Center for Urban Environmental StudiesNortheastern University, Boston, MA 02115

TECHNICAL REPORT NO. 1

LINKING POLLUTION TO WATER BODY INTEGRITY

Literature Review

Vladimir Novotny, Ph.D., P.E.Primary Investigator

Project sponsored by Grant No. R83-0885-010 to Northeastern University from the USEPA/NSF/USDA STAR Watershed Program

Bernice L. SmithEPA Program Manager

Boston, MAMay 2004

Acknowledgments

The research contained in the technical report was partially sponsored by the US EnvironmentalProtection Agency STAR Watershed Program by Grant No.R83-08875-010 to NortheasternUniversity. The author greatly appreciates this support. The findings and conclusions contained inthis report are those of the author and not of the funding agencies nor the STAR program.

The author would like to acknowledge Dr. Laurel Schaider and Ms. Jessica Brooks for their editingand finalizing the report.

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Table of ContentsTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Chapter 1

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1History of U.S. Water Pollution Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Non-Point Source (Diffuse) Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Chapter 2

IMPACT OF DIFFUSE POLLUTION ON INTEGRITY OF WATERS . . . . . . . . . . . . . . . . . . . 5Watershed Integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Expressing Integrity of Aquatic Ecosystems – Endpoints . . . . . . . . . . . . . . . . . . . . . . . . . 5

Chapter 3

RELATING ENDPOINTS TO HUMAN STRESSES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Human Effects on Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Simplistic Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Chapter 4

ECOSYSTEM HIERARCHICAL SPATIAL AND TEMPORAL SCALES . . . . . . . . . . . . . . . 17Ecosystem Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17River Continuum Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Ecosystem Temporal Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Expressing Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Buffering Capacity of a System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Biotic Responses to Time-Variable Stresses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Substratum (Benthos) Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Human Impacts on Substrate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Chapter 5

MODELING ECOSYSTEMS WITH DYNAMIC MULTIVARIATE APPROACHES . . . . . . 27Applying Multivariate Models to Ecological Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 27Multi-layer Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Selection of endpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30Development of the Model Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Chapter 6

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MODEL BUILDING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Selection of Submodels (Functional Links) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Model structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Layer I - Assessment Endpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Layer II - Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

I. Water Column Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40II. Sediment Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44III. Habitat Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45IV. Fragmentation Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Layer III - In-stream Exposure Stressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Layer IV - Landscape Stressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Organization of landscape descriptors in watershed vulnerability classification schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Relating diffuse pollution to water body integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Chapter 7

WATERSHED VULNERABILITY INDICATORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Vulnerability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Index of Watershed Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Chapter 8

RISK PROPAGATION FROM STRESSORS TO ASSESSMENT ENDPOINTS – A MAZE OF PROBABILITIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Linking Stressors to Biotic Endpoints – Risk Propagation Model . . . . . . . . . . . . . . . . . 67Interactions Among Stressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70Uncertainty and Its Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Parsimony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

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List of Figures

Figure 1.1 Watershed, land use changes and other impacts on water quality . . . . . . . . . . . . 2Figure 2.1 Principal factors and components that comprise the integrity of surface waters . . 6Figure 3.1 Behavior of IBI metrics along a stressor gradient . . . . . . . . . . . . . . . . . . . . . . . . . 9Figure 3.2 Relationship between macroinvertebrate IBI and percent imperviousness . . . . . 10Figure 3.3 Scatter plot of IBI scores vs. percentage of urban land use . . . . . . . . . . . . . . . . . 13Figure 3.4 IBI as a function of % urbanization and % urban riparian forest . . . . . . . . . . . . 14Figure 4.1 Stream ecosystem cross-section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Figure 4.2 The River Continuum Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Figure 4.3 Dissolved oxygen variations in three Wisconsin creeks. . . . . . . . . . . . . . . . . . . 22Figure 4.4 Cumulative probability distribution of annual DO distributions . . . . . . . . . . . . 24Figure 5.1 Concept of a multivariate/multimetric ecological model . . . . . . . . . . . . . . . . . . 28Figure 5.2 Conceptual model of the primary external stressors and internal structure of

the integrity of stream aquatic biota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Figure 5.3 Concept of the stressor-risk-end point propagation model . . . . . . . . . . . . . . . . . 33Figure 5.4 Concept of the links between stressors, exposure, and response . . . . . . . . . . . . . 34Figure 6.1 Schematic of the multilayer risk propagation model . . . . . . . . . . . . . . . . . . . . . . 36Figure 6.2 Algae population shifts with temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Figure 6.3 Plot of a water quality parameter and assessment of probability of compliance

with its water quality standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Figure 6.4 Ecological risk assessment for stormwater impacts . . . . . . . . . . . . . . . . . . . . . . 42Figure 6.5 Acute values for determination of risk of copper to aquatic biota . . . . . . . . . . . . 43Figure 6.6 Dominant fish assemblages related to stream morphology . . . . . . . . . . . . . . . . . 46Figure 6.7 Flow, depth and recurrence interval of flows for natural stable channels . . . . . . 46Figure 6.8 Relationship between the Ohio QHEI and the RBP HQ index . . . . . . . . . . . . . . 50Figure 6.9 Performance of Ohio and USEPA habitat indices . . . . . . . . . . . . . . . . . . . . . . . . 51Figure 6.10 Fish IBI for modified streams in Northern Illinois . . . . . . . . . . . . . . . . . . . . . . . 52Figure 6.11 Dams on streams in the New England coastal basin . . . . . . . . . . . . . . . . . . . . . . 55Figure 6.12 Simulated sediment unit loads (MEUL) from residential land uses related to the

total imperviousness of the area and pervious surfaces covered by lawns . . . . . 59Figure 7.1 Overall vulnerability of watersheds in the U.S. . . . . . . . . . . . . . . . . . . . . . . . . . . 65Figure 8.1 Patterns of Maximum Species Richness lines . . . . . . . . . . . . . . . . . . . . . . . . . . . 68Figure 8.2 Risk estimation for the mayfly taxa of the macroinvertebrate ICI by clay in

substrate parameter for Southeastern Wisconsin streams . . . . . . . . . . . . . . . . . . 68Figure 8.3 Effect of a single stressor on ICI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70Figure 8.4 Observed ICI versus ICI predicted by the final layered regression model . . . . . 70Figure 8.5 Standard deviation of IBI measurements as a function of IBI values . . . . . . . . . 72

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List of TablesTable 3.1 Surrogate parameters for pollution used in simple ecological biotic

integrity statistical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Table 4.1 Time-variable ecological stresses and their impacts . . . . . . . . . . . . . . . . . . . . . . 22Table 5.1 Characteristics of good assessment endpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Table 6.1 Metrics used in assessment of fish communities . . . . . . . . . . . . . . . . . . . . . . . . . 38Table 6.2 Metrics of the Index of Biological Integrity for Benthic Macroinvertebrates . . . 38Table 6.3 Metrics of the Ohio Invertebrate Community Index (ICI) . . . . . . . . . . . . . . . . . 38Table 6.4 Description of habitat parameters used in the Rapid Bioassessment Protocol . . 49Table 6.5 Physical habitat attributes of the Ohio Qualitative Habitat Evaluation Index . . . 49Table 6.6 Landscape parameters and factors affecting the integrity of surface waters . . . . 58Table 6.7 Land uses and major associated pollutant types . . . . . . . . . . . . . . . . . . . . . . . . . 60Table 7.1 Index of Watershed Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

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CHAPTER 1

INTRODUCTIONHistory of U.S. Water Pollution Control

More than fifty years ago Aldo Leopold, a pioneer of land and watershed conservation, wrote aparadigm for watershed protection and conservation (Leopold, 2001):

A thing is right when it tends to preserve the integrity, stability, and beauty of thebiotic community. It is wrong when it tends to do otherwise.

A modified version of this ethical standard can be found in the Clean Water Act (CWA), whosemajor goal is restoring and maintaining the chemical, physical, and biological integrity of theNation’s waters. Section 5 of the CWA also defined pollution as anything that downgrades theintegrity of the water body. Such downgrades can be caused by discharge of pollutants from various(point and diffuse) sources, by habitat degradation due to a change of hydrology, by introductionof foreign species and by other human actions. Leopold also extended the rule of environmentalethics as:

The land ethic simply enlarges the boundaries of the community to include soils,waters, plants, and animals, or collectively: the land.

Thus, the notion of land extends to the general ecological terrestrial system. This system includesinteractions between human and nonhuman biotic system. This connection is important to diffusepollution abatement because it means that land and water are intertwined in the general ecologicalsystem and both must be protected, preserved and, if damaged, restored to their best use. Leopold,however, realized that “a land ethic of course cannot prevent the alteration, management, and useof these bv‘resources,’ but it does affirm their right to continued existence, and, at least in spots,their continued existence in a natural state.”

Throughout the last century, water pollution remediation efforts focused on the water body itself.If the water body had been polluted, then the focus was on reducing or eliminating the dischargesof pollutants first at the point of discharge to the water body. These actions involved primarilycontrol of point sources by building wastewater collection and treatment systems. The objectiveof point source abatement was improvement of water quality expressed by chemical parameterssuch as dissolved oxygen, BOD, ammonium, or suspended solids. During the last quarter of the20th century, starting with the passage of Water Pollution Control Act Amendments (Clean WaterAct) in 1972 and the international effort to clean up Great Lakes, abatement of non-point sourcesof pollution became a part of the picture. The creators of Clean Water Act recognized, in Section208 of the Act, that pollution control efforts must be conducted on a watershed scale. During the1970s many watershed-wide plans were prepared by states that included considerations of bothpoint and non-point source pollution, and all public wastewater treatment plants proposed in 1970shad to be included in Section 208 plans. Regarding, non-point source pollution, the methodologiesof assessment and abatement were in their infancy and very few non-point source pollution controls

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Figure 1.1 Watershed, land use changes and other impacts on water quality (adapted fromNovotny, 2003)

were realized as a result of Section 208 plans. By the end of the last century, non-point pollutionwas recognized to be responsible for more than half of the remaining water quality problems.

Non-Point Source (Diffuse) Pollution

Today, the focus of pollution abatement and water body recovery has shifted to a more holisticview. Following Leopold’s paradigm, a water body and its watershed are part of the same systemand streams and rivers reflect the landscape they drain (Hynes, 1975; Poff and Ward, 1990). Thespatial relationship of any lotic ecosystem can be lateral (channel - riparian - floodplain),longitudinal (lower order stream to higher order stream, upstream to downstream), and vertical(groundwater- surface water interactions), the relative importance of which vary in both space andtime (Ward, 1989; Poff and Ward, 1990). Hydrologically, pollution can enter surface waters byoverland/small channels flow, from atmospheric deposition and by discharge of groundwater fromthe shallow aquifer. Human action can change the proportions and pathways of diffuse pollutionand water inputs in the streams. Lotic ecosystems are watershed dependent.

As a result of watersheds being used and developed by humans, pollution is being generated thatdowngrades the integrity of the water bodies. Pollution is then transported from the source to the

river overland or via shallow groundwater aquifers (Novotny, 2003). The water bodies are

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themselves used for many purposes including aquatic life protection and propagation, contact andnon-contact recreation, water supply, navigation, power production, flood conveyance, andwastewater disposal. Overuse of water resources and overuse or misuse of land and land useconversion throughout the watershed generate pollution that, along with point sources directlydischarging into the water body, impairs the integrity and diminishes beneficial uses of the waterbody (Figure 1.1). Definitions of non-point source pollution with examples were included inNovotny and Olem (1994) and Novotny (2003).

Diffuse pollution, the most pervasive type of pollution, is difficult to manage and control. Diffusepollution can be local, regional and transboundary. Recent definitions of diffuse pollution haveattempted to overcome the ambiguities of previous legal definitions of point and non-point sourcesin the Clean Water Act, in which “everything else” defines non-point sources (Novotny, 2003).The diffuse pollution category includes the truly non-point source contamination (such as seepageof nitrate from agricultural land into underlying groundwater, pollution from farm fields andsilviculture, or atmospheric deposition), together with the large number individually minor pointsources such as forestry channels, field drains from farmland and urban surface runoff outfalls thatcollectively deliver significant contamination to the aquatic environment. Mobilization and deliveryof pollutants are often dependent on weather conditions and may be influenced by soil type andsurface cover. Simplistically, diffuse pollution sources may be individually minor but collectivelysignificant, distributed in a diffuse manner throughout the watershed. Diffuse pollution is thereforeassociated with many dispersed sources, but there are often aggregations of pollution sourceswithin a catchment and hierarchies of risks can often be constructed.

