, ‘dam fragmentation’,
‘metrics of flow alteration’, ‘flow regulation’, and
‘hydrologic alteration’. Additionally, personal refer-
ence libraries and the bibliography of selected papers
were also reviewed to find related and relevant pub-
lications. This resulted in the final selection of 79
publications. Papers that did not define or apply
a specific metric were excluded from the review.
With respect to river fragmentation, we only selec-
ted papers focused on instream riverine connectivity
and excluded those dealing with overland hydrologic
connections (Pringle 2001). Similarly, for flow alter-
ation, we selected papers that quantified the extent
of alteration (descriptive metrics) and excluded those
aimed at prescribing environmental flows (prescript-
ive methods).
3.1. Metrics of river fragmentation
Our review resulted in a compilation of 29 metrics
or methods to quantify river network connectivity
or fragmentation (table 1). Following the classifica-
tion by Calabrese and Fagan (2004), we grouped these
metrics into three categories based on whether they
estimate structural, potential, or actual connectivity.
Structural connectivity metrics are calculated based
on the physical attributes and spatial configuration
of the riverscape; potential connectivity metrics com-
bine information describing an ecosystem process
5

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
or organism dispersal abilities along with informa-
tion on the structural or physical attributes of the
riverscape; actual connectivity metrics are based on a
measured ecosystem process or the observed move-
ment of individuals along the spatial configuration
of the river (Kindlmann and Burel 2008). Hence,
potential and actual connectivity metrics will vary
based on the target taxa or phenomenon being con-
sidered and the spatiotemporal scales at which they
occur (Fullerton et al 2010). Table 1summarises the
description, data requirements, output, spatial scale
of application, and advantages and disadvantages of
each method.
3.2. Metrics of flow alteration
Methods to assess flow alteration can be descript-
ive or prescriptive in their application. Descript-
ive metrics are those that quantify or measure flow
alteration (i.e. how have riverine flows been altered
compared to baseline undisturbed conditions?); pre-
scriptive methods are those aimed at determining
environmental flow requirements (i.e. how much
water can be extracted or used while still maintain-
ing ecosystem processes and functions?) and usually
incorporate one or more descriptive metrics. While
the former is often quantified based on scientific
data input, the latter is management-oriented and
influenced by socio-cultural, economic, and political
drivers. This review focuses only on descriptive met-
rics, as numerous reviews of the application of pre-
scriptive environmental flow methodologies already
exist (Jowett 1997, King et al 1999, Tharme 2003,
Acreman and Dunbar 2004, Hirji and Davis 2009,
Horne 2017). Table 2summarises the description,
data requirements, output, spatial scale of applica-
tion, and advantages and disadvantages of the 12
main descriptive flow alteration metrics.
4. Decision support
4.1. River connectivity metrics
Although connectivity in river networks has been less
studied compared to their terrestrial counterparts, we
documented 29 different methods to quantify river
connectivity or fragmentation from the scientific lit-
erature (table 1). These methods vary considerably
in their data requirements, spatial scale of applica-
tion, and output, each having their own assumptions,
advantages, and disadvantages.
Figure 3presents a decision-making tree to help
identify connectivity metrics that can be used based
on the study objective, data availability, and distri-
bution of infrastructure projects in the river basin
of interest. This decision tree, when used with the
information in table 1, allows users to make informed
decisions when selecting among the connectivity
measures available and to design impact studies with
an eye toward quantifying specific outcomes. For
example, when assessing the impact of fragmenta-
tion on biotic communities, in a case where little or
no empirical data are available on the species/taxa of
interest, the decision tree presents 16 available struc-
tural and potential connectivity metrics to choose
from. Similarly, when assessing the impact of frag-
mentation on basin-wide processes, users can select
among 11 different structural, potential, and actual
measures (figure 3).
When reviewing these methods holistically, a
clear trade-off emerges between data availability and
the type of connectivity that can be assessed. While
actual connectivity metrics yield the most direct and
reliable measure of connectivity, their application
across spatial scales is often limited by the availab-
ility of field data. Nevertheless, these methods can
be effectively applied at finer spatial scales to address
specific objectives. For example, actual connectiv-
ity metrics are ideal to assess the efficacy of fish
passes (Oldani and Baig´
un 2002, Knaepkens et al
2006, Naughton et al 2007), species responses to dam
removals (Liermann et al 2017), or the restoration of
specific migration pathways (Beasley and Hightower
2000). Among the actual connectivity metrics, only
genetic or molecular techniques provide information
across extended temporal scales, whereas other meth-
ods usually quantify short-term dispersal during the
period of data availability.
In contrast, structural connectivity indices are not
data-intensive and can be calculated with relative ease
across broader spatial scales. However, they provide
only a crude estimate of connectivity, which may or
may not reflect actual conditions at the scale of their
application (Mahlum et al 2014). Given these draw-
backs, potential connectivity metrics present a more
suitable choice in the absence of empirical data. These
metrics can be informed by secondary information
on ecological or biotic requirements (such as dispersal
probabilities or habitat requirements) and can be
used to calculate potential connectivity across broad
spatial scales with relative ease. Often, structural con-
nectivity metrics have been modified or adapted to
suit research needs and data availability. For example,
the Dendritic Connectivity Index (Cote et al 2009) has
been used as the basis for other derivative connectiv-
ity metrics, such as the River Connectivity Index
(Grill et al 2014) and the Fragmentation Index (Díaz
et al 2019). Similarly, several structural connectiv-
ity metrics can be modified to incorporate additional
information to become more ecologically meaning-
ful. For example, river lengths can be weighted based
on habitat quality or habitat preference of target taxa
(Grill et al 2014, Buddendorf et al 2017).Likewise, for
structural metrics that treat all river reaches as equal,
increasing weights can be assigned to higher stream
orders or increasing river widths based on ecological
considerations and scale of analysis (Díaz et al 2019).
When assessing connectivity with respect to a
target species or guild, their behaviour, life history,
6

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. List of river connectivity or fragmentation metrics with their description, data requirements, outputs, spatial scale of application, and advantages and disadvantages.
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Structural connectivity metrics
Between Centrality
(Freeman 1977)
Reflects the importance
of each stream reach in
maintaining connec-
tions between all other
pairs of stream reaches
in a riverscape.
•River network
lengths
•Dam locations
Reaches ranked by their
importance in main-
taining basin-wide con-
nectivity
Stream reach •No primary data
needed
•Various development
scenarios can be
assessed
•Helps identify
important reaches
that maintain basin-
level connectivity
•Can be assessed using
integral index of
connectivity (IIC)
or probability of
connectivity (PC)
metrics (see below)
•Can incorporate nat-
ural barriers (water-
falls)
•Does not assess connectiv-
ity across spatial scales
•Does not explicitly analyse
the effects of dams
•Values may not change
even with the addition/re-
moval of dams
•Treats stream reaches
across a longitudinal
gradient as ecologically
equivalent
•Connectivity treated as a
binary value
•Does not incorporate any
other ecological character-
istics
Bodin and Saura 2010;
Segurado et al 2013
Lateral connectivity
classes (Amoros et al
1987)
Descriptive classes of
lateral connectivity
(0–5) between the main
channel and side chan-
nels
•Modalities of con-
nection between
waterbodies/side
channels and the
main channel (i.e.
extent of connection
during high and low
flow events)
Five lateral connectivity
classes (5–0 indicating
completely connected to
isolated)
Waterbodies/side chan-
nels
•One of the few meas-
ures of lateral con-
nectivity
•Easy to compute
•Modalities of con-
nection can be
assessed based on
field observations or
satellite imagery
•Minimal data
requirements
•Seasonal and his-
torical changes over
times can be assessed
•Can be quickly
assessed across spa-
tial scales
•Descriptive classes; does
not monitor the duration
and intensity of the actual
hydrological connection
•Assessing modalities of
connectivity for each side
channel and waterbody
can be challenging
•Side channels of different
sizes and attributes (and
hence having different
levels of resilience) may be
classified under the same
category
Lasne et al 2007
(Continued)
7

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Fragmentation classes
(Nilsson et al 2005)
A descriptive measure
based on the longest
undammed length of
the main river channel
in relation to the entire
channel length.
•River network
lengths
•Dam locations
Five fragmentation
classes (very low to very
high)
Sub-basin to basin •Easy to compute
•Minimal data
requirements
•No primary data
needed
•Various development
scenarios can be
assessed
•Can assess connectiv-
ity across spatial
scales
•Subjective classification
•Not spatially explicit
•Values may not change
even with the addition/re-
moval of dams
•Cannot incorporate barrier
permeabilities
•Treats stream reaches
across a longitudinal
gradient as ecologically
equivalent
•Does not incorporate any
other ecological character-
istics
Díaz et al 2019
Barrier density (Park
et al 2008)
A descriptive measure
calculated as the total
number of barriers per
total river length
•River network
lengths
•Number of barriers
Density of barriers per
length of river
River reach to basin •Can incorporate
natural barriers
•Easy to compute
•Minimal data
requirements
•No primary data
needed
•Can be calculated
across spatial scales
•Various development
scenarios can be
assessed
•Does not explicitly analyse
the effects of dams
•Not spatially explicit
•Treats stream reaches
across a longitudinal
gradient as ecologically
equivalent
•Cannot incorporate barrier
permeabilities
•Headwater RIPs that lie
beyond the delineated river
network are often excluded
from analysis
•All dams are treated the
same despite differences in
size and impact
•Does not incorporate any
other ecological character-
istics
Jones et al 2019;
Atkinson et al 2020
(Continued)
8

