ELOHA synthesizes existing hydrologic and ecological databases from many rivers within a region to generate flow alteration-ecological response relationships (or flow-ecology relationships, for short) for different types of rivers. These relationships correlate measures of ecological condition, which can be difficult to manage directly, to streamflow conditions, which can be managed through water-use strategies and policies.  Detailed site-specific hydrologic and biological information need not be obtained for each individual river.  As the flow chart shows, flow-ecology relationships connect the hydrologic, ecological, and social processes of ELOHA.

 
Within any river type, there are numerous rivers, and the flow in each of those rivers has likely been altered to some degree.  Flow-ecology relationships are determined by associating the extent of flow alteration with consequent changes in ecological condition for each river. A family of such relationships is developed for each river type, using a variety of flow statistics and ecological variables.

As indicated on the ELOHA flow chart, the process of developing flow-ecology relationships begins by formulating hypotheses.  Starting with hypotheses ensures that flow-ecology relationships are mechanistic and not simply empirical, and subsequent data compilation is systematic.  Haney et al (2008) describe a collaborative expert process used to hypothesize flow-ecology relationships for the Verde River in Arizona, USA.  Similarly, the flow template that is guiding environmental flow management in the Fraser River catchment of Colorado, USA, is largely based on consensus-driven flow-ecology hypotheses.

The next step is to assemble existing ecological and related data to quantify the relationships.  Ecological data used to develop the flow-ecology relationships - for example, aquatic invertebrate species richness, riparian vegetation flow response guilds (Merritt et al 2010), or life-history traits of fish - ideally are sensitive to existing or proposed flow alterations, can be validated with monitoring data, and are valued by society.  Poff et al (2009) list several examples.

For some river types, ample ecological data are available.  In most places, however, data are scarce.  In places with limited data, scientists are nonetheless advancing ELOHA through modeling and statistical analysis. 

Modeling of fish community response to flow alteration provides the ecological foundation of Michigan's Water Withdrawal Assessment Tool (WWAT), which determines whether proposed ground-water and surface-water withdrawals will adversely impact fish populations.  A conceptual flow-ecology response curve is shown below; actual curves incorporated into the Tool are presented in MichiganGroundwater Conservation Advisory Council (2007).

Statistical approaches help scientists interpret hydro-ecological data. Webb et al (2010) demonstrate the use of Bayesian hierarchical models to detect ecological responses to flow variation in data-poor situations such as large-scale, disperse environmental flow monitoring programs.

Because biota respond to many factors in addition to hydrology, streamflow may not be the sole explanatory variable but, instead, acts as a limiting factor on biota.  Limiting factors can be analyzed using quantile regression (Cade and Noon 2003). 

This figure (below) from Konrad et al (2008) shows how maximum annual daily streamflow (x-axis) limits relative richness of intolerant invertebrate species in streams across the western United States.  According to the graph, hydrologic alteration can reduce the magnitude of floods (e.g., by river regulation) or increase their magnitude (e.g., by urban development), either of which could have ecological consequences.   

Wilding and Poff (in review) used quantile regression to quantify relationships between streamflow conditions and riparian vegetation, fish, and aquatic macroinvertebrates in three types of streams in cases where ecosystem complexities precluded a simple monotonic response. 

Flow alteration-ecological response relationships require information about streamflow characteristics expressed in terms of alteration of specific flow statistics.   But in most cases, flow alteration has not been analyzed in the development of flow-ecology relations.  Instead, the biological response to alteration can be inferred from the difference in biological condition associated with difference in streamflow represented by hydrologic alteration (e.g., Wilding and Poff, 2008).  In the figure above (from Konrad et al, 2008), note that by using the magnitude of floods relative to mean streamflow, different sized streams could be analyzed together.

All stakeholders need to understand the process and uncertainties involved in developing the flow alteration-ecological response relationships that will be used as the basis for implementing policies.

Useful articles and reports on flow-ecology relationships:

Armanini et al (2010) developed an index for assessing ecological impacts of hydrologic alteration, based on the sensitivity of macroinvertebrates to river flow.  Intended as a tool for improving river management and restoration efforts, the Canadian Ecological Flow Index (CEFI) is an easily calculated metric that can be applied in many places across Canada without requiring collection of new data.

Armstrong et al (2010) determined relations between fish-community characteristics and anthropogenic alteration, including flow alteration and impervious cover, relative to the effects of physical basin and land-cover (environmental) characteristics. Fish data were obtained for 756 fish-sampling sites from the Massachusetts Division of Fisheries and Wildlife fish-community database.

Dewson et al (2007) found that leaf breakdown and primary production were not good indicators of flow alteration, but coarse particulate organic matter retention did correlate well with streamflow reduction in small New Zealand streams.

Freeman and Marcinek (2006) investigated fish assemblage responses to water withdrawals and water supply reservoirs in Piedmont streams in Georgia, USA.  Although never intended for this purpose, the state of Connecticut used these research results as the basis for its streamflow standard.

 RESOURCES 

ELOHA flow chart in:
    Spanish
    English

Journal Articles,
Reports, and Symposia 

North American Benthological Society 2009 Special Session on  Developing Flow-Ecology Response Relations to Support Regional Streamflow Management  Part I, Part II

Armanini et al (2010) Benthic invertebrates, Canada

Dawson et al (2007) Coarse organic matter retention, New Zealand           

Haney et al (2008)  Riparian ecosystem function, Arizona, USA

Freeman and Marcinek (2006) Fish assemblage, Georgia, USA

Kennen and Ayers (2002) Fish, invertebrates and algae, New Jersey, USA

Kennen et al (2007) Aquatic invertebrate assemblage, New Jersey, USA

Kennen et al (2010) Aquatic macroinvertebrate assemblage, northeastern USA.