Diffuse pollution should be differentiated from natural loads of chemicals from dissolution of soilminerals, natural erosion or natural content of precipitation. The term pollution has a broadermeaning embedded into the CWA, meaning an impairment of integrity caused by humans. In abroader meaning, this term “pollution,” as defined in the CWA, includes: excessive loads ofpollutants from point and diffuse sources (sediment, nutrients, biodegradable organics, toxins,heat); physically adverse alterations of the water body integrity such as channel lining andstraightening and impoundment, cutting down trees lining the water body, loss of riparian habitat,drainage of riparian wetlands; and hydrologic modifications in the watershed that increase flow ortemperature magnitudes and variability. Although the latter cases do not include discharges ofpollutants, if they are widespread, they can be considered to be diffuse pollution.

The Black, Adriatic and North Seas, Chesapeake Bay and Gulf of Mexico are examples of largewater bodies affected by transboundary (interstate in the US), sub-global inputs of diffuse pollution.These large water bodies have one symptom in common - they suffer from excessive inputs ofnutrients from farming operations and cities located hundreds to thousands of kilometers upstream.These nutrient loads are delivered by large tributaries including the Danube and Don Rivers for theBlack Sea, the Po River for the Adriatic Sea, the Susquehanna and Potomac Rivers for theChesapeake Bay, and the Mississippi River for the Gulf of Mexico.

In developing countries, increasing population and resulting migration are leading to megacitiesthat have poorly functioning or non-existent sewerage systems, placing severe strains on local waterbodies. Furthermore, deforestation of subtropical and tropical forests is a severe diffuse pollutionproblem. One root cause is population increases that drive impoverished populations to the practice

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of slash and burn agriculture, sometimes subsidized by governments. Deforestation is also causedby the demand for cheap wood at a price that does not include the cost of damage to the forest andthe environment. Population and economic pressures in developing countries lead to intensive andunsustainable agriculture resulting in excessive soil losses.

However, before diffuse pollution becomes a global or large scale regional problem affecting seas,it is a local problem affecting small rivers and lakes. It is manifested by a loss of use and resourcevalue of local surface water bodies and groundwater aquifers. At the end of the last century in theUS, more than 50% of receiving water bodies were not meeting their water quality goals. An evenmore severe situation can be found in other countries. Because past cleanup efforts focusedprimarily on point sources rather than on diffuse pollution, both aquatic life and human health areaffected in the present. Many aquifers and drinking water reservoirs have been contaminated bynitrates and surface waters by algae and trihalomethane precursors. Recreation opportunities onrural streams that fifty years ago exhibited good water quality have diminished because of diffuseagricultural pollution. In addition, on a local scale in and around major urban areas, metals andother toxic substances are major contamination issues, especially in sediments. Some problems areattributable to past discharges that have been either reduced or discontinued but remain a legacyissue in sediments and contaminated soils of flood plains and watersheds. Such cases include PCBcontamination of sediments.

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CHAPTER 2

IMPACT OF DIFFUSE POLLUTION ON INTEGRITYOF WATERS

Watershed Integrity

The ecological status or “health” of the water body, called “integrity,” has been defined as theability of the water body ecological system to support and maintain “a balanced integrated, adaptivecommunity or organisms having a species composition, diversity and functional organismscomparable to that of natural biota of the region” (Karr et al., 1986). Recently, the term “integrity”has been applied to water bodies that are minimally impacted by human activities while the term“health” is reserved for conditions that are desired by humans but are not necessarily natural (Karr,1996). In many areas, human activities have radically altered the landscape and the aquaticecosystem, such that an attainment of the pre-disturbance ecologic conditions of the watershed andthe water body is impossible (Committee, 2001). Establishing the ecological potential of the waterbody while considering irreversible and reversible changes in the watershed is the goal of theEuropean Water Framework Directive (WFD) and also of the US watershed management programsrequired by the Clean Water Act (CWA).

There are multiple root causes of damages to the ecological status of surface and groundwaterresources (impairment of integrity) and their diminished uses for humans (Figure 2.1). While non-point loads of pollutants from the watershed and direct point source discharges are major causesof damage, another major cause is habitat degradation by stream modification and change of landssurrounding the water body. These stressors create a risk or a probability that aquatic speciesindigenous to the water body will disappear. At the same time, the stressors also may causeincreased risk to public health by people eating contaminated fish, drinking contaminated water andcontracting gastrointestinal disease after using the water body for swimming and other contactrecreation. The ultimate result is the degradation of the aquatic ecological system exhibited as thedisappearance of species of organisms that would otherwise thrive in the unimpacted water bodyand a loss or impairment of the beneficial uses of the water body for humans.

Expressing Integrity of Aquatic Ecosystems - Endpoints

Environmental indicators are categorized as stressor, exposure, and response indicators (Yoder andRankin, 1999; Yoder et al., 2000).

• Stressor indicators include activities that add external (allochthonous) loads that impact butmay or may not degrade the integrity of the receiving water body or the watershed.Stressors include point and non-point loadings (including atmospheric deposition), land usechanges, stream modification, and other large scale influences that generally result fromanthropogenic activities. A disruptive stressor that can cause a damage or an adverse changeof integrity is called a hazard (Hunsaker et al., 1990). Source terms imply qualitative and

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Figure 2.1 Principal factors and components that comprise theintegrity of surface waters (adapted from Karr et al.,1986)

quantitative descriptions of the stressor. If these stressors are distributed over the watershedand are not identifiable point sources they could be classified as extended diffuse pollution.

• Exposure indicators include chemical parameters, whole effluent toxicity, tissue residues,sediment contamination, habitat degradation and other parameter values that result in a riskto the resident biota. A risk is a numeric value assigned to an exposure stressor thatexpresses a probability that the population sizes and diversity of the resident organisms willbe degraded and some organisms will be lost from the system, due to either acute or chronictoxicity effects or to habitat degradation.

• Response indicators are the direct measures of the ecological status (integrity) of the waterbody. Another term used in the literature is biotic or assessment endpoint because the biota(including humans) represent the highest level of effects caused by the propagation ofstresses throughout the ecosystem.

Endpoints are environmental entities that are exposed to the stresses or hazards. Suter (1990)characterized endpoints as formal expressions of the actual environmental value that is to beprotected or improved. The output of the assessment and modeling effort is a probability that theendpoint will be improved or impaired or remain steady. Reference environments or systems aresystems of a similar character to the investigated system with the least human impact.

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Ecosystem endpoints are numerous. In earlier water quality studies when protecting the health offish was the primary goal, the endpoint was the concentration of dissolved oxygen (DO) becauseit was known that fish kills were observed if the DO concentration dropped below some thresholdvalue (e.g., 3 mg/L). To protect the well-being of fish and also to incorporate a margin of safety,the water quality standard was set at 5 mg/L with some variations considering presence or absenceof juvenile fish or cold-water and warm-water fish species or spawning and migratory routes(USEPA, 1986). However, in 1972, the Clean Water Act defined the integrity in three dimensions:physical, chemical, and biological. Thus, the endpoint indicator must also express the integrity thathas these three dimensions:

• Physical integrity implies habitat conditions of the water body that would support abalanced biological community.

• Chemical integrity is the chemical composition of water and sediments that would not beinjurious to the aquatic biota (and to humans).

• Biological integrity describes a composition of aquatic organisms that is balanced andresembles or approaches that of unaffected similar water bodies in the same ecoregionwithout invasive species.

Therefore, in the true meaning of the law, an integrity endpoint has three parts (Novotny et al.,1997):

• Physical habitat evaluation

• Chemical evaluation using chemical standard and chemical risk calculations, toxicitybioassays

• Biotic evaluation using standards for pathogens, and biotic indices

For aquatic life use considerations, the community and population response parameters representedby the indices of biotic integrity are considered the principal response indicators. Based on themultidimensional concept of integrity introduced by Karr et al. (1986), shown in Figure 2,“integrity” of a water body can be simplified to three dimensions: physical (habitat), whichincludes flow, hydrology and habitat structure parameters, chemical (water and sedimentcomposition, including temperature), and biological parameters. Indices of Biotic Integrity (IBIs)for assessing this three dimensional integrity have been developed and implemented (Barbour etal., 1997, 1999) in the US, originally in the Midwest, but the use has spread all over the NorthAmerica. In the US, both fish and macroinvertebrate community composition and habitatassessment indices and criteria are used in addition to chemical assessment and criteria/standards(Novotny et al., 1997). A macroinvertebrate index originally proposed in Europe (Kolkwitz andMarson, 1908) has almost a 100 year tradition. Similar to the US, almost every country in Europeuses some kind of biotic index for assessment of the quality (integrity) status of the water body(e.g., Sláde…ek, 1979;Wright et al., 1988; Hughes and Oberdorff, 1999). Extensive reviews of theIBIs’ concepts and uses have been published in Simon (1999) and Davis and Simon (1997).

A human connection was added to Figure 2.1 to indicate the human component of the integrity.Humans are adversely affected by degraded quality of the water resource because they may eatcontaminated fish, drink water drawn from the resource, or be affected by ingestion or skin

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exposure during contact recreation. Humans are also a cause of degradation of integrity bygenerating pollution. As human impacts increase, the health of the ecosystem decreases, changingto a “sick” ecosystem dominated by a few tolerant species that may develop to unsustainablenumbers. The endpoint of the maximum human influence is an ecosystem without life. Based onLeopold (2001) and the Clean Water Act paradigms, such a state is not acceptable. On the otherhand, many water bodies irreversibly impacted by pollution cannot be fully returned to theirpredevelopment health. Thus, a measure of what is acceptable for these irreversibly modified andimpacted streams must be developed. Karr (1996) and Karr and Chu (1999) define two criteria thatwould set the threshold for whether a loss of species is acceptable. First, the human activity shouldnot adversely alter the long term sustainability of the resource to provide goods (e.g., fish) andservices (e.g., recreation, water supply). Second, human uses should not degrade off-site areas, i.e.,the floodplain or landscape of the watershed, that would adversely and irreversibly affect the waterbody to a point that a balanced and sustainable aquatic community cannot be maintained.

Both “water quality” and “integrity” may have different meanings to different users of the waterbody. For example, water supply industries and even agencies may not be concerned with bioticintegrity of the water body as long as chemical parameters are suitable for water supply or can beadjusted by treatment. Irrigators may worry about salt content and several major chemicalparameters. However, a “healthy” ecology of the water body is a necessary prerequisite for mostdirect human (drinking, contact recreation) and aquatic life uses. The biotic integrity indices reflectlong term natural and anthropogenic impacts and are in a state of equilibrium with allochthonousand autochthonous stresses and the state of the watershed.

Chemical integrity assessment has some drawbacks. First, it is not possible to evaluate allchemicals and their synergetic effects on biota. Second, most sampling and monitoring programsare not continuous; in fact, samples are taken and analyzed infrequently and, typically, not duringthe periods of the greatest stress. However, standards developed for chemicals are related to the riskthat sensitive species will be adversely affected and could disappear from the ecosystem. Chemicalstandards adequately protect the most sensitive species. Biotic criteria, on the other hand, reflectthe long term effects of all stresses, but the causative stressors and factors are difficult andsometimes impossible to determine without chemical and physical assessment. Thus, all threecategories of assessment must be conducted. US EPA requires an independent applicability of thethree categories of assessment; i.e, if any one category of assessment indicates impairment, thenthe overall integrity is considered to be impaired (USEPA, 1994). Others argue (see Novotny, 1994;Novotny et al., 1997) that biotic integrity evaluation is more important, because if the bioticevaluations document that the composition of species resembles the non-impacted referencecondition despite some prior violations of chemical standards, the integrity status is attained andthe chemical standard may be overprotective for the indigenous biotic population in the water body.

The Index of Biotic Integrity as defined by Karr et al. (1986) consists of a numerical evaluation ofthe number and tolerance of fish species in a pre-specified reach of a stream. The fish IBI includesevaluation of numbers, composition, tolerance and species health, including disease, erosion,lesions, and tumors. Other indices use macroinvertebrate organisms (Sláde…ek, 1979; Hilsenhoff,1987; Wright et al., 1988). The indices of benthic macroinvertebrate integrity have a similarcomposition.

9

Figure 3.1 Behavior of IBI metrics along a stressor gradient (after Yoder,2002)

CHAPTER 3

RELATING ENDPOINTS TO HUMAN STRESSES

Human Effects on Communities

The composition, diversity and density of organisms are related to various stressors. Most previousresearch related the IBI metrics to a single dominant stressor. Figure 3.1 shows the concept of theeffect of human-induced stress on the metrics of the IBI. The human disturbance impacts on varioustaxa and metrics of the Index of Biotic Integrity were extensively presented and discussed by Karrand Chu (1999). Figure 3.2 shows the relationship of the IBI to the most widely-used surrogatestressor parameter, the percent imperviousness of the watershed. The concept is simple anddefensible. With the increased stress, sensitive (intolerant) native species will be replaced bytolerant species that in the undisturbed systems were either present in smaller numbers due topredation and competition or were not present at all (invasive species). Typically, a healthy systemwill have a large number and diversity of species but smaller number of individuals within eachspecies. A stressed system will have a smaller number of species dominated by those most tolerantto the stress that may develop in larger mass and numbers. At higher levels of stress, the health ofall organisms becomes affected and even the tolerant organisms may disappear from the system,resulting ultimately in a system without life. The Index of Biotic Integrity (Karr et al., 1986; Plafkinet al., 1989; Barbour et al., 1997, 1999) expresses numerically this sequence.