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Continuity index (Prato,
Comoglio, and Calles
2011)
A descriptive measure
calculated as the ratio of
total river length to the
number of obstacles
•River network
lengths
•Number of barriers
Ratio of total river
length to the number
of obstacles
River reach to river net-
work
•Easy to compute
•Minimal data
requirements
•No primary data
needed
•Can be calculated
across spatial scales
•Various development
scenarios can be
assessed
•Does not explicitly
analyse the effects of
dams
•Not spatially explicit
•Treats stream reaches
across a longitudinal
gradient as ecologic-
ally equivalent
•Cannot incorporate
barrier permeabilities
•Does not incorporate
any other ecological
characteristics
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
•All dams are treated
the same despite
differences in size
and impact
Prato et al 2011
(Continued)
9

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Total remaining core
length (Fuller et al 2015)
The length of unaffected
core habitat for a spe-
cific species or guild,
calculated as the differ-
ence between the total
network length and the
length of river affected
by fragmentation (sum
of upstream and down-
stream matrix and edge
habitats created by each
barrier in the network)
•River network
lengths
•Dam locations
•Length of dam-
affected matrix and
edge habitats
•Habitat requirements
of target species or
guilds
Total remaining core
length
Sub-basin to basin •No primary data
needed
•Incorporates specific
habitat requirement
data based on target
species or guilds
•Can be evaluated
for target species,
taxa or guilds based
their specific habitat
requirements
•Accounts for dams
of different sizes and
ecological impact,
i.e. all dams are not
treated the same
•Can incorporate
natural barriers
•Not spatially explicit
•Values are centred
around a focal taxa
or guild, hence not
directly comparable
•Cannot incorporate
barrier permeabilities
•Measuring the length
of dam-affected mat-
rix and edge habitats
can be subjective and
challenging
•Treats stream reaches
across a longitudinal
gradient as ecologic-
ally equivalent
•Results susceptible
to change based on
the extent of the river
network (i.e. sensit-
ive to DEM resolu-
tion, flow direction
and accumulation
algorithms and delin-
eation thresholds
used)
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
Hall et al 2011
(Continued)
10

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Dam Impact Index
(Latrubesse et al 2017)
An index calculated
from (i) the ratio of
river length affected by
dams, (ii) ratio of num-
ber of major tributaries
affected by dams, and
(iii) number of dams per
basin/sub-basin
•River network
lengths
•Dam locations
•Length of dam-
impacted river
reaches
Index of impact from 0
to 100
Sub-basin to basin •No primary data
needed
•Easy to compute
•Incorporates 3 differ-
ent metrics
•Can assess various
developmental scen-
arios
•Can incorporate
natural barriers
•Can account for
dams of different
sizes and ecological
impact
•Can be calculated
across spatial scales
•Not spatially explicit
•Measuring the length of
dam-affected upstream
and downstream river
reaches can be subjective
•Treats stream reaches
across a longitudinal
gradient as ecologically
equivalent
•Results susceptible to
change based on the extent
of the river network
•Headwater RIPs that lie
beyond the delineated river
network are often excluded
from analysis
•Does not incorporate any
other ecological character-
istics
Latrubesse et al 2017
River channel
connectivity index
(Li et al 2018)
Quantifies the unob-
structed degree of
river flow based on the
concept of time access-
ibility. It is calculated
as the ratio of the time
accessibility of a given
volume of streamflow
without any barriers to
that with barriers from
one location to another
in the river channel
•River network length
•Dam locations
•Barrier classification
and estimated block-
ing weights (based on
natural flow passabil-
ity)
•Channel cross-
section for flow with
and without barriers
Index ranging from
0 (disconnected) to 1
(connected)
River reach to tributary •Incorporates aspects
of streamflow
•Barriers treated indi-
vidually to assess
blocking degree
•Various development
scenarios can be
assessed
•No primary data
needed
•Values range between
0 and 1 and are easy
to interpret
•Treats stream reaches
across a longitudinal
gradient as ecologically
equivalent
•Assumes river velocity the
same with or without a
barrier
•Relies on accurate assess-
ment of cross-sectional
area
•Requires expert knowledge
on barrier impacts to score
barriers
•Cannot incorporate nat-
ural barriers
Li et al 2018
(Continued)
11

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Potential Connectivity Metrics
Integral index of
connectivity (IIC)
(Pascual-Hortal and
Saura 2006)
A habitat reachability
index based on habitat
availability and binary
connectivity values for
a target taxa or guild. It
assesses the possibility
of dispersal between all
pairs of stream reaches
based on topological
distances
•River network
lengths or patch area
•Dam locations
•Estimate of threshold
dispersal distance
Index of connectivity
ranging from 0 to 1
Subbasin to basin •Easy to compute
•No primary data
needed
•Various development
scenarios can be
assessed
•Can be used to meas-
ure maximum dis-
persal distance
•Suited to study
genetic transmission
or connectivity
•Can assess connectiv-
ity across spatial
scales
•Various development
scenarios can be
assessed
•Barrier permeability
treated as a binary
value
•Results susceptible
to change based on
the extent of the river
network
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
•Estimate of threshold
dispersal distance can
be arbitrary
•Does not accurately
represent the actual
number of organisms
that move through-
out the landscape
Segurado et al 2013;
Branco et al 2014;
Lehotský et al 2018
(Continued)
12

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Probability of Con-
nectivity (PC) (Saura
and Pascual-Hortal
2007)
A habitat reachability
index, like the IIC, that
assesses the probabilit-
ies of dispersal between
all pairs of patches or
stream reaches. Con-
nectivity is not restricted
to binary values.
•River network
lengths
•Dam locations
•Estimates of dispersal
probabilities
•Directional dam
passability valuesa
Index of connectivity
ranging from 0 to 1
Subbasin to basin •No primary data
needed
•Can incorporate
continuous barrier
permeabilities
•Can incorporate
natural barriers
•Various development
scenarios can be
assessed
•Correctly assumes
the probability of
passing a barrier is
dependent of the
probability of passing
other barriers
•Greater importance
given to reaches with
large flows
•More accurately rep-
resents the number
of organisms that
move throughout the
landscape
•Distinct upstream
and downstream dis-
persal probabilities
can be set
•Can assess connectiv-
ity across spatial
scales
•Estimating exact dis-
persal probabilities
can be challenging
•Results susceptible
to change based on
the extent of the river
network
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
Bodin and Saura 2010;
Malvadkar et al 2015
(Continued)
13

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
PCA lateral connectiv-
ity metric based on
environmental vari-
ables (PCA-LC) (Paillex
et al 2007)
A surrogate measure of
lateral connectivity. Five
environmental variables
are summarized with a
centred principal com-
ponent analysis to pro-
duce a factorial axis that
is used as the synthetic
variable for the level of
connectivity between the
main river channel and
the cut-off channels.
•Paired sites along
the main river and
cut-off channels
•Measured data on
5 environmental
variables (water con-
ductance, aquatic
vegetation cover,
organic content of
the upper sediment
layer, diversity of
sediment grain size,
NH3-N concentra-
tion)
Site scores along the
primary PCA factorial
axis, with increasing
values corresponding to
increasing connectivity
Sites from which data
have been gathered
•One of the few met-
rics measuring lateral
hydrological con-
nectivity
•Suitable to river–
floodplain systems
•The 5 environmental
variables are known
to integrate the level
of connectivity of
the floodplain sites
with the main river
channel
•Values of the
factorial axis indicate
between-sites vari-
ability in measured
variables
•PCA site scores can
be rescaled between
0 (lowest connectiv-
ity) and 1 (highest
connectivity)
•Only a surrogate
measure; does not
monitor the duration
and intensity of the
actual hydrological
connection
•Reliability of this
metric depends
on the statistical
strength of the
factorial axis being
used
•Between-site variabil-
ity in environmental
variables is assumed
to be explained only
by the extent of lat-
eral connectivity.
However, it may also
be influenced by
other variables, such
as season, decompos-
ition, nutrient con-
sumption by plants
etc.
•Does not account for
other connectivity
dimensions
Besacier-Monbertrand
et al 2014
(Continued)
14

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Dendritic Connectivity
Index—potadromous
(Cote et al 2009)
An index of connectiv-
ity calculated from
stream length, which
assesses the potential
of a potadromous fish
to travel between two
chosen points in a river
network. Based on coin-
cidence probability (Jae-
ger 2000)
•River network
lengths
•Dam locations
•Directional dam
passability valuesa
•Waterfall locationsa
An index of connectivity
ranging from 0 to 100
River reach to basin •Can incorporate
natural barriers
•Easy to compute
•Minimal data
requirements
•No primary data
needed
•Values range between
0 and 100 and are
easy to interpret
•Barrier permeabilit-
ies can be incorpor-
ated
•Various development
scenarios can be
assessed
•Can assess connectiv-
ity across spatial
scales
•Distinct upstream
and downstream dis-
persal probabilities
for target species/taxa
can be set
•If species/taxa spe-
cific data unavailable,
index can be applied
with binary passab-
ility values (in which
case it is a structural
metric)
•Results susceptible
to change based on
the extent of the river
network
•Treats stream reaches
across a longitudinal
gradient as ecolo-
gically equivalent.
Hence, dams placed
upstream or down-
stream can produce
same DCI values des-
pite having different
ecological impacts
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
•Assumes the prob-
ability of passing one
barrier is independ-
ent of the probability
of passing another
barrier
Perkin and Gido 2012;
Anderson et al 2018
(Continued)
15