Kennen and Riskin (2010) Fish and aquatic invertebrate assemblage, New Jersey, USA.

Konrad et al (2008) Benthic invertebrate assemblage, western USA

Kanno and Voukoun (2010) Fish assemblage, Connecticut, USA

Merritt and Poff (2010) Cottonwood and tamarisk abundance and recruitment, SW USA

Propst et al (2008) Fish assemblage, New Mexico

Rehn (2008) Benthic macroinvertebrates, California

Royet al (2005) Fish assemblage, Georgia, USA

Rypel et al (2009) Freshwater mussels and baldcypress trees, southeatern USA

Stromberg et al (2007) Riparian vegetation, southwestern USA

Taylor et al (2008) Fish assemblage, southeastern USA

Van Sickel et al (2006) Fish and invertebrates, Oregon, USA

Webb et al (2009) Salinity and smelt, Victoria, Australia

Wilding and Poff (2008) Fish, inverts, veg, Colorado, USA

Zorn et al (in review) Fish assemblage, Michigan, USA

More
Flow-Ecology Relationships 

Riparian vegetation flow response guilds (Dave Merritt, USDA-FS)

Life-history traits of fish in the upper Missouri River basin (Valerie Kelly, USGS)

Flow template for the Fraser River basin, Colorado (John Sanderson, TNC)

Fish community structure in the lower Flint River basin, Georgia (James Peterson, USGS)

Haney et al (2008) used a collaborative expert workshop to develop flow-ecology hypotheses for a ground-water dependent desert river in Arizona, USA.

Kanno and Vokoun (2010) evaluated the ecological effects of water withdrawals and impoundments on fish assemblage structure using electric fishing data collected at 33 wadeable streams in Connecticut, USA.

Kennen and Ayers (2002) examined population data for 43 fish species, 170 invertebrates species, and 103 algae species in their analysis of urbanization effects on aquatic health in New Jersey, USA

Kennen et al (2007) used non-metric multidimensional scaling (NMS) to evaluate variation in aquatic-invertebrate assemblage structure and built a series of multiple linear regression (MLR) models that identify the most important environmental and hydrologic variables driving the differences in aquatic-invertebrate assemblages across a disturbance gradient.

Kennen et al (2010) related streamflow patterns to aquatic macroinvertebrate assemblages in 67 small-to-medium sized (15-526 km2) upland streams in the northeastern United States, and found negative affects of hydrologic alteration on biotic integrity.  Mean April flow and duration of high flows were particularly indicative of assemblage variability.

Konrad et al (2008) used a nonparametric screening procedure to identify different forms of streamflow-invertebrate associations for streams across the western United States.  Selected ceiling and floors that represent conditional responses of invertebrates to streamflow were analyzed using quantile regression.  "Although this approach cannot distinguish the effects of streamflow on invertebrates from those of confounding factors that are correlated with streamflow".....it "is an assessment of the potential for a particular type streamflow pattern, such as frequency of high flows, to limit a characteristic of benthic invertebrate assemblages."

Michigan Groundwater Conservation Advisory Council (2007) and Zorn et al (in review) explain how relationships between fish community assemblage and median August streamflow reduction were developed, vetted, used to set environmental flow standards, and incorporated into Michigan's Water Withdrawal Assessment Tool (WWAT).

Propst et al (2008) describe how the natural flooding regime in a desert river was a primary factor in shaping fish assemblages. Figure 6 indicates an expected ecological response to increased diversions from the Gila River basin, New Mexico, USA

Rehn (2008) examined benthic macroinvertebrates as indicators of biological condition below hydropower dams on west slope Sierra Nevada streams, California, USA.  Figure 5 shows the relationship between IBI scores and constancy/predictability index and May low flows.

Roy et al (2008) investigated the relationship between hydrologic alteration and fish assemblage in urbanizing streams in the Etowah River catchment, Georgia, USA.  Overall, hydrologic variables explained 22 to 66% of the variation in fish assemblage richness and abundance.

Stromberg et al (2007) examined the influence of high and low streamflow durations; flood frequency; depth, magnitude, and rate of ground-water decline; and other hydrologic conditions on phreatic vegetation along rivers in the arid southwestern United States. 

Taylor et al (2008) analyzed changes to flow regime and fish assemblage after construction of the Tennessee-Tombigbee Waterway in northeastern Mississippi, USA, based on contemporary comparisons to historical fish collections and discharge data.

Van Sickel et al (2006) used statistical methods to estimate both flow and biological conditions (fish and invertebrate status) for second- to fourth-order streams throughout the Willamette River basin in Oregon, USA.

Wilding and Poff (2008) mined diverse data from 149 sources, including journal articles, technical reports and theses, to quantify relationships between streamflow conditions and riparian vegetation, fish, and aquatic macroinvertebrates for the three types of streams that exist in Colorado, USA.  Comparison of measured ecosystem parameters across a range of flow conditions (varying levels of modification) allowed patterns to emerge that provided a basis for quantifying ecosystem response.  Where ecosystem complexities precluded a simple monotonic response to flow change, the best-available flow-ecology relationship was inferred as the ceiling of the scattered data, as defined by quantile regression (Cade and Noon 2003). The flow-ecology relationships are embedded in Colorado's new Watershed Flow Evaluation Tool (WFET)