10

Figure 3.2 Relationship between macroinvertebrate IBI metric topercent imperviousness of the watershed (from Schuelerand Galli, 1992; Schueler, 1994)

Finding the relationship of a biotic endpoint to hundreds of stressors in the water body, in theatmosphere and throughout the watershed may not be as complex as mapping the DNA sequencebut is far from simple. The endpoints are complex assemblages of metrics that were selected to bestrepresent the three groups of aquatic organisms: fish, benthic macroinvertebrates and periphyton.Only about a dozen species or less, out of possible thousands, were included in the evaluation ofIBIs. These organisms respond to their immediate stresses such as lack of food, exposure to toxicchemicals, elevated temperature and lack of adequate habitats. The organisms do not directlyrespond to stresses in the watershed, such as diffuse pollution, or in the atmosphere, such as acidrain or PCBs. These stresses are transmitted through various pathways, modified and attenuated.The stresses may be long-term (steady), transient or random.

In some cases, one or a few stresses may appear to dominate; however, looking for a simplisticrelationship may, in some cases, illustrate the effect, but it may provide neither the answer nor aremedy for the problem. Nevertheless, there is a large number of articles in the literature that focuson the simplistic relationships of the indices of biotic integrity to one or several parameters andidentifying these dominant parameters is useful.

Simplistic Relationships

Figure 3.2 is an oversimplified but widely-published relation of IBI of macroinvertebrates to thepercent imperviousness of the watersheds located in the Washington, DC metropolitan area. Percentimperviousness is a surrogate for many “bad” impacts caused by urbanization and development(Field et al., 2000). A nearly identical plot of benthic IBI vs. impervious area was published byKleindl (1995) for lowland streams in Puget Sound, Washington, and replotted in Karr and Chu

11

(1999). Similar plots have been developed using percent urbanization or population density(Dreher, 1997) and other surrogate landscape parameters (Table 3.1). Wang et al. (2000, 2001)evaluated the effect of changes from agriculture to urban use and analyzed and published negativeeffects of % impervious area on the fish IBI that were even more profound than the effects on thebenthic IBI mentioned above. Wang et al. (2001) then found that the “connected impervious area,”i.e., impervious urban area directly connected to the concentrated surface flow drainage conduit(e.g., storm sewer), yielded the best correlation to the fish index of biotic integrity of urban andurbanizing watersheds. The authors concluded that most of the studies listed above and in Table3.1 have noted a sharp decline in fish community integrity attributes at 8% to 12% imperviousness.

In theory, one could postulate that these surrogates could also be substitutes for the level of diffusepollution. However, it is becoming evident that such oversimplifications can cause more harm thanbenefit to the understanding of the cause-effect relationship of pollution on the integrity ofreceiving waters. The percent imperviousness parameter is irreversible in most cases. To bring thisrelationship to an absurd conclusion, one could argue that every watershed with more than 8% to12% imperviousness is degraded, therefore all urban development should consist of low-density,scattered subdivisions and no other remedies should be considered, except removing the imperviousareas.

Investigations by Yoder et al. (2000) in Ohio, shown in Figure 3.3, effectively dispute the notionof a simple relationship between biotic indices and a surrogate stressor such as imperviousness orsome other land use parameter. Karr and Chu (1999) observed similar results. Yoder et al. (2000)analyzed data from small urban watersheds (<125 km2) in Ohio. Small watersheds are particularlysusceptible to degradation by urbanization. The Ohio program of using biotic integritydeterminations and application to water quality management is described in Yoder and Smith(1999). Ohio uses the fish assemblage Index of Biotic Integrity (IBI) and Invertebrate CommunityIndex (ICI).

Figure 3.3 shows that determining a simple relationship for the Ohio urban areas (similar to Figure3.1) using a standard regression analysis was not possible. The authors qualify the relationship byidentifying other stressors they found significant such as habitat degradation, wastewater and CSOinputs and legacy pollution in sediments. When the data points were separated by identifying therelationship for each individual urban area (Cincinnati, Cleveland/Akron, Columbus, Dayton,Toledo, and Youngstown), four urban areas showed a detectable decreasing IBI relationship withthe logarithm of percent urbanization and the two remaining areas did not. However, even for theindividual urban areas that have shown a decreasing trend of fish IBI with increased urbanization,the correlation was relatively poor.

As pointed out by Karr and Chu (1999), using simple stressor relationships may have some valuein regional studies and this relation may be used in GIS-based analyses and modeling of the effectsof diffuse pollution and other stresses on the biotic integrity expressed by IBIs or similar integrativeindices. Percent imperviousness and percent urbanization parameters have many generic diffusepollution impacts, most of them being correctable (see Novotny, 2003), such as:

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Table 3.1 Surrogate parameters for pollution (landscape and chemical) used in simpleecological biotic integrity statistical models

Type of DependentVariable Y= IBI = f(X1, X2...)

Independentvariable, X1

Independentvariable, X2-n

Authors

Macroinvertebrate % impervious area Schueler and Galli (1994)Kleindl (1995)

Fish % impervious area Wang et al. (2000)

Fish % connectedimpervious area

% agricultural land% woodland% wetland

Wang et al. (2001)

Fish % urban land cover Wang et al. (1997)Yoder et al. (2000)

house density Benke et al. (1981)

Macroinvertebrate human populationdensity

Jones and Clark (1987)Dreher (1997)

Fish Macroinvertebrate

width of forested urbanriparian corridor

% urban land use Steedman (1988)May et al. (1997)

Fish % agricultural landwithin 30 metersriparian zone along theentire upstreamnetwork

2) % agriculturalland within 30meters immediatelyadjacent3) % agriculturalover the entireupstream basin

Van Sickle (2003)

Macroinvertebrate degree of recreationalactivity

Patterson (1996)

Fish pollutantconcentrations

Karr et al. (1985a)Thorne and Williams (1997)

Macroinvertebratesaprobien (orHilsenhoff) index

organic pollution(BOD)

Sláde…ek and Tu…ek (1974)Hilsenhoff (1987)

Macroinvertebrate taxain unpolluted streams(British index)

physical variablesX1 - distance from sourceX2 - mean substrateparticle size

Chemical variablesX3 - nitrate+nitrite NX4 - alkalinityX5 - chloride

Moss et al. (1987)

The 5 variables were reducedfrom original 28 withoutlosing reliability of predictions

Macroinvertebrate five land-coverparameters

Physical channelconditions(substrate texture)

Morley and Karr (2002)Puget Sound Basin

13

Figure 3.3 Scatter plot of IBI scores vs. percentage of urban landuse upstream from the IBI monitoring site for 267small (<125 km2) watersheds in Ohio (from Yoder etal., 2000).EWH = exceptional habitat criterion, WWH = warmwater habitat criterion for IBIs in Ohio.

• Imperviousness changes the hydrology of a watershed by increasing the surface runoff(polluted) flow and decreasing groundwater recharge and inputs of (less polluted orunpolluted) groundwater base flows into the receiving waters (correctable by implementingstorage and infiltration).

• Urbanization increases variability of flows and water quality parameters, including salinityand temperature (correctable by implementing storage).

• Increased variability of urban runoff and magnitude of high flows makes flooding morefrequent and causes bank instability and erosion. As a result, sediment loads increase andbank habitats are adversely impacted (correctable by implementing storage and infiltration).

• Urban runoff is more polluted with toxic compounds such as metals, PAHs, and cyanidesin the winter in snow-belt areas (correctable by implementing source controls andtreatment).

• Urbanization is also related to point source loads of pollutants, urban erosion fromconstruction sites that increase sediment and pollutant loads, combined sewer overflows,and loss of flow to satisfy various urban uses and water transfers (correctable byimplementing appropriate best management practices).

• Due to increased high flow and development pressures, urbanization results in diminishedriparian zones and stream modifications making them more constricted and faster flowing,including channel lining, straightening and, ultimately, covering (correctable by streamrestoration).

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Figure 3.4 Qualitative bi-variate IBIregression model for IBI as afunction of % urbanization and% retention of urban riparianforest (from Steedman, 1988)

Thus, in addition to the overall surrogate stressor, expressed by percent imperviousness or percenturbanization, other stressors may be significant, including excess flow variability, which can bereduced by application of best management practices. Obviously, for non-urban streams landscapefeatures such as percent forested or agricultural area of the watershed (Wang et al., 2000; VanSickle, 2003), riparian zone conditions and buffers, geology of the watershed and morphology ofthe stream, ecoregional attributes (Omernik, 1987; Omernik and Gallant, 1989) or hydrologicstressors such as flow variability (Poff and Ward, 1989) are important. The other surrogates ofstressors such as agricultural or forest land become important as the dominating effect ofurbanization diminishes at low percentages of imperviousness.

Karr and Chu (1999) point out several other factors that express human influence on bioticintegrity, beyond just static landscape imperviousness. Specifically, they focus on land use changes.In most cases, diverse human activities during the land use changes (e.g., urbanization) interact toaffect conditions in watersheds, water bodies or stream reaches and it is the gradient and type ofchange that are important. Removal of natural riparian vegetation has an effect, but replacing it witha vegetative bank protection has less impact than reinforcing banks with riprap or stone or concreteembankments. Impounding the stream also has significant effects. These changes, and theirgradient, to land and stream use should then be linked to the pollutant inputs, for which dischargesof toxic pollutants may have greater effect than the discharges containing nutrients or domesticeffluents. Thus, pollutant levels and gradients are very important and are “original” stressors(Sláde…ek and Tu…ek, 1974; Krenkel and Novotny, 1980; Mason, 1991; Thorne and Williams,1997). Alternatively, streams can be categorized according to disturbance categories such asrecreation impact on mountain streams (Patterson, 1996).

Figure 3.4 presents a contour plot of a two-variate simple regression model incorporating percenturban land use and percent riparian forest.

The recent work of Park et al. (2002) is the most comprehensive multivariate analysis and modeldevelopment. It linked 34 environmental variables to the macroinvertebrate Shannon diversityindex (SH) and species richness (SR). The data were collected at 664 sites on 23 different watertypes in the Netherlands. The water bodies included springs, canals, streams, ditches, lakes andpools. The researchers used the counter propagation Neural Network Model (CPN) (Hecht-Nielsen

15

1990); however, in the research they used only feed-forward feature of the model withoutcouterflow. The model consists of supervised and unsupervised learning algorithms that classifythe inputs and predict output values. It is a layered model consisting of input, output and one ormore internal layers. This approach is very close to that proposed in the NortheasternUniversity/University of Wisconsin research.

The 34 environmental variables used in building the model were:

Percentage cover emergent vegetation Percentage cover floating vegetationPercentage cover floating algae Percentage sampled habitat: emergent vegetationPercentage sampled habitat: detritus Percentage sampled habitat: floating vegetationPercentage sampled habitat: gravel Percentage sampled habitat: clayPercentage sampled habitat: bank Percentage sampled habitat: submerged vegetationPercentage sampled habitat: silt Percentage sampled habitat: stonesPercentage sampled habitat: peat Percentage sampled habitat: sandDissolved oxygen percent saturation Percentage cover by bank vegetationPercentage cover by submerged vegetation Percentage cover by all vegetationStream width Width/depth ratioCalcium ChlorideDepth Silt thicknessElectric conductivity AmmoniumNitrate Oxygen concentrationOrtho-phosphate AcidityFlow velocity Water temperatureTotal phosphate Slope

The output of the model fitted the SH and SR measured values well with a high accuracy ofprediction (r > 0.90 and 0.67 for learning and testing process, respectively).

In developing the model, the input data, both environmental variables and biological attributes,were proportionally scaled from 0 to 1 in the range of the minimum and maximum values. Beforescaling data, the environmental variables were transformed by natural logarithms to reduce skeweddistributions.

16

17

CHAPTER 4

ECOSYSTEM HIERARCHICAL SPATIAL ANDTEMPORAL SCALES

Ecosystem Hierarchy

Poff and Ward (1990) categorize hierarchically the lotic aquatic systems as (a) watersheds, (b)stream segments, (c) pool-riffle systems and (d) microhabitat systems. The subsequent discussionand model development will deal with the upper hierarchy systems.