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Dendritic Connectivity
Index—diadromous
(Cote et al 2009)
An index of connectivity
calculated from stream
length, which assesses
the proportion of river
length accessible to a
diadromous fish from
the mouth of a river
•River network
lengths
•Dam locations
•Upstream and down-
stream dam passabil-
ity valuesa
•Waterfall locationsa
An index of connectivity
ranging from 0 to 100
River reach to basin •Can incorporate
natural barriers
•Easy to compute
•Minimal data
requirements
•No primary data
needed
•Values range between
0 and 100 and are
easy to interpret
•Barrier permeabilit-
ies can be incorpor-
ated
•Various development
scenarios can be
assessed
•Can assess connectiv-
ity across spatial
scales
•Distinct upstream
and downstream dis-
persal probabilities
for target species/taxa
can be set
•If species/taxa spe-
cific data unavailable,
index can be applied
with binary passab-
ility values (in which
case it is a structural
metric)
•Results susceptible
to change based on
the extent of the river
network
•Values may not
change even with
the addition/removal
of dams upstream of
the first dam on the
mainstem
•Treats stream reaches
across a longitudinal
gradient as ecologic-
ally equivalent
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
•Assumes the prob-
ability of passing one
barrier is independ-
ent of the probability
of passing another
barrier
Buddendorf et al 2017;
Choy et al 2018
(Continued)
16

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Index of longitudinal
riverine connectivity
(ILRC) (Crook et al
2009)
evaluate both upstream
and downstream effects
of dams and water with-
drawals on longitudinal
connectivity in tropical
streams evaluate both
upstream and down-
stream effects of dams
and water withdrawals
on longitudinal con-
nectivity in tropical
streams evaluate both
upstream and down-
stream effects of dams
and water withdraw-
als on longitudinal
connectivity in trop-
ical streams Estimates
probability that an indi-
vidual shrimp larva can
migrate downstream to
the estuary (based on
proportion of median
flow left in the stream
after withdrawal) and
return to the reach
where it was released
as a larva (based on pro-
portion of days with
flow over the impound-
ment)
stimate the probability
that an individual ‘aver-
age’ shrimp will be able
to migrate downstream
to the estuary and return
to the reach where it was
released as a larva stim-
ate the probability that
an individual ‘average’
shrimp will be able to
migrate downstream to
the estuary and return
to the reach where it was
released as a larva estim-
ate the probability that
an individual ‘average’
shrimp will be able to
migrate downstream to
the estuary and return
to the reach where it was
released as a larva.
•Locations of dams
and water-intake
structures
•Long-term daily
streamflow data
•Estimated or actual
water withdrawal
volume data
Index ranging between
0 and 1, split into three
classes (high, moderate
and low for ILRC scores
of 0–0.33, 0.34–0.66,
and 0.67–1 respectively)
Each water intake struc-
ture
The effect of water withdrawal
on juvenile shrimps is influenced
by individual intakes in addi-
tion to all downstream intakes.
Where there are intakes in linear
succession, juvenile shrimps may
have to climb past all intakes in
order to reach their ultimate
habitat. In order to account
for the lower probability that
an individual juvenile shrimp
will successfully scale multiple
intakes, the proportion of days
with flow for any downstream
intake is multiplied by the pro-
portion of days with flow for any
upstream intake
•Incorporates the effect of
flow alteration and dams on
longitudinal connectivity
•Accounts for upstream and
downstream cumulative pas-
sage probabilities
•Represents longitudinal con-
nectivity of streams from
headwaters to estuaries.
•Related to a biotic response;
ecologically meaningful
•Can be evaluated in relation
to months of seasonally low
discharge and drought
•Data intensive;
requires long-term
daily streamflow data
and water withdrawal
volumes
•Suited to assess con-
nectivity with respect
to shrimp
•Connectivity classes
are arbitrarily
described based on
the index value
•Assumes larvae are
uniformly mixed in
the water column
although larval dens-
ity varies with flow
volume
•Assumes that dam
reservoirs do not
impede connectivity
Crook et al 2009
(Continued)
17

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Between Centrality—k
(Bodin and Saura 2010)
A modified BC metric
that weighs each stream
reach by its patch area
and maximum dispersal
probabilities or topo-
logical distances (based
on whether PC or IIC
metric is used).
•River network
lengths
•Habitat area or
volume
•Dam locations
•Estimates of dispersal
probabilitiesa
Reaches ranked by their
importance in main-
taining basin-wide con-
nectivity
River reach •No primary data
needed
•Various development
scenarios can be
assessed
•Stream reaches car-
rying larger flows
that connect bigger
patches are assigned
higher weights; more
ecologically mean-
ingful
•Incorporates dis-
persal probabilities
•Does not assess con-
nectivity across spa-
tial scales
•Does not explicitly
analyse the effects of
dams
•Values may not
change even with
the addition/removal
of dams
•Headwater/fringe
reaches will always be
ranked lower
Segurado et al 2013
(Continued)
18

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
DEN connectivity model
(Padgham and Webb
2010)
Represents the ability of
a fish to access different
parts of a river network.
Model based on hab-
itat length, quality, and
directional transition
probabilities.
•Habitat quality (0–1)
•Reach length
•Dam locations
•Upstream and down-
stream connectivity
values (0–1)
•Habitat volume
Matrix of transition
probabilities between
every pair of reaches
in a network +reach
scores that indicate equi-
librium proportions of
a population expected
within each reach
River reach to network •Spatially explicit
•Can assess con-
nectivity necessary
to maintain meta-
populations
•Incorporates
upstream and down-
stream connectivity
probabilities
•Incorporates habitat
quality as a variable
influencing con-
nectivity
•It can be applied for
one or more target
species based on their
life history strategies
and specific habitat
requirements
•Various development
scenarios can be
assessed
•More complex para-
meters can be applied
to the model
•Weighted by habitat
volume; more ecolo-
gically meaningful
•Assumes an unlim-
ited range, which is
biologically unreal-
istic. But incorpor-
ating restricted spe-
cies ranges increases
uncertainty of estim-
ates
•Quantifying dir-
ectional transition
probabilities can be
challenging; results
may vary based on
the method used and
assumptions made
•More data intensive
•Quantifying habitat
quality is challenging
•Computationally
more challenging
•Results susceptible
to change based on
the extent of the river
network
•Theoretical models
with little empirical
support
Webb and Padgham
2013
(Continued)
19

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Barrier score
(Nunn and Cowx 2012)
Each barrier is scored
based on a prioritiz-
ation matrix of fish
stock status, passage
efficiency, likelihood of
access, habitat quantity
and habitat quality
Scores (1–5) for:
•Fish stock status of
the target species
•Passage efficiency of
target species
•Likelihood of access
based on upstream
passage
•Habitat quantity
•Habitat quality
Barrier scores ranging
from 1 to 3125
Each barrier •Easy to compute
•Suitable to rapidly
assess and prioritize
migration barriers
for passage improve-
ments
•Can be applied for
more than one target
species or river basin
•In cases of lack-
ing empirical data,
expert judgement can
be used
•Various development
scenarios can be
assessed
•Spatially explicit
•Incorporates 5 vari-
ables: more ecologic-
ally meaningful
•Can incorporate
cumulative passage
probabilities
•Does not quantify
connectivity of frag-
mentation of river
reach, network or
basin. Instead ranks
each barrier based on
potential for passage
improvements
•Not applicable across
spatial scales
•Barriers ranked as
highest priority need
not be the ones that
affect connectivity
the most
•Scoring of the five
variables for each
fragment relies on
subjective data or
expert judgement
Nunn and Cowx 2012
(Continued)
20

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Habitat Connectivity
Index of Upstream pas-
sage (HCIUP) (Mckay
et al 2013)
Assesses upstream fish
passage connectivity
as a habitat-weighted,
cumulative passage rate.
By summing across all
reaches, the HCIUP is
computed as the ratio of
accessible to total habitat
in the river network
•Metric of habitat
availability (river
network length, area,
volume etc)
•Upstream connectiv-
ity values (0–1)
•Dam locations
•Waterfall locationsa
Ratio of accessible hab-
itat ranging from 0 to 1
Sub-basin to basin •Can assess connectiv-
ity for target species,
taxa or guilds
•The measure of hab-
itat availability could
factor in habitat
quality, discharge
or other variables of
interest (river length,
area, volume, length-
weighted discharge
etc)
•Can be modified to
assess the impacts
of fragmentation
on other processes
such as movement
of woody debris or
sediment.
•Incorporates
quantum of hab-
itat accessible and the
cumulative passage
rate to that point
•Can assess connectiv-
ity across spatial
scales
•Can incorporate
natural barriers
•Focuses on upstream
connectivity only—
downstream passage
is neglected (suited
for diadromous spe-
cies)
•Computationally
more challenging,
especially as network
topology becomes
more complex
•Assumes the prob-
ability of passing one
barrier is independ-
ent of the probability
of passing another
barrier
•Quantifying trans-
ition or connectivity
probabilities can be
challenging; results
may vary based on
the method used and
assumptions made
•Values may not
change even with
the addition/removal
of dams
Rodeles et al 2019
(Continued)
21

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
River Connectivity
Index (RCI) (Grill et al
2014)
An index of connectivity
calculated from river
flow volume; like DCI, it
assesses the potential of
a fish to travel between
two chosen points in a
river network.
•River network
lengths
•Reach wetted widths
and heights
•Dam locations
•Dam passability
valuesa
•Waterfall locationsa
An index of connectivity
ranging from 0 to 100
River reach to basin •Values sensitive to
the location of the
barrier on the river
network (impact of
dams further down-
stream is weighted to
be higher by volume)
•Can incorporate
natural barriers
•No primary data
needed
•Values range between
0 and 100 and are
easy to interpret
•Barrier permeabilit-
ies can be incorpor-
ated in the analysis
•Various development
scenarios can be
assessed
•Can assess connectiv-
ity across spatial
scales
•Challenging to derive
reach-level habitat
volume (computed
as reach lengthawet-
ted widthawater
stage/height); often
volume estimates
are prone to high
error in small reaches
and regions of poor
data-availability
•Results susceptible
to change based on
the extent of the river
network
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
•Assumes the prob-
ability of passing one
barrier is independ-
ent of the probability
of passing another
barrier
Grill et al 2015
(Continued)
22

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Weighted River Con-
nectivity Index (Grill
et al 2014)
An index of connectiv-
ity calculated from
river flow volume and
weighted by ecologically
meaningful variables
such as river class/e-
coregion (RCIclass) or
species-specific migra-
tion ranges (RCIrange).
•River network
lengths
•Reach wetted widths
and heights
•Dam locations
•Information on key
variables to be used
in the weighting
•Dam passability
valuesa
•Waterfall locationsa
An index of connectivity
ranging from 0 to 100
River reach to basin •Values sensitive to
the location of the
barrier on the river
network (impact of
dams downstream
weighted to be higher
by volume)
•Can incorporate
natural barriers
•Can incorporate
other variables of
importance such as
connectivity between
different river classes
or migration ranges
•Values range between
0 and 100 and are
easy to interpret
•Barrier permeabilit-
ies can be incorpor-
ated in the analysis
•Various development
scenarios can be
assessed
•Can assess connectiv-
ity across spatial
scales
•Challenging to derive
reach-level habitat
volume (same as
RCI)
•Results susceptible
to change based on
the extent of the river
network
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
•Assumes the prob-
ability of passing one
barrier is independ-
ent of the probability
of passing another
barrier
Grill et al 2014
(Continued)
23

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
C metric (Diebel et al
2015)
Defines the connectivity
of a stream reach as a
function of the degree of
access to and from the
range of seasonal habitat
types that fish use. The
‘C’ values for all the
segments in a watershed
can be aggregated to
describe connectivity at
the watershed scale
•River network length
•Habitat types
•Barrier passability
values
•Habitat quality
metricsa
•Dam locations
•Waterfall locationsa
•Distance-weighted
dispersal limit
Connectivity status ran-
ging from 0 to 1
Reach and watershed
level
•Quantifies the indi-
vidual and cumulat-
ive effects of barriers
•Accounts for natural
barriers
•Accounts for habitat
quantity, quality, and
distance of different
habitat types that
can be accessed by
stream-resident fish
in both directions
•Incorporates
distance-based dis-
persal limitations
•Can be defined for an
individual species or
a fish community
•Barrier permeabilit-
ies can be incorpor-
ated
•Various development
scenarios can be
assessed
•Can assess connectiv-
ity across spatial
scales
•Oriented to stream-
resident fish; not
suited to diadromous
species
•Does not explicitly
analyse the effects of
individual dams
•Treats stream reaches
across a longitudinal
gradient as ecologic-
ally equivalent
•Results susceptible
to change based on
the extent of the river
network
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
•More data-intensive
O’Hanley et al 2013
(Continued)
24

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Fragmentation index
(Díaz et al 2019)
A fragmentation index
calculated from stream
length and Strahler
stream order
•River fragment
lengths
•River fragment
stream order
•Dam locations
Fragmentation index
between 0 to 1
Sub-basin to basin •No primary data
needed
•Easy to compute
•Values range between
0 and 1 and are easy
to interpret
•Values sensitive to
the location of the
barrier on the river
network
•Can incorporate
natural barriers
•Various development
scenarios can be
assessed
•Can assess connectiv-
ity across spatial
scales
•Allows assessment of
cumulative effects of
barriers
•Barrier permeability
treated as a binary
value
•Cannot incorporate
ecological informa-
tion
•Stream orders are
dependent on data
resolution and
threshold of delin-
eation
•Results susceptible
to change based on
the extent of the river
network
•All dams are treated
the same despite
differences in size
and impact
•Headwater RIPs that
lie beyond the delin-
eated river network
are often excluded
from analysis
Díaz et al 2019
(Continued)
25

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Metapopulation models
of directed connectivity
A migration model for
metapopulation con-
nectivity of salmon (or
any other diadromous
species)
•River network length
•Dam locations
•Locations of pop-
ulation groups and
sizes
•Distance matrix
between all popu-
lations
•Distance-based dis-
persal probability
matrixa
•Population source-
sink structure as a
diagraph
Diagraphs of spatially
explicit populations
under various scenarios
of development (with
population size and con-
nectivity strength and
direction illustrated)
Basin scale •Assesses impact of
barriers on popula-
tion or metapopu-
lation of target spe-
cies in a basin(s).
Can shed light on
source-sink dynam-
ics, colonisation, and
network-wide popu-
lation connectivity
•Historic and future
scenarios of develop-
ment can be incor-
porated
•Incorporates recruit-
ment in its measure
of connectivity
•Spatially explicit
•Graph theory sheds
light on inter-
population con-
nectivity and the
importance of single
populations in a river
network
•Model illustrates
system function,
and sheds light on
restoration strategies
•Data-intensive;
requires information
on the distribution of
distinct populations
in a river network,
population size and
movement dynamics
•Species-specific
and works best for
anadromous species
•Not suited to non-
migratory species
with small home
ranges
•Defining distinct
populations could be
subjective
•Defining strength of
inbound and out-
bound connections
could be subject-
ive and error-prone
depending on data
availability
•Analysis cannot be
carried out across
spatial scales
Isaak et al 2007; Schick
and Lindley 2007;
Leibowitz and White
2009
(Continued)
26

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Actual connectivity metrics
Lateral connectiv-
ity parameter (Cd)
(Reckendorfer et al
2006)
A connectivity para-
meter (Cd) defined as
the average annual dur-
ation (days per year) of
surface connection of
floodplain waterbod-
ies with the main river
channel
•Stage-discharge
relationship at the
upstream end of each
side channel
•Frequency distri-
bution of river dis-
charge
•Stage at which water
flows into the side
channel
Cdvalues for each
waterbody
Waterbodies/side chan-
nels
•Quantifies the dura-
tion of actual hydro-
logical connection
based on flow data
•Depends on flow pat-
tern of the river and
the position of these
waterbodies relative
to river height
•Can be calculated
across seasonal and
temporal time scales
•Waterbodies can
be categorised into
connectivity classes
based on ranges of
Cdvalues
•Reliance on multi-
year flow data limits
its application in
data-deficit regions
•Cannot assess
impacts of proposed
scenarios since it
relies on flow data
•Ability to calculate
Cdvalues for a side
channel depends on
the availability of a
gauging station at its
upstream end
•Change in Cdcan be
influenced by RIPs
or other drivers such
as climate change or
changes in baseline
conditions
•Does not account for
other connectivity
dimensions
Reckendorfer et al 2006
(Continued)
27

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Human observations of
movement
Location of target spe-
cies/taxa over the study
area
•Variable, depending
on study objectives
and design (snor-
kelling, filming)
Variable River reach to tributary •Detailed information
on actual move-
ment/connectivity
and behaviour
•Accurately represents
the movement of
animals
•Effort intensive
•Smaller spatial scale
of application
•Data collection is
limited by on-ground
condition
•Influenced by imper-
fect detections
Johnston 2000
Bio-
acoustic/hydroacoustic
sonar
Measurement of fish
locations, densities,
and movement using
fixed or mobile acoustic
sensors
•Primary hydroacous-
tic sonar data
Variable River reach to river net-
work
•Detailed information
on actual move-
ment/connectivity
•Can incorporate
aspects of behaviour
•Accurately represents
the movement of
animals
•Influence of seasons
and other habitat
parameters can also
be assessed
•High data processing
•Limited application
•Expensive
•Effort intensive
•Smaller spatial scale
of application
•Species/taxa specific
Burwen et al 2005; Dey
et al 2019
(Continued)
28

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Telemetry Movement information
of tagged individuals
over space and time
•Telemetry data (usu-
ally from PTI, radio
or acoustic tags)
Variable River reach to river net-
work
•Detailed information
on actual move-
ment/connectivity
and behaviour
•Accurately represents
the directionality and
extent of movement,
path of travel and
passage efficiency
•Influence of body
size, sex, maturation
and other variables
on dam passability
can be assessed
•Expensive
•Data and effort
intensive
•Smaller spatial scale
of application
•Data collection is
limited by on-ground
condition
•Species/taxa specific
•Tagging may influ-
ence species beha-
viour, survival and
growth, thereby con-
founding results
Schrank and Rahel 2004;
Gosset et al 2006
Direct sampling (elec-
troshocking, seining or
trapping)
In-situ fish capture Spatially explicit
information on:
•Richness
•Presence/absence
•Abundance
•Density
•Species composition
Presence-absence data,
composition similar-
ity, richness, diversity,
abundance and density
estimates
River reach to river net-
work
•Detailed information
on community com-
position and changes
across spatiotem-
poral scales
•Can measure eco-
logical continuity
based on composi-
tion dis(similarity)
across sites
•Can be linked to
river infrastructure
projects and other
influencing variables
•Influence of body
size, sex, maturation
and other variables
on dam passability
can be assessed
•Limited information
on barrier passability
•Effort intensive to
collect data over
spatiotemporal scales
•Smaller spatial scale
of application
•Data collection is
limited by on-ground
condition
•If not accounted for,
variable gear effi-
ciency and sampling
effort can produce
misleading results
•Species/taxa specific
Merritt and Wohl 2006;
Alexandre and Almeida
2010; Jumani et al 2018
(Continued)
29