Watersheds, or stream systems, unify an entire drainage system and represent the highest level inthe hierarchy. Watershed characteristics reflect the landscape/morphologic history as affected bygeological, tectonic and long-term climatic factors. The scale of change is on the order of thousandsof years. Human impacts and interactions affecting watersheds occur more quickly; however, theyalso can date back one hundred to one thousand years. The earliest deforestation periods by humanscan be attributed to Romans (two thousand years ago), Mayans (one thousand years ago) andVenetians (five hundred years ago). Rapid urbanization with significant watershed impacts datesback to the industrial revolution 150 to 200 years ago, but has accelerated in the last forty yearswith the onset of widespread automobile use and building of freeways.

The watershed-receiving water body system has the following components that should beconsidered when estimating the watershed loads and the receiving water quality and their impacton integrity (Novotny, 2003):

• Surface flow component that contributes surface runoff, sediments and pollutants to thereceiving water body.

• Top soil components that store most of the contaminants and may contribute on occasionto interflow loads to the receiving water body.

• Shallow aquifer or subsurface zones that contribute groundwater (base) flow to thereceiving water body.

• Impervious surfaces that contribute to fast surface flow (in rural areas such surfaces wouldinclude roads, farmsteads and feedlots or roads in forested areas)

Stream segment systems contain the receiving water body, its in-situ deposits and water and theriparian corridor. The segments are bounded by major discontinuities such as a tributary, changeof slope, bank materials, human modifications (i.e., low head dam or drop structure), substratumcharacter, riparian canopy or floodplain riparian characteristics.

The channel and its corridor are a part of the same riverine ecosystem. The areas outside the mainchannel but inside of the corridor, called riparian zones, play an important role in the ecologicalhealth (integrity) of the aquatic system. The riparian zones contain wetlands and oxbow lakes that

18

Figure 4.1 Stream ecosystem cross-section.

are primarily abandoned channels, meadows and riparian forests. This system is created by thehydrogeological action of previous floods and sometimes tectonic forces over geologicaltimescales. In urban areas the riparian zones are modified by development and flood controlmeasures that sometimes may eliminate the riparian environment. The length of stream segmentsare in hundreds of meters. Laterally, the stream segment cross-section is shown in Figure 4.1. Theextent of the floodplain to the 100 year level is more or less arbitrary; however, it is well-established by flood control regulations and insurance. The actual width of the stream corridor isgiven by natural landscape features such as bluffs and terraces or manmade dikes.

Pool-riffle systems are characterized by breaks in the water surface slope and bed topography andare exemplified by depth and velocity patterns. The pool-riffle structure is restricted to the baseflow channel - the thalweg. In larger deeper streams, the pool-riffle structure is replaced by a run-bend structure. These features of the stream are important parts of the habitat and feeding/spawning activities of the aquatic biota and their numeric evaluations have been included in thehabitat quality indices (Plafkin et al., 1989; Barbour et al., 1997, 1999). Channel modifications andimpounding changes these important habitat features or even completely eliminate them.

Microhabitat systems are components of the pool-riffle structure that are similar in substrate texture(coarser substrate in the riffle, finer in the pool), water depth (deeper in the pool) and velocity(faster in the riffle). The scale of these features in meters. These subscale features have lessrelevance in the watershed classification and the developing of models relating watershed landscapeand pollution characteristics to the integrity of the receiving water body.

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River Continuum Concept

Poff and Ward (1990) emphasize that the physical hierarchy is important for lotic systems wheremost organisms are in contact with the substratum for at least some time during their life-span.Habitat change at any higher level of the hierarchy has a cascading impact on all subordinate levels.The level of interaction with the bottom (benthic) layers changes with the morphological order ofthe stream. As the river becomes larger, the habitat and biotic composition changes. The River(Watershed) Continuum Concept advanced in the US by the Federal Interagency Task Force(1998), adapted from Vannote et al. (1980), is an attempt to generalize and explain longitudinalchanges in stream ecosystems (Figure 4.2). The concept proposes a relationship between the streamsize and order and progressive shift in structure and functional attributes.

The conceptual model helps identify the connections to generalize the watersheds, floodplain, andstream system. The concept also describes how the biological community and water quality developand change from headwater areas to the river mouth. The Continuum Concept hypothesis assumesthat many first to third order headwater streams are shaded by riparian forest canopy. The shadinglimits the growth of algae, periphyton, and other aquatic plants. Since energy cannot be createdthrough photosynthesis, the aquatic community in the stream is dependent on allochthonousmaterials (material from outside the channel such as leaves, twigs, and other organic debris)brought in from the surrounding watershed and riparian zones. Biological communities in thestreams are uniquely adapted to the use of externally-derived organic inputs and have, for example,macroinvertebrate communities dominate with shredders and collectors. As one proceedsdownstream to fourth, fifth, and six order streams, the channel widens, which increases availablelight and, consequently, primary production. The stream begins to become more dependent onautochthonous materials (materials originating from inside the channel). In these downstreamsections, species richness of the biological community increases as the ecological system adaptsto using both allochthonous and autochthonous food sources.

In large streams of seventh and to twelfth order, there is a trend toward increased physical stability,but also a significant shift in structure and biological function as well as water quality. Large riversdevelop increased reliance on primary production by phytoplankton. These river sections receivelarge inputs of dissolved and ultra-fine particles from upstream. The River Continuum Concept isimportant when interpreting the biotic composition and water quality of rivers. Several bioticindices were developed to characterize the ecological health of the stream (see Plafkin et al., 1989;Barbour et al., 1997, 1999; Novotny, 2003). However, these indices and classification systems werecalibrated using the ecological (biotic and morphological/habitat) condition of small, wadeable,lower-order streams.

20

Figure 4.2 The River Continuum Concept (Federal Interagency TaskForce (1998), adapted from Vannote et al. (1980))

21

Ecosystem Temporal Scale

Temporal variability is also an important factor. Variability of ecosystems is inherent and,generally, variability can be broken down (Bendat and Piersol, 1971) to:

• Pulse or step. An isolated and infrequent significant change of the parameter value caused,for example, by a spill or an abrupt transient change of a stressor or the system. A pulse isa short duration change that returns in a short time to its pre-disturbance level. A step is apermanent relatively sudden change (e.g., fast irreversible deforestation, implementationof best management practices or treatment to control pollution inputs, impounding a river)

• Trend. A long-term change of a parameter or ecosystem characteristic, either ascending ordescending. Global warming is apparently a trend effect.

• Periodicity. A cyclic oscillation of a parameter with different and often multiple frequenciesand magnitudes. Periodic oscillation of the water body ecosystem can be multiannual (e.g.,El Niño meteorological changes, Hurst phenomenon for meteorological patterns and streamflows, see Hurst, 1951; Klemes, 1974), annual (flow, temperature, dissolved oxygen, diffusepollution loads from the watershed), weekly (pollutant loads from urban areas), or diurnal(temperature, dissolved oxygen).

• Random fluctuations. Random fluctuations also can be related to the rate of change orduration of the parameter (system characteristic) magnitude. Random fluctuation can becharacterized as wide bends (slow and fast variations) or narrow bends (primarily slowfrequency variations). Causes of random fluctuations are numerous.

Table 4.1 presents examples of time-variable ecological stresses and their impacts. None of thecomponents of the temporal variations can be exactly predicted. Even annual variations vary fromyear to year, although their probabilistic predictability is better than that for random fluctuations.

Random pulse or step events can sometimes be predicted (e.g., impact of treatment) but often areunpredictable. Characteristics of random fluctuations can be revealed from the past data but thefuture fluctuation cannot be exactly predicted. Together, these components form a stochastic timeseries and the system they reflect is a stochastic system that can only be described in probabilisticterms. Depending on the relative importance of the components in the series, the systems can rangefrom partially predictable to unpredictable; however, even unpredictable random systems, whererandom components predominate, can be characterized in probabilistic terms (e.g., mean andprobability ranges and probability distribution of the values of the parameter). The cyclic, trend andrandom fluctuations (wide or narrow band) can be quantitatively ascertained by time series analysisusing, for example, Autoregressive-Moving Average (ARMA) modeling (Box and Jenkins, 1976)or, for complex interactive time series, by Artificial Neural Network or Genetic Algorithm models.

22

Table 4.1 Examples of time-variable ecological stresses and their impacts

Type of stress Ecological impact

Pulse Toxic spill or sewer overflow Flash flood Algal crush (sudden die off)

Acute toxicity, die-off of sensitive organismsScour of bottom habitat, flushing of organismsDO depletion

Step Treatment process shut down or start up Cutting down riparian tree cover

Change of pollutant loadsChange of habitat, increase of temperature

Cyclic Flow, temperature, algae photosynthesis Changes in growth rates, other adaptations

Expressing variability

Figure 4.3 shows dissolved oxygen variations of three streams located in southeastern Wisconsin.Lincoln Creek, located in the Milwaukee metropolitan area, is almost 90 percent urbanized andrelatively stable as far as further development is concerned. Quaas Creek, located about 45 kmnorthwest of Milwaukee, is about 30% urbanized and urbanization is expanding. Nichols Creek isa reference rural stream. Quaas and Lincoln Creeks exhibit dissolved oxygen oscillations typicalof nutrient and algae enriched streams. The oxygen saturation value is approximately 10 mg/L. Dueto photosynthetic oxygen production during the day time and respiration during darkness,supersaturation was reached in the late afternoons and oxygen depletion occurred in the earlymorning hours. The magnitude of the oscillations is proportional to the degree of enrichment, inthis case expressed by the surrogate parameter of percent urbanization. Of note is the steep crashof algal population in the Lincoln Creek that resulted in zero oxygen.

The variability can be expressed by various methodologies (see Bendat and Piersol, 1971) such asautocorrelation functions and spectral and Fourier analyses. The most simple is probabilisticplotting, as in Figure 4.4, which plots the annual DO variability for two of the creeks shown inFigure 4.3. In the probabilistic plotting, the data are fitted to the cumulative Gaussian normalprobability distribution. The probabilistic plotting can be either arithmetic or logarithmic where thestressor values are entered in the analysis as their logarithms. If the data, either in the original ortransformed form, follow the normal distribution, they will arrange on the plot as a straight line.Most of the water quality parameters follow log-normal probabilistic distributions.

The best expression of the periodic variability is in terms of the magnitude of the fluctuations andtheir frequency. A power spectrum is a plot of the distribution of the variance of a series of data vs.the frequency. This can be best expressed by the power spectrum of the time series (Bendat and

23

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Dis

solv

ed O

xyge

n (m

g/l) Nichols Creek Quaas Creek Lincoln Creek

Figure 4.3 Dissolved oxygen variations in fully urbanized Lincoln Creek (Milwaukee,WI), partially urbanized Quaas Creek (West Bend, WI) and reference NicholsCreek. Source: Tim Ehlinger.

Piersol, 1971). The problem with this time series analytical method is the need for extensive(continuous) time series of the key or surrogate (e.g., conductivity or DO) parameters.

If variability is significant and is caused by high frequency fluctuation of a potentially critical waterquality parameter, development of a relationship between organism sensitivity (adaptability) andvariations at different frequencies could be researched.

Clearly, as shown in Figure 4.4, Lincoln Creek DO concentrations are more variable than those of QuaasCreek. In the most simple way this can also be expressed by the coefficient of variation:

CVstandard deviation

mean=

A first derivative of the time series of the magnitudes of a parameter is the rate of change. Manyecological processes and biotic compositions reflect the temporal changes of the parameters suchas temperature, sunlight, nutrient and food availability, and frozen and ice-free periods.

Buffering Capacity of a System

24

Dissolved Oxygen (mg/L)

cum

ulat

ive

perc

ent

0 5 10 15 20 25 30 350.1

15

2050809599

99.9

Figure 4.4 Cumulative probability distribution of the annual DO distribution in2002 for Quaas and Lincoln Creeks. Source: Tim Ehlinger.

It is important to distinguish between variability of the stressors that act on the boundary of thesystem and internal response/stressors in the water body. Terrestrial and aquatic systems have theability to buffer variability, meaning that the response of the system to a variable external stresswill be less variable. For conservative substances this will impact the magnitude of the fluctuations,not the mean mass of the response. For nonconservative substances both mean and the fluctuationswill be reduced. The buffering capacity of the system is proportional to the size of the system andis related to the frequency of the fluctuation (both cyclic and random) of the outside stressor. It isalso related to the type of flow in the aquatic system, which can be characterized as plug flow(typically a river), completely mixed (round lake) or dispersed flow (an estuary). For example, alake that has a retention time of one year will remove most daily fluctuations in concentrations ofa conservative toxic compound resulting from a time variable discharge while a plug flow river oran impoundment with a residence time of one day will not. The same large lake will also buffershort term higher frequency random fluctuations.

The estimate of buffering capacity of aquatic ecosystems to various transient conservative and non-conservative periodic and random inputs was developed and published by Novotny (1977).Although this paper refers to design of wastewater treatment units, the same principle and equationsapply to plug flow, dispersed flow, and completely mixed aquatic systems. In general, large waterbodies and completely mixed water bodies will have the largest buffering capacity, followed by thelarger dispersed flow bodies, and the dispersed flow river will have the least amount of buffering,depending again on the size.