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 1. (Continued).
Connectivity/
fragmentation metric Description
Inputs/Data
requirements Outputs
Spatial scale of
application Advantages Disadvantages Applications
Molecular or genetic
markers (such as DNA
microsatellites)
Genetic material extrac-
ted from tissue samples
•Molecular or genetic
data on target species
Genetic variation,
diversity, differentiation
or similarity
Subbasin to basin •Quantifies metapop-
ulation connectivity
for a given site and
species of interest
•Fine spatiotemporal
resolution
•Can shed light on
connectivity across
temporal scales
•Can distinguish
between populations
and even individuals
•Requires technical
skill to analyse and
interpret the data
•Connectivity can be
assessed at the scale
of the populations or
individuals
•Often requires spe-
cialised mathematical
and computer pro-
gramming expertise
to develop models
•Requires collec-
tions of specimens
or biotic samples
•Significantly more
expensive compared
to other methods
Wofford et al 2005;
Faulks et al 2011; Tor-
terotot et al 2014
anot essential data requirements
30

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. List of flow alteration metrics with their description, data requirements, output, spatial scale of application, and advantages and disadvantages.
Flow altera-
tion metric Description
Data
requirements Output
Spatial
scale of
application Advantages Disadvantages Applications
Annual pro-
portional
flow devi-
ation (APFD)
(Gehrke et al
1995)
Comparison
of post-
impact and
unimpacted
baseline
monthly
flows, cal-
culated as
the sum of
the ratios of
change in
monthly
flow
(actual—
natural)
to natural
monthly
flow
Short-term
(1–5 years)
monthly flow
data across un-
impacted and
impacted spa-
tial or temporal
scales
APFD values ranging
from 0 (unregu-
lated river) to 3.46
(where there is a
100% increase or
decrease in flow with
no seasonal change)
River reach from
which hydrolo-
gical data have been
gathered
•Reliance on monthly
measured or simulated
flow data increases its
scope of application even
in data-limited environ-
ments
•Simple indicator, can be
quickly calculated when
flow data are available
•Indicates how flow
volume and seasonal
flow patterns are being
affected; mitigation meas-
ures can be tailored to
target restoration
•Can assess the individual
and cumulative impact of
reservoirs
•sensitivity to changes in
flow waveform.
•Can be calculated at
monthly and annual
timescales
•Reliance on monthly flow
data can limit its applica-
tion in data-deficit regions
•Difficult to obtain unim-
pacted monthly flows
•Difficult to assess RIPs
with short post-dam
hydrology or no flow
gauges
•Cannot assess impacts
of proposed RIPs since it
relies on post-dam flow
data
•Ability to calculate cumu-
lative impacts depends
on the spatial configura-
tion of dams and gauging
stations
•Change in flow para-
meters between ‘before’
and ‘after’ scenarios can
be influenced by other
drivers such as climate
change or changes in
baseline conditions (i.e.
assumes stationarity)
•Does not directly relate
ecological responses to
flow statistics
•Does not explicitly con-
sider various components
of the flow regime
•Not suitable for ephem-
eral streams where natural
monthly flows can be nil
Ladson et al 1999
(Continued)
31

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Indicators
of hydrolo-
gic alteration
(IHA) (Richter
et al 1996)
Quantifies
the eco-
hydrological
effects of
flow reg-
ulation by
measur-
ing changes
in 33 flow
statistics,
organized
within the
five primary
compon-
ents of flow
regime (flow
magnitude,
frequency,
duration,
timing,
and rate
of change)
Time-series of
daily stream-
flow data
Measures
of central
tendency
and disper-
sion for 33
hydrologic
paramet-
ers (i.e. 66
inter-annual
statistics)
River reach
from which
hydrolo-
gical data
have been
gathered
•Strong conceptual found-
ation
•Quantitatively robust
•Simple indicators of flow
components allow for
quick calculation when
flow data are available
•Indicates the degree to
which different flow com-
ponents are being affected;
mitigation measures can
be tailored to target res-
toration
•Analysis supported by
an open-access software
developed by The Nature
Conservancy
•Can assess the individual
and cumulative impact of
reservoirs
•Reliance on long-term
flow data limits its applic-
ation in data-deficit
regions
•Difficult to assess dams
with short post-dam
hydrology or no flow
gauges
•Cannot assess impacts
of proposed RIPs since it
relies on post-dam flow
data
•Extensive data processing
to account for data-gaps
•Ability to calculate cumu-
lative impacts depends
on the spatial configura-
tion of dams and gauging
stations
•Assumes stationarity
•Large number of inter-
correlated metrics can
be redundant and com-
plicated to apply in eflow
assessments
•Does not directly relate
ecological responses to
flow statistics
Mathews
and Richter
2007; Timpe
and Kaplan
2017
(Continued)
32

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Range of
variability
approach
(RVA) (Richter
et al 1997)
Quantifies
the change
in the range
of variation
of 33 IHA
parameters
from the
pre-impact
period to the
post-impact
period. Each
parameter is
categorised
into high,
medium or
low categor-
ies based on
user-defined
targets, and
a hydrologic
alteration
category is
calculated
based on
relative fre-
quency of
the RVA tar-
get range not
attained
Time-series of
daily stream-
flow data
Hydrologic
alteration
category for
each of the
33 paramet-
ers based on
the percent-
age of years
the RVA tar-
get range is
not attained,
expressed
as high,
medium and
low (with
hydrologic
alteration
values of
68%–100%,
34%–67%,
and 0%–
33% respect-
ively)
River reach
from which
hydrolo-
gical data
have been
gathered
•Relies on IHA parameters;
has a strong conceptual
foundation based on nat-
ural variability of ecosys-
tems
•Simple to measure when
flow data are available
•Indicates the extent of
deviation of the range
of natural variation for
33 IHA parameters; flow
management measures
can be tailored accord-
ingly
•Analysis supported by
an open-access software
developed by The Nature
Conservancy
•Can assess the individual
and cumulative impact of
reservoirs
•Useful for setting flow tar-
gets for regulated streams
•Can be adapted based on
ecological information
and monitoring data
•Uses the pre-development
range of natural variation
of IHA parameters as a
reference to determine
the extent to which flow
regimes have been altered
•Reliance on long-term
flow data limits its applic-
ation in data-deficit
regions
•Challenging to charac-
terize natural range of
variation when stream-
flow records pre-dating
human perturbation are
not available
•Applying 33 eflow targets
can be complicated
•Difficult to assess dams
with short post-dam
hydrology or no flow
gauges
•Cannot assess impacts
of proposed RIPs since it
relies on post-dam flow
data
•Extensive data processing
to account for data-gaps
•Ability to calculate cumu-
lative impacts depends
on the spatial configura-
tion of dams and gauging
stations
•Assumes stationarity
•Deciding on the measure
of dispersion and RVA
targets is subjective and
based on specific goals
•Does not consider the
periodicity or temporal
order of IHAs (only con-
siders the frequency of
each IHA)
Richter et al
1998; Mittal
et al 2014
(Continued)
33

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Degree of reg-
ulation (DOR)
(Lehner et al
2011)
Calculates
the pro-
portion
of a river’s
annual flow
that can be
withheld by
a reservoir or
a cluster of
reservoirs for
a river reach
Reservoir stor-
age capacities
and annual
discharge
A continu-
ous index of
proportions
River reach
to river net-
work
•Does not require flow data
or information on dam
operations; hence can be
applied in the most data-
deficit regions
•Input data of reservoir
storage capacities and
discharge can be estimated
even when not available
•Can be calculated easily
across spatiotemporal
scales, making it suitable
for iterative scenario ana-
lysis
•Can assess the individual
and cumulative impact of
reservoirs
•Various development
scenarios can be assessed
•Does not explicitly con-
sider the flow regime
•While high values cor-
respond to higher inter-
and intra-annual flow
alteration, low values do
not always correspond to
lower impacts. Low values
may be associated with
severe impacts on some
aspect of the flow regime
•Impacts may manifest
differently in differently
sized streams
•Does not represent impact
on biological patterns and
processes.
•Does not consider flow
alteration due to water
abstraction or river dewa-
tering
•Cannot be applied in
eflow assessments
Grill et al
2019
(Continued)
34

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Dundee
Hydrological
Regime Alter-
ation Method
(DHRAM)
(Black et al
2005)
Applies
the IHA
approach
to classify
the risk of
damage to
instream
ecology from
streamflow
alterations
using a five-
class scheme
compat-
ible with
the require-
ments of the
EC Water
Framework
Directive
Time-series of
daily mean flow
in un-impacted
and impacted
sites in relation
to any type of
anthropogenic
hydrological
impact
DHRAM
scores
(0–30) and
DHRAM
classes
between
1 (Un-
impacted
condi-
tion) and
5 (Severely
impacted
condition)
River reach
from which
hydrolo-
gical data
have been
gathered
•Indicates the degree to
which different flow com-
ponents are being affected;
mitigation measures can
be tailored to target res-
toration
•Where suitable hydrolo-
gical data are unavailable
or incomplete, synthetic
flow data can be generated
using approaches outlined
in the DHRAM manual
•Scoring of reaches per-
mits identification of sites
requiring further assess-
ments and conservation
efforts
•Analysis supported by
a Windows program,
WiDHRAM
•Can assess the individual
and cumulative impact of
reservoirs
•Reliance on long-term
flow data limits its applic-
ation in data-deficit
regions. Use of synthetic
data increases the risk of
errors, distorting DHRAM
results
•Difficult to assess dams
with short post-dam
hydrology or no flow
gauges
•Cannot assess impacts
of proposed RIPs since it
relies on post-dam flow
data
•Extensive data processing
to account for data-gaps
•Does not relate ecological
response to the flow stat-
istics
•For widespread status
assessments, repeated
DHRAM applications
on river reaches down-
stream of alterations will
be required
•Ability to calculate cumu-
lative impacts depends
on the spatial configura-
tion of dams and gauging
stations
•Assumes stationarity
Gao et al
2009
(Continued)
35

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Hydroecological
Integrity
Assessment
Process (HIAP)
(Henriksen et al
2006)
Uses a
Hydrolo-
gic Index
Tool to cal-
culate 171
streamflow
statistics and
a Hydrolo-
gic Assess-
ment Tool
to determine
the degree
of depar-
ture from
baseline con-
ditions
Time-series of
daily mean flow
and peak flow
data
171 biologic-
ally relevant
streamflow
statistics
for baseline
and altered
condition
River reach
from which
hydrolo-
gical data
have been
gathered
•Indicates the degree to
which different flow com-
ponents are being affected;
mitigation measures can
be tailored to target res-
toration
•Analysis supported by the
Hydrologic Index Tool
and Hydrologic Assess-
ment Tool
•Where suitable hydrolo-
gical data are unavailable
or incomplete, simulated
flow data can be used
•Can assess the individual
and cumulative impact of
reservoirs
•Useful for setting flow tar-
gets for regulated streams
•Reliance on long-term
flow data limits its applic-
ation in data-deficit
regions
•Designed to use USGS
mean daily and peak
flow discharges from the
National Water Informa-
tion System, and is hence
most suitable for Amer-
ican rivers
•Does not relate ecological
response to the flow stat-
istics
•Extensive data processing
to account for data-gaps
•Difficult to assess dams
with short post-dam
hydrology or no flow
gauges
•Cannot assess impacts
of proposed RIPs since it
relies on post-dam flow
data
•Ability to calculate cumu-
lative impacts depends
on the spatial configura-
tion of dams and gauging
stations
•Assumes stationarity
Kennen et al
2009
(Continued)
36

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Environmental
flow compon-
ents (EFC)
(Mathews and
Richter 2007)
Quantifies
changes in
34 flow stat-
istics organ-
ized within
five major
ecologically
important
flow com-
ponents:
low flows,
extreme low
flows, high
flow pulses,
small floods,
and large
floods.
Time-series of
daily stream-
flow data
Measures
of central
tendency
and disper-
sion for 34
environ-
mental flow
component
parameters
River reach
from which
hydrolo-
gical data
have been
gathered
•Complements the original
33 IHA parameters
•Based on ecologically
important flow paramet-
ers
•When pre-impact flow
data is available, can be
used in RVA analysis
•Strong conceptual found-
ation
•Quantitatively robust
•Simple indicators of flow
components allow for
quick calculation when
flow data are available
•Indicates the degree to
which different flow com-
ponents are being affected;
mitigation measures can
be tailored to target res-
toration
•Analysis supported by
an open-access desktop
software developed by The
Nature Conservancy
•Can assess the individual
and cumulative impact of
reservoirs
•Reliance on long-term
flow data limits its applic-
ation in data-deficit
regions
•Difficult to assess dams
with short post-dam
hydrology or no flow
gauges
•Cannot assess impacts
of proposed RIPs since it
relies on post-dam flow
data
•Extensive data processing
to account for data-gaps
•Ability to calculate cumu-
lative impacts depends
on the spatial configura-
tion of dams and gauging
stations
•Assumes stationarity
•Large number of inter-
correlated metrics can be
redundant and complic-
ated to apply in environ-
mental flow assessments
Morid et al
2019
(Continued)
37

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Overall degree
of hydrolo-
gic alteration
(Shiau and Wu
2007)
An index of
overall flow
regulation
based on the
integration
of individual
degree of
hydrologic
alteration
for each
of the 33
hydrologic
parameters
of the IHA
Time-series of
daily stream-
flow data
Percentage
indicating
overall flow
regulation
River reach
from which
hydrolo-
gical data
have been
gathered
•Based on IHA indicators
•Collapses the many inter-
correlated metrics of IHA
into a single value that
is easy to interpret and
analyse
•Easy to compute based on
the individual degree of
hydrologic alteration for
each of the 33 hydrologic
parameters of the IHA
•Can assess the individual
and cumulative impact of
reservoirs
•Reliance on long-term
flow data limits its applic-
ation in data-deficit
regions
•Difficult to assess dams
with short post-dam
hydrology or no flow
gauges
•Cannot assess impacts
of proposed RIPs since it
relies on post-dam flow
data
•Does not indicate the
degree to which differ-
ent flow components are
being affected
•Extensive data processing
to account for data-gaps
•Assumes stationarity
•Does not relate ecological
response to the flow stat-
istics
Shiau and
Wu 2007
(Continued)
38

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Ecodeficit/ecosurplus
concept (Vogel
et al 2007)
Nondimensional
metric,
based on
a flow
duration
curve
(FDC),
which
represents
the deficit
or surplus
streamflow
resulting
from flow
alteration,
as a fraction
of the mean
streamflow
in a typical
or median
year
Unimpacted
and impacted
FDCs (or
water resource
index dura-
tion curves)
for a period
of record or a
median annual
year
Quantification
of difference
in the net
volume
of water
available
to meet
instream
flow require-
ments
River reach
from which
hydrolo-
gical data
have been
gathered
•Less data intensive to
compute FDCs
•Can be computed over
any time period of interest
(month, season, or year)
and reflect the overall loss
or gain in streamflow due
to flow regulation during
that period
•Use of seasonal FDCs can
capture seasonal vari-
ations
•Graphical representation
of these metrics provides
an easily understood visu-
alization of changes to
flow conditions
•Water resource index dur-
ation curves can also be
used (Vogel and Fennessey
1995)
•Although FDCs represent
the historical frequency
of streamflow conditions,
they do not account for
the timing or duration of
flow events
•May not be able to capture
the life history require-
ments of target species
•Careful interpretation
of results required when
period of record FDCs are
used
•Does not relate ecological
response to the flow stat-
istics
•Generally, small values
of ecodeficit/ecosurplus
correspond to low values
of hydrologic alteration.
•Cannot assess the impact
of individual reservoirs on
river regulation
Gao et al
2009
(Continued)
39

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Flow-ecology
response
curves (part
of numerous
eflow assess-
ments such
as ELOHA,
DRIFT) (Poff
et al 2010)
Combines
hydrology,
channel
hydraul-
ics, ecology
and social
processes to
build mech-
anistic links
between
hydrology
and eco-
logy through
flow-ecology
response
curves based
on river
type.
Time-series
of flow data
to build the
‘hydrologic
foundation’
of baseline
and present-
day hydro-
graphs; ecolo-
gical data and
expert opin-
ion to create
flow-ecology
response curves
Flow-
ecology
response
curves for
classified
rivers across
a broad area
River reach
from which
hydrolo-
gical data
have been
gathered
•Accounts for ecological
response to flow alteration
•Factors in a social process
where e-flow goals can be
societally set
•Applicable across broader
scales
•Accounts for cumulative
effects of all water uses in
the catchment
•Can be continually
improved through mon-
itoring, validation, and
stakeholder feedback
•Suited for real-world
implications, as it allows
policy- and decision-
makers and stakeholders
to influence the outcome
while still being scientific-
ally rigorous
•Clear applications
•Relies on existing data and
can combine existing lit-
erature, expert knowledge,
and empirical data
•Relies on extensive and
synchronised hydrologic
and biological databases
•Accuracy of outputs
depend on accuracy of
curves correlating ecolo-
gical and flow conditions.
•Links between biotic and
abiotic factors are com-
plex and data is mostly
imperfect, causing uncer-
tainty
•Does not account for loss
of longitudinal or lateral
connectivity due to barri-
ers
•Science-derived but still
subjective given that
human stakeholders and
decision-makers ulti-
mately decide on targets
and which curve to use
Mcclain
et al 2014;
Cartwright
et al 2017;
Rosenfeld
2017
(Continued)
40

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
River Regu-
lation Index
(RRI) (Grill
et al 2014)
Quantifies
how strongly
a river may
be affected
by flow alter-
ations from
upstream
dams
Reservoir stor-
age capacities
and annual dis-
charge (meas-
ured or estim-
ated)
Continuous
index of
proportions
River basin •Does not require flow data
or information regarding
dam operations; hence
can be applied in most
data-deficit regions
•Input data of reservoir
storage capacities and
discharge can be estimated
even when not available
•Easily calculated across
scales, making it suitable
for iterative scenario ana-
lysis (including new dams
and future scenarios)
•Various development
scenarios can be assessed
•Does not explicitly con-
sider the flow regime
•While high values cor-
respond to higher inter-
and intra-annual flow
alteration, low values do
not always correspond to
lower impacts. Low values
may be associated with
severe impacts on some
aspect of the flow regime
•Impacts may manifest
differently in differently
sized streams
•Does not represent impact
on biological patterns and
processes.
•Does not consider effects
of water abstraction or
river dewatering
•Cannot be applied in
eflow assessments
Grill et al
2015
(Continued)
41