25

It can be assumed that the aquatic living organisms may also have a similar buffering system forassimilating and adapting to transient changes; however, this resistance or adaptability to changesin external stressors may be less than for terrestrial warm-blooded species.

Biotic Responses to Time-Variable Stresses

Poff and Ward (1990) describe the response of organisms to time variant changes of the stressorsand/or of the system. The first response to a non-catastrophic event or change is behavioral, e.g.,the organisms will try to avoid stressful conditions. If the stressful condition cannot be avoided, theorganism will undergo a physiological adaptation to the new condition. For example, highertemperature will result in lower growth rates. If the stress is brief, the organism will return to itsprevious conditions. If the changes continue, the organisms will adapt to the change with a newphysiological state. If adaptation cannot occur, the organisms may, after a certain period, disappearfrom the area either by avoidance or due to chronic effects of the stressor. Most organisms areadapted to annual or daily fluctuations; however, if the frequency or magnitude of the fluctuationschanges, this would also represent a change of the stress. The biotic species that cannot adapt tothe variability will be replaced by species that are more resistant to the variability. Watersheds thatare naturally or anthropogenically flashy, based on precipitation and landscape characteristics, willcontain fish and macroinvertebrate communities that have evolved to recover quickly fromrepeating disturbances (Poff and Ward, 1990; Detenbeck et al., 2000).

Substratum (benthos) effects

In lotic systems, the physical habitat structure is critical to abundance and species diversity oforganisms (Southwood, 1977, 1988; Poff and Ward, 1990). Surface roughness and embeddednessaffect colonization dynamics of benthic organisms and feeding, refuge and spawning of fish. Insectdiversity is positively correlated with the surface substratum complexity and particle sizeheterogeneity.

Periodic and random events that disrupt the substrate will have an effect on the quality and diversityof aquatic life residing in a water body. Species requiring stable substrata for growth will not existsuccessfully in a water body where the substratum is constantly disrupted by navigation or transientlarge flows from operation of locks or from peak hydropower plants (McAuliffe, 1984).

The substratum texture and mobility may have an equally profound impact on the composition ofbenthic species. Generally, the texture and composition of the benthic layer is related to the shearstress of the flow that is expressed as

J = ( R Se

where ( is the specific weight of water (9810 N/m3), R is the hydraulic radius, which for streamsis approximately equal to the depth of the stream (meters), and Se is the water surface slope. Theunit of shear stress, J, is N/m2.

The bottom sediment has a resistance to scour that is related to the grain size of the sediment andsediment type. Cohesive fine texture sediments composed of clay, silt and organic matter are more

26

amenable to scour erosion than coarser, non-cohesive sediments of sand and gravel with less or noorganic matter present in the sediment. Cohesive sediments exist only in slow moving or nearly-stagnant lowland streams and impoundments. Literature data (Mehta et al., 1989) indicate that thecritical shear stress for deposition and accumulation of cohesive sediments is about Jc = 0.06 to 0.08N/m2. Deposition and formation of cohesive sediments will not occur if the shear stress at flows lessthan the mean annual flow is greater than the critical shear stress, Jc. For streams at steady state,the slope of the water surface coincides with the channel slope. Non-cohesive sediments (sand andgravel) exist mostly when the shear stress is greater than 1 N/m2. Between J = 0.1 and 1 N/m2, thesediment composition will be mixed.

In polluted or nutrient-enriched water bodies (also considered polluted if the enrichment is notnatural), the sediment in the lowland or impounded streams and in lakes has a high organic content.The deeper layers of the sediment are anaerobic and the particulate organic compounds undergoanaerobic diagenesis (breakdown). The products of diagenesis are methane, carbon dioxide,ammonium and phosphates.

Human Impacts on Substrate

The most profound impact on aquatic habitats caused by human activities results from streamimpoundment, for navigation purposes and/or power production. Impoundment changes thesubstrate texture and increases sedimentation of fine texture sediments and organics that can thenform deep layers. These sediments exhibit sediment oxygen demand and may be resuspended bybarge traffic. Bhowmik et al. (1981) studied the effect of barge traffic on resuspension of sedimentin the impoundments of the Illinois and Ohio Rivers and concluded that:

• Tow passage increases suspended sediment concentrations.

• The increase in concentration is greater in channel border areas than in the navigationalchannel.

• The increase is more significant when the ambient suspended sediment concentration islow.

• The concentration is transient and may last 60 to 90 minutes.

Bhowmik et al. (1981, 1989) showed for the Illinois and Ohio Rivers there was a significant butvery transient resuspension of sediments during barge tow passage. The increases lasted betweena few minutes and ten minutes, at most. Typically, sediment concentrations increased during thebarge tow passage by as much as 90 mg/L but the concentration subsided to its pre-passage valuewithin 10 minutes after the passage. In addition, Butts and Shackleford (1992), who studied theUpper Illinois River, did not find significant differences in sediment concentrations with andwithout traffic. However, constant resuspension may disrupt the habitat for benthic invertebratesand feeding of fish that will have an effect on biotic integrity.

Even streams that were impounded for purposes other than navigation exhibit diminished speciesdiversity and composition that is subsequently reflected in the magnitude of the Index of BioticIntegrity (AquaNova/Hey Associates, 2003).

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CHAPTER 5

MODELING ECOSYSTEMS WITH DYNAMICMULTIVARIATE APPROACHES

Applying Multivariate Models to Ecological Systems

Multivariate methods are now widely accepted by ecologists and many treatises have been writtenregarding their application to ecological systems (Green, 1980). Measuring similarities amongsamples or groups of samples with respect to taxa is the most common problem in ecology.Ecological studies and models often require prediction of responses of more than one biologicalvariable caused by more than one stress. Green states that multivariate analyses often represent themost appropriate and the most powerful approaches to both the description of the ecosystem andhypothesis-testing. Every univariate model used in ecology is only a part of a general multivariatemodel and the latter is more appropriate for most ecological problems. However, parsimony of themodel, i.e., using fewer important variables over a multiplicity of variables when significance isless than the noise, is counterproductive. Thus, the development of a multivariate model mustinclude the following steps:

1. Identify the ecological endpoints to be measured and modeled

2. Identify the stressors in a hierarchical order

3. Find cross-correlations between the stressors both horizontally (among the stressors on thesame level of hierarchy) and vertically (between the stressors at the upper and lower layersof hierarchy)

4. Make appropriate transformations of variables, e.g., using log transformed variables

5. Conduct a multivariate analysis to identify the relationships among the stressors and theendpoints

6. Conduct sensitivity analyses and make the model parsimonious by eliminating insignificantstressors or cross-correlated stressors

7. Verify the model

8. Display the results visually

Multivariate methods of analysis of biological data and their relation to boundary and internalstresses have been used and accepted by ecologists and also by water quality specialists (modelers)for a long time. The ecological models that rely on these relationships have been extensivelycovered in the literature (e.g., Chapra, 1997; Jørgensen and Bendoricchio, 2001). Such modelsprimarily describe water quality concentrations and the mass (or concentration) of the lower trophiclevel overall biotic composition or surrogates (e.g., chlorophyll a, phytoplankton and zooplanktonbiomass and growth). The development of models that describe fish or macroinvertebrate species

28

Figure 5.1 Concept of a multivariate/multimetric ecologicalmodel

or even genera biomass has not been successful using deterministic, strictly functionalmathematical, models.

Ecological modeling has progressed from simple dissolved oxygen models, conceived in 1920s,to population dynamics river models developed between 1960 and 1975, to ecotoxicological modelsin 1990s, to models developed by learning software such as Neural Networks and GeneticAlgorithms (Jørgensen and Bendoricchio, 2001). The application and development of the mostrecent generation of “learning models” to ecology and ecological processes are still in their infancy.

Likens (1985) pointed out that the motivation for ecological studies and modeling is to achieve anunderstanding of the entire ecosystem, giving more insight than the sum of knowledge about itsparts relative to the structure, metabolisms and biochemistry of the landscape. An ecosystem isorganized, but also includes a degree of randomness. The more that is known about the processesand stresses that affect the composition of the biotic assemblages, the more uncertainty isintroduced into the description of the system because each subprocess has its own randomness anduncertainty. Uncertainty is not identical to randomness. Uncertainty, as the term implies, includesboth the randomness inherent in each process and the lack of precise knowledge about the processand its complexity. Jørgensen and Bendoricchio (2001) distinguish structural complexity, definedas the number of interconnections between components in the system and functional complexity,which is the number of distinct functions carried out by the system (Figure 5.1).

A multivariate ecological system is rarely in a stagnant invariant state. Even for unimpactedwatersheds covered by native vegetation (see Figure 1.1), the biotic composition may respond tolong- and short-term meteorological variations, seasons and other factors. Such inherent variability

29

is the reason why the biotic integrity of disturbed watersheds should always be related to ornormalized by that measured at reference, unimpacted water bodies of similar character located inthe same ecoregion. The biotic integrity may be in equilibrium with the long-term invariantstressors that can be expressed by invariant surrogates (e.g., percent imperviousness) or stressorsthat cause a downward temporal or permanent change such as the rate of deforestation in thewatershed or a change of a regional diffuse pollution load by one or more pollutants such as anincrease in acidity of rainfall.

Multi-layer models

An ecological model linking stressors to biotic endpoints is often hierarchical, where the impactsof stressors propagate through several structural layers. Such a model has been proposed by Allenand Starr (1982). In Allen and Starr’s concept, the hierarchical model is defined in terms of stemsand holons. A holon is a structural element of the model and a stem is a functional connector of theholons. In a nested case, the span of a given holon is the sum of the parts of which the model ismade. Holons are connected by stems. A stem is a functional relation that converts a stimulus (e.g.,risk) from a lower level holon to a higher level holon. The structure of this model is similar toadvanced neural net models (Hecht-Nielsen, 1990; Lek and Guégan, 2000). An artificial neuralnetwork model (ANN) is a layered multi-regression model that can resolve and learn both linearand nonlinear relationships. ANN is a computer algorithm that responds to a problem in a fashionsimilar to the human brain, including association, generalization, parallel search, learning andadaptability (Treveleaven et al., 1989).

Multivariate/multi-metric ecological models cannot detect variability below a seasonal fluctuation(e.g., daily variations). Because such models are developed a posteriori from measured data, evenseasonal fluctuation may be difficult to detect because of the lack of data.

A model is always a crude representation of a real, complex biotic system. However, there arecommonalities between the system and the model representing it. A system is made of components(building blocks) that receive inputs and boundary stresses, process them and produce a responsethat may then act upon another component. The components often include a storage feature thatacts when the output is constricted so that the excess input is stored and can be processed inside thecomponent. Commonly, the storage capacity has a limit that can impact the magnitude of theoutput. Mathematically, the system can be linear or nonlinear, steady (invariant) or dynamic (timevariable).

A component of the ecosystem can by either physical or biological. Physical components includesoil within the watershed, water and sediments in the water bodies, water in underground aquifers,or accumulated solids on impervious surfaces. The habitat for biota is a physical component thatstrongly affects the composition of the biotic species but has a relatively small effect on the transferof chemicals and nutrients unless it is part of the riparian buffer. Biological components arenumerous and can be categorized biologically along trophic levels from autotrophic andheterotrophic microorganisms to invertebrate and vertebrate communities. The componentsexchange energy, nutrients and other chemical mass. The most measured ecosystem properties arethe biomass of the system or its components, productivity of the system, nutrient dynamics (Suter,1990) and the chemical status of the components. The status and productivity of an ecosystem tend

30

to have an impact on the density and composition of the biotic endpoints that can themselves alsoform a component of the system. For example, the macroinvertebrate community that is commonlyused as an indicator of the biotic integrity endpoint is linked in a hierarchical manner to the highertrophic level fish ecosystem response indicator/endpoint.

The components themselves and their storage are related to landscape and water bodymorphological and ecological parameters. These characteristics are readily measurable and in mostcases are invariant. Although they are not a stress themselves, the changes in these parameters andemissions of pollutants may result in stressors. They also affect the capacity of the systemcomponents to store and sometimes assimilate (decompose) the accumulated pollutants.

Selection of endpoints

Although the goal of this project is to find a multivariate model based on the metrics of the twoIndices of Biotic Integrity (fish and benthic macroinvertebrate), it may be useful to reiterate thatthere are numerous endpoints that have been proposed for watershed and water body ecologicalclassifications. The term endpoint has an analogous meaning in environmental system analysis thatdeals with decision variables and criteria. For example, the endpoint of the simple dissolved oxygenmodel is the DO concentration in the river that is then, in the decision phase, compared with theestablished DO standard. In this sense, the comparison with the water quality standard is theendpoint.