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Effective
Degree of
Regulation
(EDOR)
(Ehsani et al
2017)
Ratio of
volume of
water that
is displaced
(stored or
released) by
the oper-
ation of a
dam or a
cluster of
dams, to
the river’s
naturalized
flow without
dams
Reservoir stor-
age capacities
and annual dis-
charge (meas-
ured or estim-
ated) Reser-
voir operation
(volume of
water released
and stored)
Continuous
index of
proportions
River reach
to river net-
work
•Sensitive to changes in
reservoir operation
•Can be calculated at
monthly and annual time
scales
•Can assess the effect of
climate change on the
operation of dams
•Does not require flow data
•Easily calculated across
scales, making it suitable
for iterative scenario ana-
lysis
•Reliance on reservoir
operation information
limits its application
•Does not encapsulate
variability in flow com-
ponents
•While high values cor-
respond to higher inter-
and intra-annual flow
alteration, low values may
not correspond to lower
impacts. Low values may
be associated with severe
impacts on some aspect of
the flow regime
•Impacts may manifest
differently in differently
sized streams
•Does not represent impact
on biological patterns and
processes
Ehsani et al
2017
(Continued)
42

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Table 2. (Continued).
Flow altera-
tion metric Description
Data require-
ments Output
Spatial
scale of
application Advantages Disadvantages Applications
Statistical mod-
els of the coun-
terfactual (Valle
and Kaplan
2019)
Model-based
evaluation
of how post-
dam hydro-
logy differs
from ‘what
would have
happened’
in the
absence of
the impact
Time-series of
hourly, daily,
or monthly
flow or water
level data and
other relevant
hydroclimate
data (water
levels, flows,
precipitation,
etc)
Magnitude
and stat-
istical like-
lihood of
difference
between
each post-
impact
observa-
tion and the
expected
pre-impact
value
River reach
from which
hydrolo-
gical data
have been
gathered
•Avoids assumptions of
stationarity inherent to
‘before-after’ analyses
•Captures uncertainty
associated with data gaps
•Can identify statistic-
ally significant altera-
tion without a prescribed
period of post-impact
data
•Explicitly identifies post-
impact observations that
deviate from expected
behaviour; not restricted
to pre-conceived flow
metrics or statistics
•Requires hydroclimate
data to build models of
response variables (water
level or flow) in the pre-
impact period, which may
not be available
•Assume climate variables
are not impacted by dams
or their reservoirs
•Does not explicitly
account for land use/land
cover change
•Difficult to summarize as
a simple index
Valle and
Kaplan 2019
43

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Figure 3. Decision-making tree for selection of river connectivity metrics based on objective, data availability, and distribution of
infrastructure projects in the basin. Superscripts indicate barrier passability values to be binary (b) or continuous (c); The symbol
indicates method designed to assess connectivity for fish communities. Colours blue, black, and orange indicate structural,
potential, and actual connectivity metrics, respectively.
and resource requirements (especially directional-
ity of movement, dispersal distances, and migra-
tion) should influence metric selection. For example,
when assessing connectivity for diadromous species,
the DCI-d, Weighted RCI, ILRC, DEN connectivity
model, HCIUP, or the Metapopulation model of dir-
ected connectivity could be applied. Metric selection
should also be informed by the distribution of RIPs in
the study area. Some metrics, such as Fragmentation
classes, DCI-d, BC-k, and HCIUP, may not reflect any
change with the addition or removal of dams because
of the way they are defined. For example, when using
Fragmentation classes (Nilsson et al 2005), dammed
large tributaries are assigned a fragmentation score
of 2. This score remains the same irrespective of the
number of dams present. Similarly, when applying
the DCI-d (Cote et al 2009) at the scale of the river
network with binary dam passabilities, the addition
or removal of dams above the first barrier will not
alter the index value. Hence, these metrics should
only be used in specific instances where applicable.
Another consideration for river length-dependent
metrics is the presence of RIPs on seasonal headwater
streams. Often such dams lie beyond the delineated
stream network and are consequently excluded from
the analysis (as done in Hoenke et al 2014, Anderson
et al 2018). Hence, when numerous RIPs are situated
in headwater streams or when connectivity in head-
water reaches needs to be specifically assessed, these
metrics should be used with caution.
An important application of fragmentation met-
rics is the optimization of barrier removal or place-
ment to maximise connectivity for a target species
or taxa (Mckay et al 2017). The reliability and ecolo-
gical significance of the connectivity metric used in
these applications are crucial, and hence the use of
structural metrics should be avoided in these cases.
When more reliable metrics are unavailable due to
data limitations, all attempts should be made to val-
idate structural metrics with empirical field data and
determine their spatial scale of influence. Another
point of consideration is that structural and potential
44

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
metrics that rely on river network lengths are prone
to non-uniform change based on the extent of the
river network delineated, which itself is dependent on
the resolution of the base data, delineation techniques
and thresholds used (Zhou and Liu 2002, Murphy
et al 2008, Ariza-Villaverde, Jim´
enez-Hornero, and
Guti´
errez de Rav´
e2015, Kumar et al 2017). It is
important to note that these changes are an artefact
of changing river network lengths and do not signify
a change in actual connectivity.
4.2. Flow alteration metrics
A vast majority of research related to flow altera-
tion caused by RIPs is prescriptive and mostly aimed
at recommending environmental flows in regulated
streams (Hirji and Davis 2009, Poff et al 2010, Horne
2017). These approaches have been well studied and
reviewed in the scientific literature, but comparat-
ively far fewer descriptive measures of flow alteration
exist. Descriptive measures allow users to assess the
extent of alteration of a river’s natural flow regime in
response to various anthropogenic influences relative
to undisturbed baseline conditions. Since streamflow
is a master variable influencing water quality, phys-
ical habitat characteristics, ecosystem functions and
processes, and native biotic communities (Poff et al
1997), quantifying the extent of flow alteration has
important implications for basin-wide conservation
and development planning, and for setting suitable
environmental flow recommendations.
Our review documented 13 descriptive measures
of flow alteration. These methods vary in their data
requirements, spatial scale of application, and output,
each having their own assumptions, advantages and
disadvantages (table 2). Figure 4presents a decision-
making tree to help users select a suitable method to
assess flow alteration given the availability of stream-
flow, reservoir storage and discharge data, and spe-
cific objective. This decision tree, when used with
the information in table 2, can allow users to make
informed decisions about the types of flow alteration
measures that can be quantified in different contexts.
For example, when long-term observed or simulated
streamflow data are available, we identified 10 avail-
able methods to assess flow alteration. Of these, the
IHA, RVA, DHRAM, EFC, and HIAP quantify the
degree to which different flow components (i.e. flow
magnitude, frequency, duration, timing, and rate of
change) are affected. This contrasts with the APFD,
Overall Degree of Hydrologic Alteration and Ecodefi-
cit/Ecosurplus methods which quantify the extent of
flow alteration over a given time scale. Flow-ecology
response curves and statistical models of the coun-
terfactual can be used to assess both the alteration
to various flow components and overall flow over a
period of time (figure 4).
When long-term streamflow data are unavailable,
as is the case in numerous developing countries wit-
nessing a surge in dam development, we identified
only three possible methods- DOR, RRI, and EDOR-
that require data on reservoir storage capacities and
annual discharge. Though these metrics are useful
in data-deficit regions and are easy to calculate, they
provide no insight on how various components of
the flow regime are affected over time. They also do
not consider the impacts of water abstraction (except
for EDOR), diversion, and dewatering of river chan-
nels. This is especially problematic if the study area
has numerous small or low-head dams with little
or no reservoir storage. Furthermore, the impacts
of flow regulation as measured by these methods
can manifest differently in differently sized streams,
despite having the same numerical values. To this
end, reach-scale classification through characterist-
ics such as geomorphic features may be useful to
relate ecological relationships in regions with defi-
cient streamflow records (Poff et al 2010). Complex
alphanumeric classification methods (e.g. Rosgen
1994) may prove overly cumbersome to relate chan-
nel features to ecological systems (Simon et al 2007);
simpler geomorphic classifications that describe vari-
ations in processes such as sediment mobility and
stream power (Montgomery and Buffington 1997,
Poff et al 2006) can explain ecological relationships
where streamflow alteration cannot be assessed. Such
classification methods could be useful for relating
effects of river discharge to ungauged streams.
All but one of the above methods utilise stream-
flow, reservoir storage and/or discharge data to
calculate various flow statistics without relating
to an ecological response. If users need a metric
that links flow alteration to an ecological response,
the flow-ecology response curve is a versatile
approach that can be used to assess the extent of
change to one or more flow components or overall
flow in relation to ecological responses of interest
(Poff et al 2010).
Overall, the efficacy of all the connectivity and
flow alteration metrics listed above will be greatly
influenced by spatiotemporal extent and resolution of
input data (Murphy et al 2008, Yang et al 2014, Woo-
drow et al 2016), uncertainties or errors associated
with modelled or simulated data (Bond and Kennard
2017), and compatibility of scale of response and scale
of analysis (Gaucherel 2007, Mahlum et al 2014).
While no single method can be all-encompassing,
the selection of appropriate connectivity and flow
alteration metrics should be carefully made based on
the study objective, data availability, and a thorough
knowledge of the assumptions, advantages, and dis-
advantages of the available methods.
5. Applications and directions for furture
research
The methodological advancements in characteriz-
ing river fragmentation and flow alteration described
above provide a wide variety of tools for researchers
45