Ecological endpoints are more complex but based on the same principle. Endpoints have beencategorized as assessment and measurement endpoints (Suter, 1990; Simon and Davis, 1992). Anassessment endpoint is an environmental characteristic that should have societal relevance that isunderstood and valued by the public and by decision makers. Suter (1990) presented the criteriafor assessment endpoint selection, as shown in Table 5.1. The Indices of Biotic Integrity fit into thiscategory because (1) they express the goals of the Clean Water Act to provide for a balancedaquatic biota and (2) they are becoming widely acceptable by decision makers and are graduallybeing accepted by the public as well.

Measurement endpoints should correspond to or be a predictor of an assessment endpoint, i.e., ofthe IBIs. Suter (1990) states that measurement endpoints should be correlated either to theassessment endpoint or be one of a set of measurement endpoints that predicts an assessmentendpoint through a statistical or mathematical model. If fish IBI is selected as the main assessmentendpoint, then the measurements endpoints are those lower-level variables to which the fish IBIwill be correlated, e.g., habitat endpoint (index), concentrations of contaminants in water andsediments expressed as a risk, and even the macroinvertebrate endpoint (IBI) because fish belongto a higher trophic level. The lower level endpoints also may be useful on their own. For example,a chemical risk derived from concentrations of toxic compounds is a common tool for evaluatingcompliance with the goals of the Clean Water Act (chemical integrity), and the habitat suitabilityindex serves for assessment of actions that would lead to water body restoration. This indeed leadsto a hierarchy of endpoints and to a hierarchical model of linking the stressors (lowest layer ofvariables of the model) to the lower level measurement endpoints and then to the highest levelendpoint, fish. The well-being of humans could become an endpoint if humans are linked to the

31

ecosystems model through drinking water, eating fish and skin exposure to contaminated water byprimary recreation.

Generally, in risk assessment, endpoints reflect regional societal values, such as crop health, humanhealth, and fish composition. Detenbeck et al. (2000) divides the endpoints into structural andfunctional. Structural endpoints listed in this paper are peak flow stages during spring snowmelt,snowmelt and base flow water quality, stable bottom sediment characteristics, physical in-streamand riparian characteristics, and periphyton, macroinvertebrate, and fish community structure(sampled once during baseflow conditions in late summer). Functional endpoints derived fromthese static measurements include macroinvertebrate and fish guilds, fish reproductive and flowtolerance guilds, and percentage of motile biraphid diatoms in periphyton communities. Theidentification of the multivariate classification template in Detenbeck et al. (2000) is not muchdifferent from the endpoint identification of the Indices of Biotic Integrity. According to thesedefinitions, the Indices of Biotic Integrity are functional endpoints.

Table 5.1 Characteristics of good assessment endpoints (from Suter, 1990)_________________________________________________

Social relevanceBiological relevanceUnambiguous operation definitionAccessible to prediction and measurementSusceptible to the hazard

_________________________________________________

Development of the Model Structure

Karr et al. (1985b) provided the first insight into the complex structure of the relationships betweenthe landscape and other stressors and fish communities. These relationships formed the basis forthe formulation of the biotic integrity concept expressed in Figure 2.1. This concept is shown inFigure 5.2. The structural components were described as:

• Energy source: allochthonous organic matter vs. primary production in the stream, particlesize distribution of particulate organics

• Water quality: temperature, turbidity, dissolved oxygen, soluble organics and inorganics,toxic metals, other toxic substances

• Habitat structure: bottom type, water depth, current velocity, availability of spawning,nursery and hiding places, diversity of habitats (e.g., pool and riffle complexes)

• Flow regime: water volume, temporal distribution (seasonality and low flows) of wateravailability, flood frequency

• Biotic interaction: competition, predation, disease, and parasitism

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Figure 5.2 Conceptual model of the primary external stressors and internalstructure of the integrity of stream aquatic biota (from Karr et al.,1985b)

Figure 5.3 is a schematic of the ecosystem progression of risks, showing the main functional linksand endpoints that are being developed in the STAR research project by Northeastern Universityand partners. The first step for the a priori model development from the measured databases is toorganize the ecosystem model structure into structural and functional components. The concept ofthe model concept shown in Figure 5.3 is almost identical to the structure of the stress progressionproposed by Karr and Yoder (2003), shown in Figure 5.4 and derived partially from the conceptualmodel proposed by Karr et al. (1985b). It is possible now to generalize the stressor-exposure (risk)-endpoint model and develop the layers of its hierarchical structure. A more detailed description ofmodel components and functions will be presented in Chapter 6.

The lowest layer, past and ongoing landscape and channel modifications by humans (pollution butnot pollutants) and emissions of pollutants from point and non-point sources, represents the rootcauses of the problem. The landscape and emission parameters have to be quantified. Severaltraditional models are available such as the Universal Soil Loss Equation (Wischmeier and Smith,1965) for pervious areas and build-up/wash-off concept for pervious areas (see Novotny, 2003).Such input parameters should be long-term and expressed in statistical terms so that the variabilitycan be also estimated. In some cases surrogate parameters (e.g., percent connected impervious area,percent forest or agricultural area) with associated unit loads may be substituted for more reliableyet simple functional models. Examples of functions include the dilution model, delivery ratiofunction, simple sedimentation functions, and simple sediment partitioning. A function fordissolved oxygen variability as a result of nutrient enrichment can also be developed.

33

Figure 5.3 Concept of the stressor-risk-endpoint propagation model based onNovotny et al. (2001) and Novotny (2003).

The third layer is the estimation of risks. A risk is a numeric probability that some species will beadversely affected by the exposure to the contaminants and habitat impacts and will disappear fromthe system. In most cases, only the most sensitive species are in a danger. The risk (probability) canbe calculated from the statistics of the contaminant and the resistance of the representative species.The risks are chemical or channel disturbance specific. The model should link the individual risksand consider their synergy, additivity or antagonism. The risks are the measurement endpoints.

Because they express the same measure, the probability that species can disappear, they can becompared and prioritized. Risk can also be linked to the probability of exceeding established waterquality standards.

The risks can be related to the biotic endpoints expressed by the metrics of the Indices of BioticIntegrity (fish and macroinvertebrate). If human impacts are considered, risks of priority pollutantscan be linked by pollutant partitioning and biomagnification models to the risk effects on humanhealth.

Given the many complex interactions involved in modeling the relationships between stressors andendpoints, it is clear that univariate or multivariate single layer models may not work, except insome localized single stressor situations. Multi-layer models, as will be described in the nextchapter, represent a more realistic approach.

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Designated use(water body specific)

Pollutant (P and NP) loading for all sources

(source specific)

Pollution (specific human activities)

Ambient pollutant levels in water body(chemical specific)

Ecological health (cumulative effects onbiological condition)

Stressor

Exposure(in-stream)

Response

Channel and flow

alterations

Land useeffects

Riparian and in-channel effects

Endpoint

Exposure(landscape)

Human health (health outcomes including disease)

Figure 5.4 Concept of the links between stressors,exposure, and response (end points) by Karr andYoder (2003) modified from the Committee toAssess TMDL (2001)

Several key stressors that impact the integrity of the water body are not due to pollutant discharges.Such stressors affect habitat, spawning areas, or living conditions of aquatic species. They includestream hydraulic modifications by impoundments, lining, drop structures, or ripraps, which cancause siltation by excessive sediment inputs, habitat loss or degradation, intensive navigation andloss of riparian vegetation. Habitat fragmentation and interruption of migratory routes by dams orthermal barriers caused by thermal discharges are also important stressors. Other exposure linksare related to the effects of pollutants on water quality. Such links include eutrophication, dissolvedoxygen levels and fluctuations, temperature and its fluctuations, and salinity fluctuations andtransient inputs during winter in snow-belt areas. When the dissolved oxygen reaches certain lowlevels, some species will disappear and may be replaced by species tolerant to the decrease in (e.g.,worms, certain lower level species of fish) or complete lack of oxygen (facultative or anaerobicmicroorganisms).

CHAPTER 6

35

MODEL BUILDING

Selection of Submodels (Functional Links)

The models (hierarchical links) describe the variability of the stressor with respect to a thresholdat which a breakpoint in continuity (discontinuity) will occur. The discontinuity may not be assimple as a deterministic crash of part of the system; rather, it is a probability that the system or acomponent will be adversely affected to a point that it cannot function at a level necessary for thesustainability of the system.

Effects-based threshold models or submodels are based on the clustering of community-levelresponses to stressors such as hydrologic regime (Detenbeck et al., 2000). Poff and Allan (1995)identified fish guilds’ response to two hydrologic variables - baseflow stability and flashiness.Other models related the membership of particular reproducible guilds to turbidity (Poff and Ward,1989) and to recovery of fish population to disturbance (Detenbeck et al., 1992).

Model structure

Figures 5.2 to 5.4 show the conceptual organization of the hierarchical model that progresses fromthe lowest level stressors to the highest level endpoints. It is not the task of this research project tocarry out the progression all the way to humans; however, adding a human component certainly haspotential. At this level of knowledge about the ecosystem, modeling a four-layer model, shown inFigure 6.1, has been proposed and will be investigated.

The hierarchical layered model is different and, obviously, more complex than the one layerunivariate (e.g., IBI vs. imperviousness) or multiparameter watershed classification systemsproposed by Detenbeck et al. (2000), Moss et al. (1987), Poff and Ward (1989), Morley and Karr(2002), and others. The basic premise of the layered model is the fact that the biota in the aquaticsystem are separated by buffers and other processes from the land based stressors. In other words,it is not the imperviousness that causes a loss of species; it is the immediate exposure and risk, suchas elevated or fluctuating temperature or toxic contaminants in water or damaged habitat. Thislayered approach allows for the identification of the stresses one or two layers down that aresignificant and should or could be managed. A sensitivity analysis of the model can accomplish this

36

Figure 6.1 Schematic of the multilayer risk propagation model.

task. Some functional links may have a buffer component that reduces the variability andmagnitude of the stress variable being passed from one layer to the next.

Starting with the top layer, the assessment endpoints, the structural components and functional linesof the model, are described in the following sections.

Layer I - Assessment Endpoints

The objective of the research is to determine the factors that affect the integrity of the receivingwater bodies. While Suter (1990) and others have listed several biotic endpoints (see the discussionin the preceding section), there are only two established and widely accepted Indices of BioticIntegrity that will be used in the research as the main assessment endpoints. These are the fishIndex of Biotic Integrity, originally proposed by Karr et al. (1986), and the macroinvertebrateIndex, originally proposed by Hilsenhoff (1987) and further expanded and modified (e.g., Karr andKerans, 1992). These indices were included in the national USEPA guideline documents (Plafkinet al., 1989; Barbour et al., 1997, 1999). Both indices were modified for regional conditions(Lyons, 1992; Miller et al., 1988; Lyons et al., 1996, 2001; Yoder and Smith, 1999) and newindices for benthic systems have been developed (Karr and Chu, 1999).

37

The most recent manual on the Rapid Bioassessment Protocols (Barbour et al., 1999) also containsthe IBI methodology for periphyton (benthic algae), which are primary producers and an importantfoundation of many stream food-webs. Periphyton, as well as zoobenthos, have been used asindicators in Europe for at least twenty years (Marvan, 1991; Whitton et al., 1991) and themethodology is now gaining acceptance in the US. Algae are indicators of both nutrient enrichment(or deficiency) and water quality, including temperature. How the periphyton indicators fit into theconcept of biotic integrity modeling is unclear and still unknown. Periphyton have been used formonitoring water quality only in few states and there may be a paucity of data in the nationaldatabases. Furthermore, the periphyton evaluation has not yet been fully developed into aquantitative index. Nevertheless, the periphyton box has been added to the model concept presentedin Figure 6.1 for future references and research.

Both fish and macroinvertebrate indices are scored evaluations of composition, health andabundance of the organisms. They have to be applied within an ecoregional context, in most casesby normalizing the absolute site-specific IBIs to the values obtained in reference water bodies.

The fish index has a higher trophic level than the macroinvertebrate index. This implies that theremay be some interrelationships between the invertebrate index and fish index. Theseinterrelationships can be correlative where the invertebrate quality and composition affect the fishpopulation or cross-correlative where both groups of organisms responds in a similar fashion to astressor or stressors. Even though no such relationships have been identified in the literature, theyare likely to occur.

Each index is a summation of the valuation of its metrics. The fish index has 12 metrics separatedinto three categories: (1) species richness and composition, (2) trophic composition, and (3) fishabundance and conditions. The individual metrics and their scoring are given in Table 6.1.

There are several similar variations in benthic invertebrate indices. Karr and Morley (2001) useda 10 metric IBI (Table 6.2) with four categories: (1) Taxa richness and composition, (2) Populationattributes, (3) Tolerance and intolerance, and (4) Feeding and other habits. The Ohio InvertebrateCommunity Index (ICI) has 10 metrics, as shown in Table 6.3.