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
Figure 4. Decision-making tree for selection of flow alteration metric to be used based on data availability. Temporal scale of
streamflow data required: sd =sub-daily, d =daily, m =monthly. # Incorporates ecological data of interest to build flow-ecology
curves
and resource managers to understand the effects of
RIPs on river ecosystems. A range of these metrics can
be effectively applied to guide monitoring and adapt-
ive management programs aimed at maximising riv-
erine and ecological connectivity and restoring or
maintaining the natural flow regime under various
scenarios of existing and proposed RIP development.
They can also be applied to identify priority reaches
for the implementation of mitigation measures, and
aid in the creation of basin-wide conservation and
development plans not only after but also before pro-
jects are implemented. The growth and utility of such
tools have coincided with widely available resources
to facilitate analysis: increasing access to GIS and
computational capabilities (such as FIPEX (Fisheries
and Oceans Canada 2011), FIDIMO (Radinger et al
2014)), online repositories of dams (such as GRAND
(Lehner et al 2011), GOODD (Mulligan, van Soesber-
gen, and S´
aenz 2020), FHReD (Zarfl et al 2015), spa-
tial datasets of hydrologic networks (such as Hydro-
SHEDS (Lehner et al 2008), HydroBASINS (Lehner
and Grill 2013) and streamflow data (GSCD (Beck
et al 2013); GRDC (http://grdc.bafg.de), FLO1K (Bar-
barossa et al 2018); RiverATLAS (Linke et al 2019))
has made several fragmentation and flow altera-
tion indices more readily applicable across larger
spatial scales. Despite these advances, there remain
numerous areas for further research to improve
the performances of these metrics, especially given
their applications in basin-wide conservation and
development planning. These are briefly discussed
below.
5.1. Relationships among metrics
Although river connectivity and flow alteration char-
acterize two different types of variables, because
flows control hydrologic connectivity, the two vari-
ables often interact and influence one another (Grill
et al 2014). For example, dam-induced flow alter-
ations can result in reduced wetted channel widths
and/or depths, which can affect lateral and vertical
connectivity (Junk et al 1989, Wiens 2002). Water
abstraction and diversions can create dewatered river
stretches which impede water-mediated longitud-
inal connectivity (Deitch, Kondolf, and Merenlender
2009). Large reservoirs can significantly alter thermal
regimes, which can further act as a thermal barrier
to various organisms (Caudill et al 2013).While most
measures of connectivity focus on the longitudinal
dimension, far fewer metrics are aimed at assessing
lateral and vertical connectivity. Additionally, since
river connectivity is water-mediated, the force and
direction of flow exerts a strong influence on eco-
logical connectivity and ecosystem processes such
as transport of sediment, nutrients, and organisms
with limited or no mobility (Fullerton et al 2010).
Hence, connectivity measures that do not account
for flow can be misleading in terms of ecological
46

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
connectivity. In order to address these issues, future
research should be aimed at developing methods that
(a) measure the interactions between connectivity
and flow alteration and metrics within each category,
(b) measure lateral and vertical connectivity, and (c)
incorporate the effects of flow within connectivity
metrics,. Understanding relationships among con-
nectivity and flow alteration metrics can provide
additional insights regarding the effects of RIPs on
stream ecosystems over space and time. From a man-
agement perspective, connectivity metrics could be
combined with flow alteration metrics to inform pre-
scriptive tools for maintaining environmental flows.
In regions where time or resources are limited, rela-
tionships between metrics that require extensive data
collection (such as actual connectivity or flow alter-
ation methods that require streamflow data) and
metrics that do not (such as structural connectiv-
ity indices and flow alteration methods that do not
require streamflow data) may be useful for extrapol-
ating actual connectivity more broadly in a region,
or for understanding conditions where metrics
diverge.
5.2. Ecological significance
The actual ecological relevance of most flow alter-
ation and connectivity metrics remains largely
unknown. This is especially true for structural con-
nectivity indices that are gaining rapid popularity
and widespread implementation (Perkin et al 2015,
Anderson et al 2018). Despite this knowledge gap,
numerous assessments and prescriptive documents
use connectivity indices to prioritize barrier removal,
under the assumption that an increase in connectivity
(as defined by a particular index) will improve biotic
communities (Bourne et al 2011, Perkin et al 2015).
Similarly, the ecological relevance of most flow alter-
ation indices has not been adequately studied. Since
ecological responses are expected to be influenced
not only by connectivity and flow alteration met-
rics, but also by other environmental factors and the
behaviour and resource requirements of the target
species or taxa, assessing these relationships across
river classes (Dallaire et al 2019) become essential.
Hence, rigorous field studies that quantify the associ-
ation between these metrics and biotic communities
(such as fish (Perkin and Gido 2012, Mahlum et al
2014), macroinvertebrates (Solans and Jal´
on 2016),
and riparian vegetation (Mcmanamay et al 2013))
and/or ecosystem processes and functions (such as
sediment transport and primary productivity(Yarnell
et al 2015)) are an important area for further research.
Such empirical studies can not only inform the eco-
logical relevance of connectivity and flow alteration
measures, but can also shed light on how behavi-
oural components influence ecological connectiv-
ity across spatial and temporal scale (Fullerton et al
2010).
5.3. Spatial and temporal scales of application
The ecological utility of a connectivity or flow alter-
ation index will depend its spatiotemporal scale of
application and the species, assemblage or ecosystem
process being considered (Crooks and Sanjayan 2006,
Gaucherel 2007, Llaus`
as and Nogu´
e2012). Since dif-
ferent species perceive habitats at different spatial
scales across their life-history stages, their response
to fragmentation and flow alteration will likely be
scale-dependent, and also influenced by their hab-
itat and resource requirements (Rossi and van Halder
2010, Llaus`
as and Joan 2012). Generally, as spa-
tial scales of analysis increases, other confounding
landscape-level variables (such as elevation, land use,
discharge) begin to influence response communities
(Mahlum et al 2014). The application of spatial graph
and network models across hierarchical river net-
works presents an opportunity to better understand
factors influencing ecological connectivity across spa-
tial scales (Er˝
os and Lowe 2019). Similarly, due to
temporal shifts in streamflow, ecosystem processes,
and species life-history stages, ecological connectivity
and flow alteration need to be assessed over adequate
temporal (or seasonal) scales based on the ecological
response being considered to avoid misrepresentation
of results (Fullerton et al 2010). While it may not
be feasible to quantify connectivity across all spati-
otemporal scales, it is essential that further research
be aimed at identifying the range of scales over which
connectivity and flow alteration metrics may influ-
ence populations or processes of interest (Fullerton
et al 2010).
5.4. Applications in data-scarce regions
One of the greatest challenges in understanding the
effect of RIPs on connectivity and flow alteration
is the effective application of informative indices in
data-scarce regions. Most tropical developing coun-
tries striving to recognise their hydropower potential
are characterised by high levels of freshwater biod-
iversity and the presence of river-dependent local
communities (Auerbach et al 2016). These regions are
also often limited in terms of long-term hydrologic
and ecological data availability. Hence, despite there
being a strong need for science-based management
and decision-making, the lack of available resources
precludes effective assessments of existing and pro-
posed RIPs across spatiotemporal scales. The devel-
opment of ecologically meaningful measures of con-
nectivity and flow alteration that can be applied in
such data-deficit regions to aid monitoring, restora-
tion, and conservation development efforts remains a
vital research frontier. Additionally, concerted efforts
to establish partnerships and collaborations between
governments, project proponents, scientists, water-
managers and NGOs can go a long way in improv-
ing hydrologic data availability, which can then aid
in informing water management policy and decision-
making. Similar collaborations to establish a network
47

Environ. Res. Lett. 15 (2020) 123009 S Jumani et al
of gaging stations and collect periodic data on river
habitat variables and biotic communities can provide
the foundation required to apply more sophisticated
and informative methods to assess the impacts of RIPs
and create basin-wide monitoring and conservation
plans (Horne 2017).
Conclusion
Continued demand for non-fossil fuel-based energy
and water supply to meet the needs of growing human
populations and zero-emission power will likely con-
tribute to increasing reliance on RIPs through the
21st century. While the impacts of these projects on
aquatic, riparian, and terrestrial ecosystems may be
profound, tools to evaluate or predict the effects of
RIPs on river ecosystems can provide critical inform-
ation for conservation and management to mitigate
their impacts in the future. Resource managers across
the globe, over a wide range of technical capacities,
need to understand the tools that are available for
analysing how RIPs alter connectivity and streamflow.
To this end, decision support remains one of the most
important contributions that hydrologists and ecolo-
gists can make to sustain aquatic ecosystems.
Our review highlights the substantial progress
toward understanding the hydrological consequences
of RIPs, yet significant gaps remain. The recent pro-
liferation of research using remotely sensed met-
rics to evaluate river network fragmentation and
flow alteration highlights the potential for remote
sensing to support applications including comparis-
ons across broad regions and predictions of future
impacts, but it also underscores their limitations.
Without organism-based, field-based data collection,
the ecological meaning of such metrics is unsup-
ported. Assessments of actual ecological impacts will
require extensive measurement of factors such as
presence and absence (and changes over time), move-
ment, and dispersal of organisms under a range of
conditions. Such studies may be complex and expens-
ive and require multi-year study relative to remote
sensing studies, but they are a necessary step for con-
servation and sustainability of aquatic ecosystems in
the future.
Data availability statement
No new data were created or analysed in this study.
Acknowledgments
We gratefully thank Mr Aldo Farah-P´
erez and
Mr Siddarth Machado for their invaluable sugges-
tions on the manuscript. We express our sincere
gratitude to the editor and two anonymous review-
ers for their comments and suggestions, which have
greatly improved the quality of this manuscript. We
also acknowledge the United States Department of
Agriculture Hatch Grant No. FLA-WFC-005577 and
the University of Florida Soil and Water Sciences
Department for their support to enable open-access
publication.
ORCID iD
Suman Jumani https://orcid.org/0000-0002-2292-
7996
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