Throughout the model development, it will be assumed that at least the categories of metrics willbe affected differently by the lower level stressors. For example, sediment contamination risk willhave a greater impact on benthic macroinvertebrates than on fish populations, while for watercolumn contamination will have a greater impact on fish. Similarly, fragmentation risk will affectfish and macroinvertebrate populations differently.

38

Table 6.1 Metrics used in assessment of fish communities (from Karr et al., 1984, 1986;Plafkin et al.,1989)

Scoring Criteria ___________________________

Category Metric 5 (best) 3 1(worst)

__________________________________________________________________________________Species Richness Total number of fish species Varies with stream size and regionand Composition Number and identity of darter species Varies with stream size and region

Number and identity of sunfish species Varies with stream size and regionNumber and identity of sucker species Varies with stream size and regionNumber and identity of intolerant species Varies with stream size and regionProportion of individuals as green sunfish <5% 5-20% >20%

Trophic Conditions Proportion of individuals as omnivores <20% 20-45% >45%Proportion of individuals as insectivores >45% 20-45% <20%CyprinidsProportion of individuals as top carnivores >5% 1-5% <1%

Fish Abundance and Number of individuals in sample Varies with stream size and regionHealth Proportion of individuals as hybrids 0 0-1% >1%

Proportion of individuals with disease, 0 0-1% >1%tumors, fin damage and other anomalies

__________________________________________________________________________________

Table 6.2 Metrics of the Index of Biological Integrity for Benthic Macroinvertebrates (B-IBI)from Karr and Kerans (1992)

Category Metrics__________________________________________________________________________________Taxa Richness and Composition Total taxa richness

Mayfly taxa richnessStonefly taxa richnessCaddisfly taxa richnessLong-lived taxa richness

Tolerance and Intolerance Intolerant taxa richnessTolerant taxa %

Feeding and Other Habits Clinger taxa richnessPredators %

Other Dominance by top 3 taxa % __________________________________________________________________________________

Table 6.3 Metrics of the Ohio Invertebrate Community Index (ICI) from DeShon (1995)__________________________________________________________________________________Total number of taxa Percent caddisfly compositionNumber of mayfly taxa Percent tribe tanytarsini midge compositionNumber of caddisfly taxa Percent of other dipteran and non-insects compositionNumber of dipteran taxa Percent tolerant organismsPercent mayfly composition Number of qualitative Ephemeroptera (mayflies), Plecoptera

(stoneflies), and Trichoptera (caddisflies) (EPT) taxa__________________________________________________________________________________

Several aspects of the project proposal are related to exploring and developing indices.

39

Figure 6.2 Algae population shifts with temperature(Cairns, 1955)

• Linking macroinvertebrate IBI to fish IBI. The database containing both B-IBI (ICI) andfish IBI will be investigated to identify correlations between the macroinvertebrate and fishmetrics. These correlations may be affected by habitat quality and chemical risks. Theproject could be a MSc thesis or group research project for a limnology class. The researchshould use simple multiple regression methodologies and plotting to identify potentialrelationships.

• Improvement and development of a quantitative periphyton index and linking it tostressors and fish and macroinvertebrate integrity. Periphyton can be a very usefulindicator of integrity, especially with respect to nutrient and organic enrichment,temperature, and current. Algae usually do not develop in zones of higher organic pollutionmainly due to predation by heterotrophic decomposers, even in zones of high nutrientenrichment. When high organic pollution is removed, filamentous and other attached algaemay grow in-stream at high densities. The composition of the algal species is also relatedto temperature; lower temperatures tend to favor diatoms, while higher temperatures oftenlead to the development of blue-green algae in nutrient-enriched streams (Figure 6.2).Periphyton densities and diversity will also be related to the depth of the stream, currentvelocity and substrate character (Barbour et al., 1999).

Layer II - Risks

Four categories of risk can be considered for an aquatic water body:

A. Water column risk: A risk caused by water column pollutant levels (includingtemperature) and their variability causing indigenous species to disappear from water

B. Sediment risk: A risk that the organisms residing or feeding in the benthic layer or

40

interstitial sediment-water layer will disappear as a result of the upper sediment layercontamination

C. Habitat risk: A risk that the habitat has been modified or disturbed by humans to a degreethat life functions of the organisms such as spawning, feeding, or shelter cannot besupported

D. Fragmentation risk: A risk due to fragmentation of the system by impoundments,hydraulic modifications, drop steps, bridges and culverts, or diking that prevents naturalmigration of the organisms (e.g., to and from natural spawning areas). The fragmentationcan be longitudinal (upstream/downstream) or transverse (between the channel and theriparian wetlands)

While the first three types of risks have been analyzed quantitatively, as will be described below,the fragmentation risk has not yet been quantitatively defined.

A. Water Column Risks

The water column risk can be calculated by two methods:

1. Calculating a probability that a threshold value corresponding to the adverse effect on themost sensitive species by a stressor is exceeded.

This is a methodology that can take advantage of the definition and development of water qualitystandards. The standards are based on the sensitivity of the organisms at the 5th percentilesensitivity (acute and chronic) to a contaminant. This value is called the Final Acute (Chronic)Value. FAV (FCV) represents a value at which approximately 95% of species would not beadversely affected. To provide a close to full protection, the standard (criterion) is selected asCMC = " (FAV), where the recommended value for " = 0.5.

Because most water quality (concentration) data are log-normally distributed, the probability ofexceeding the standard can be calculated using standard statistical methods (see Hahn and Mecker,1991; Gibbons, 2001; Novotny, 2003). Figure 6.3 shows graphically the concept of estimating theprobability. The risk can then be calculated as a joint probability (Novotny and Witte, 1997)

p . p1p2 pww "

where p is the overall joint probability of adverse toxicological-ecological effect, p1 is the safetyfactor incorporated in the numeric criteria from the 96-hour bioassays using the USEPA procedure(this factor has a value of approximately 0.001), p2 is the probability of exceedance of the waterquality criterion (which should consider the biological availability effects as expressed in the watereffect ratio, WER), pww is the probability of wet-weather flow (for the Central United States, thisprobability is about 0.065) or dry weather flow, and " is a factor that considers the effect of thedifference between the 96-hour duration of the test exposure and the expected duration of stormevents (for an average storm of 9-hour duration, " = 0.3). If wet and dry weather flows are notseparated then pww = 1.0.

41

Figure 6.3 Log-normal probability plot of a water quality parameter and assessmentof probability of compliance (noncompliance) with its water qualitystandard

2. Calculating joint probability of the concentration and species adverse effect.

For multispecies biotic systems, the probability of an adverse change of the system is the same asthe risk that species will disappear from the system. The risk is due to single or multiple stresses.Since each species may have a different tolerance to the stresses, the most sensitive species willdisappear first. The species also differ in tolerance to the different stressors causing the risk. Fishare very sensitive to low dissolved oxygen concentrations while sludge worms (e.g., Tubifextubifex) are not. However, in contrast, sludge worms are far more sensitive to concentration of toxicmetals, such as copper, than fish. The risk for an individual species of organisms is linked to theprobability that the stressor will reach a threshold value that will result in an adverse effect that canbe either acute (lethal) or chronic (loss of mobility or reproduction). The overall risk that some orall of a species will disappear is a cumulative probability (integral) of the individual risks. Theconcept of the ecologic risk for a single stressor (e.g., concentration of a toxic compound) or riskis shown in Figure 6.4. It was outlined in the Water Environment Research Foundation (WERF)methodology by Parkhurst et al. (1996) and modified for stormwater discharges in Novotny andWitte (1997). This concept is based on a direct consideration of the joint probability of twoprobability functions:

(1) the probability density function (pdf) of the stressor (C) adjusted for the appropriate dilution

42

Figure 6.4 Ecological risk assessment for stormwater impacts (fromNovotny and Witte, 1997).

ratio (DR) and Water Effect Ratio (WEF): f(C) = pdf(C x DR/WER), and

(2) the risk function g(RzC), which gives the value of the probability that an organism will beadversely affected by the exposure to the stressor C modified by DR and WER.

The joint probability is:

h R C C g R C( , ) ( ) ( )= ⊥

The integration over all probabilistic values of the stressor, as summarized in Figure 6.4, will thenyield the total risk, r.

The r value, however, expresses the total risk due to one stressor only. Therefore, the total risk dueto all the relevant stressors will be:

R ri= Σ

The stressors may exert a combined effect on an organism (additive), they may interfere one withanother (antagonism), or their overall effect may be greater than when acting alone (synergism)(Mason, 1991). An example of an additive interaction is the combined toxicity of zinc and cadmiumto fish, though their toxicity is synergistic to algae. Calcium (hardness) is antagonistic to heavymetals.

43

Figure 6.5 Plot of genus mean acute values for determination of risk of a pollutant(copper) to aquatic biota

Statistical Specifications

An example of the risk function, r, for copper is shown in Figure 6.5. For metals, the sensitivity oforganisms depends on the hardness of water and should be adjusted accordingly. Given that thereare more than 700 species of fish in North America (Suter, 1993) and tens of thousands of otheraquatic species, the expectation that the limited set of species that have been tested contains themost sensitive species would be quite naïve. This assumption is avoided by assuming that thesensitivity of species follows some probability distribution.

The risk function shown in Figure 6.5 for copper extends over several orders of magnitude ofcopper concentrations. Other parameters will have a more narrow impact range (e.g., temperature,dissolved oxygen).

44

KCCp

s

d=

Selecting Distribution for Risk Function

The choice of distribution can have a profound effect on the final risk estimation, especially in thearea of lower concentrations. There are two distributions involved in risk calculation: (1)distribution of concentration and (2) distribution of toxic response to given concentration. Usually,the distribution of ambient concentration is well described. It has been established by numerousstudies that the ambient concentration of most parameters follows a log-normal distribution. Themean and standard deviation of log-concentrations provide sufficient information to fit thedistribution, given that sample size is sufficiently large. On the other hand, the toxic response curveis defined rather poorly. Depending on the specific chemical tested, sometimes only as few as 10data points are available.

B. Sediment risk

Risk to benthic organisms caused by sediment contamination may be ascertained in a similar wayas the aquatic risk. However, there are several key differences: (1) The variability of the sedimentconcentrations is more spatial and not as temporal as that for the water column; (2) Only the upperlayer of the sediment (approximately 4 cm thick) may affect the benthic macroinvertebrates; (3)Many aquatic organisms (e.g., periphyton) may not be affected, so there should be a directfunctional link between the sediment risk and benthic IBI; and (4) Acute effects are of lesserimportance than the chronic effects on the biota residing or feeding in or near the sediments.

The major assumptions to be made in the modification of the risk estimation for sediments are: (1)The exposure of receptor organisms is limited to benthic species, and (2) the major, and thus onlyconsidered, exposure route is through the interstitial pore water. Applied to sediments, the jointprobability function gives the probability that (1) a particular concentration will occur as a resultof partitioning in sediment and (2) an indigenous benthic organism will be adversely impacted bya given concentration of a contaminant.

The estimation of pore water concentrations is done by using the sediment partitioning concept:

where Kp is the partitioning coefficient, Cs is the concentration in the solid phase and Cd is thedissolved concentration in pore water.

There are two major obstacles that complicate the use of ecological risk methodology for estimatingthe effects of contaminated sediment. First, data on sediment quality are often limited. This isespecially true for data available from USGS or STORET, where the sediment is sampled only onceso the distribution of concentration, both spatial and temporal, is unknown. Second, the exact valuefor the partition coefficient used to estimate pore water concentration from sediment contaminationis unknown.

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C. Habitat Risk

Habitat risk can be related to a habitat index. Most habitat indices are numeric summations ofseveral metrics and do not express a risk as defined above, i.e., a probability that indigenous specieswill disappear from the water body. Such effects can be implicitly considered in cases such ashuman modifications by channelization, flow withdrawals (lack of flow) or by habitats inhospitableto certain species. Generally, the metrics express the effects of flow, hydrology and habitat structure(Figure 2.1) on integrity. However, work by Bartošová (2002) has documented that, at least in somecases, the habitat parameter metrics describing substrate and riparian conditions can be correlatedto the numeric values of the metrics of the macroinvertebrate index.

The Habitat Quality index (RBP HQ) was included in the Rapid Bioassessment Protocols (Barbour1997, 1999). Rankin (1995) described the concept of the Ohio Qualitative Habitat Evaluation Index(QHEI) and compared it with the RBP HQ index. As is true for all biotic indices, the applicationof the habitat index must be adjusted to regional or ecoregional conditions. This requirement wasthe reason for developing the Ohio Index.

The habitat index is closely related to the hydrology and morphology of streams. Thesecharacteristics are described in several excellent texts, including Leopold et al. (1994) and Rosgen(1996). Barbour and Stribling (1991) describe four generic categories of stream types: mountain,piedmont, valley/plain, and coastal, for which the relative importance of habitat will differ. Thisclassification scheme was preceded by twenty years by Huet (1949), who proposed a system forclassification of streams according to fish species that used width (size) and gradient of the stream(Figure 6.6). There is an apparent similarity between the two classification schemes. Hence,gradient and size (width) of the streams are the most important habitat classification parameters.Rankin (1995) concludes that much of the variability in habitat conditions among these streams isrelated to the energy of the streams that affects habitat substrate, gravel and rock for high slopemountain streams, gravel and sand for medium slope piedmont streams, alluvial deposits and finesediments for low slope valley/plain water bodies, and organic fine sediments for the flat slopelowland and coastal waters. Impounding the streams changes the energy and the substrate. Barbourand Stribling (1991) modified the original RBP habitat procedure (Plafkin et al., 1989) by includingseparate methods for high gradient (riffle/run dominated) streams and low slope, large streamsdominated by pool/bend sequence.

Rosgen (1994) presented a stream and river classification system based on the premise thatdynamically stable channels have a morphology that provides appropriate distribution of flowduring storm events. He identified 8 major variables, each of which affect the stability of channelmorphology but are mutually independent: channel width, channel depth, flow velocity, discharge,channel slope, roughness of channel materials, sediment load and sediment particle sizedistribution. When streams have one of these characteristics altered, some of their capability todissipate energy is lost. Leopold et al. (1994) show that stable channels have enough capacity toaccommodate flows that have a recurrence interval of 1½ to 2 years (Figure 6.7).

46

Figure 6.6 Dominant fish assemblages related to stream morphology (fromHuet, 1949)

Figure 6.7 Flow, depth and recurrence interval of flows for natural stable channels (from Leopold et al., 1994).

The habitat structural components that dissipate flow energy are (Barbour et al., 1999):

• sinuosity

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• roughness of bed and bank materials

• presence of point bars (slope is an important characteristic)

• vegetative conditions of stream banks and riparian zone

• conditions of the floodplain (accessibility from bank, overflow, and size are importantcharacteristics)

The Rapid Bioassessment Protocols (Barbour et al., 1999) that describe methodology fordevelopment of the Index of Biotic Integrity list ten habitat parameters. Some of them can becorrelated to flow characteristics. Each parameter is ranked poor, marginal, suboptimal and optimaland assigned a numerical value that ranges from 0 to 20 or 0 to 10, depending on the relativeimportance of the parameter. The index is then a summation of the numeric ranking. The maximumvalue of the RBP habitat quality index is 170. The parameters for physical habitat evaluation areshown in Table 6.4.

The Ohio Qualitative Habitat Evaluation Index (QHEI) includes 6 groups of parameters (Table 6.5).Rankin (1995) compared the RBP habitat index and QHEI using extensive data from Ohio streams.He argued for regionalization of the habitat indices which was also suggested in the RBPdocuments (Barbour et al. 1997, 1999) and by Barbour and Stribling (1991). Rankin (1995)documented that the index may lose its power if it is not tailored to local conditions. The regionalmodifications are mostly needed to adjust weight of the habitat metrics and not the selection of theparameters in the metrics. Figure 6.8 shows the relationship between Ohio’s QHEI and the RBPHQ index (based on Plafkin et al, 1989). The 1989 RBP HQ index has 9 metrics with a maximumnumber of points of 135. One may anticipate that there should be a very high degree of correlationbetween the two indices. Nevertheless, Rankin (1995) has proven that the regionalized QHEIoutperformed the original RBP HQ index (Plafkin et al., 1989) as shown in Figure 6.9. No suchcomparison was found in the literature for the latest improved version of the RBP HQ index inBarbour et al (1999).

Table 6.4 Description of habitat parameters and scoring scheme used in the RapidBioassessment Protocol to assess habitat quality (Barbour et al., 1999).Maximum total score is 170.

_______________________________________________________________________________

1. Epifaunal substrate/available cover. Range of scores: 0 - 20Includes relative quantity of natural structures in the stream, such as cobble (riffles), large rocks, fallen trees,logs and branches, undercut banks available as refugia, feeding, or sites for spawning and nursery functionsof aquatic macrofauna.

2.a Embeddedness Range of scores: 0 - 20Measures the degree to which boulders or gravel are surrounded by fine sediments (silt, mud, or fine sand). Asrocks become embedded, the surface area available to macroinvertebrates and fish (shelter, spawning, and eggincubation) is decreased. Embeddedness measurements are taken in the riffle and cobble portion of the stream

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Table 6.4 Continuing

2.b Pool substrate characterization Range of scores: 0 - 20Expresses the extent to which rocks (gravel, cobble, and boulders) are covered or sunken into the silt, sand,or mud of the stream bottom. Same effect as 2.a.

3.a Stream velocity/depth combination Range of scores: 0 - 20Patterns of velocity and depth are included for high gradient streams as an important feature of diversity. The beststreams will have 4 patterns present (1) slow-deep, (2) slow-shallow, (3) fast-deep and (4) fast-shallow. Thedivision between shallow and deep is 0.5 meters and between fast and slow is 0.3 m/s.

3.b Pool variability Range of scores: 0 - 20The 4 basic pool types in a stream are large-shallow, large-deep, small-shallow and small-deep. A pool is large ifany of the pool dimensions (i.e., length, width, oblique) are greater than half the cross-section of the stream. Onemeter depth separates deep and shallow pools.

4. Sediment deposition parameter Range of scores: 0 - 20Expresses the amount of sediment that has accumulated in pools and the changes in stream bottom resulting fromthe accumulation. High levels of sediment deposition is a symptom of an unstable system that becomes unsuitablefor many organisms.

5. Channel flow status Range of scores: 0 - 20Expresses the degree to which the channel is filled with water. The flow status changes as the channel enlarges(a common symptom of urbanization) or as flow decreases as a result of diversion or storage in reservoirs.

6. Channel alteration Range of scores: 0 - 20A measure of large-scale changes in the shape of the channel as a result of human actions by straightening, lining,deepening for flood control, irrigation or navigation, or by bridges.

7.a Frequency of riffles (or bends) Range of scores: 0 - 20Expresses heterogeneity of the stream. Riffles provide a high quality habitat.

7.b Channel sinuosity Range of scores: 0 - 20Evaluates the meandering of the stream. A high degree of sinuosity provides for diverse habitat and fauna. Theabsorption of high flow energy by bends protects the stream from excessive erosion and flooding and providesrefuge for benthic invertebrates and fish during storm events. This parameter can be rated from maps.

8. Bank stability Range of scores: 0 - 10Measures whether the stream banks are eroded or have a potential for erosion.

9. Bank vegetative protection Range of scores: 0 - 10Measures the amount of vegetation on the bank and the near-stream portion of the riparian zone.

10. Riparian vegetative zone width Range of scores: 0 - 10Measures the width of natural vegetation from the edge of the stream bank out through the riparian zone. Thevegetative zone serves as a buffer for pollutants, controls erosion and provides habitat and nutrient input into thestream. An optimum width of the riparian zone is 4 times the stream width or greater than 18 meters.

_________________________________________________________________________

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Table 6.5 Physical habitat attributes (metrics) of the Ohio Qualitative Habitat Evaluation Index(QHEI) (Rankin, 1995). The maximum number of points of the QHEI is 100.

Metric Characteristics Maximum score

1. Substrate quality a. Two most predominant substrate types 20 ptsb. Number of substrate typesc. Substrate origind. Extensiveness of substrate embeddednesse. Extensiveness of silt cover

2. Instream cover a. Presence of each type in the reach 20 ptsb. Extensiveness of all cover in reach

3. Channel quality a. Functional sinuosity 20 ptsb. Degree of pool/riffle developmentc. Age/effect of stream channel modificationsd. Stability of stream channel

4. Riparian quality a. Width of intact riparian vegetation 10 ptsb. Types of adjacent land usec. Extensiveness of bank erosion/false banks

5. Pool/riffle quality a. Maximum pool/glide depth 20 pts b. Pool/riffle morphologyc. Presence of current typesd. Average/maximum riffle/run depthe. Stability of riffle/run substratesf. Embeddedness of riffle/run substrates

6. Local stream gradient 10 pts

.

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Figure 6.8 Relationship between the Ohio QHEI and theRBP HQ index (from Rankin, 1995).

Figure 6.9 shows that although there is a correlation of the overall fish IBI to the habitat integrityindices, the relationship is very poor when habitat is the only stressor (similar to the more remoterelationships listed in Table 3.1). Again, relating IBIs to habitat indices may only be a futileexercise. The weights of the metrics in the Ohio QHEI were calibrated to the regional conditionsand better reflected the modified stream conditions typical for Ohio. However, the 95% line, calledMaximum Species Richness relationship (see the subsequent section), does follow the increasingtrend of fish IBI with the improved habitat. The effect of stream modifications by impoundmentsis qualitatively depicted on Figure 6.10 showing the fish IBIs for Northern Illinois’ mostly modifiedwater bodies: the Upper Des Plaines River’s unmodified and impounded Dresden and Brandonpools, the Lockport pool on the Chicago Sanitary and Ship Canal (CSSC), impounded and flowingreaches of the Fox River, and the reference modified Green and Rock Rivers.

The modified reaches of the Lower and Upper Dresden Island and Brandon Road pools of the DesPlaines River and the CSSC are part of the Illinois River waterway, one of the busiest waterwaysin the nation. The impounded Des Plaines River is an effluent-dominated water body because itcarries most of the wastewater effluent and urban runoff flows from the entire Chicago metropolitanarea. The Brandon pool has problems with the low dissolved oxygen and the Upper Dresden poolis impacted by thermal discharges from a 1.3 MW power plant that is using once-through coolingwith a capacity cooling flow comparable to the entire flow in the river. Thus, the IBIs in the LowerDes Plaines River navigational pools are affected by effluent and urban runoff discharges andhabitat modifications (see AquaNova/Hey Associates, 2003).

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Figure 6.9 Performance of Ohio and USEPA habitatindices (from Rankin, 1995).

Of note are the results of the USEPA study on the Fox River that compared fish IBI 0.5 kmdownstream (free-flowing) and 0.5 km upstream (impounded) sampling points with respect tolocation of dams. The comparison of free-flowing and impounded sections of the river shows thatthere is a “penalty” in IBIs due to impounding of approximately 12 to 15 IBI points. This is alsosupported by the IBI measurements of the reference Green and Rock Rivers, which had fish IBIsin the range of about 38 to 48 while reference wadeable streams typically have IBIs from 55 to 60.

The problem with the RBP HQ and QHEI indices is the fact that the weights of the metrics aresomewhat arbitrary and may not reflect the actual weight of the effect of the metrics on IBIs,despite the fact that much scientific judgment and qualitative research went into establishing themetrics and their weights. Rankin’s (1995) assertion that metrics of habitat indices, and by the samereasoning those of the macroinvertebrate and fish indices (Miller et al., 1988), must be regionalizedis correct, but what should be the extent of the region? Is the stratification of weights spatial (e.g.,ecoregions and subecoregions) or vertical (e.g., stream order), or both? More focused researchshould answer the question of weights and of the underlying (Level I and II) stressors.

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Ohi

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Figure 6.10 Fish IBI for modified streams in Northern Illinois (fromAquaNova/Hey Associates, 2003)

Inclusion of flow variability into the habitat index

The scope of the habitat index as defined by Rankin (1995) does not directly include flowvariability. Flow variability (flashiness) will be reflected in several metrics such as embeddedness(2), flow velocity (3), sediment deposition (4), and bank stability (8). The biotic effect of flowvariability and the parameters expressing it were reported by Richards (1990), Poff and Ward(1989) and summarized in Detenbeck et al. (1998) and Poff and Ward (1990).

Poff and Ward (1989, 1990) developed criteria for detecting hydrologic disturbances (intermittentflow and flows exceeding an index of bankfull discharge). They pointed out that the ecologicallyrelevant temporal scale is multiannual or less (intraannual) for flow variability, i.e., extremely rarehydrological events (e.g., 25 or 100 year recurrence flood or drought flows) are not relevant. Basedon their conclusions, with the reasoning for the biologically-based frequency of allowableexcursions of water quality standards (once in three years) (USEPA, 1994), an ecological systemcan fully recover from non-catastrophic hydrologic disturbances in approximately the same time.

Poff and Ward (1989) analyzed 78 watersheds and their long term series of daily flows in almostevery state (Florida, Idaho and four New England states were not represented). All flow valueswere transformed by a natural logarithmic function (ln[x+1]) so that the flow series would beapproximated by the log-normal probabilistic distribution. Each set of transformed daily flow datawas then normalized by the logarithm of the long-term mean for the entire series. The authorsdivided the streams based on their hydrology into the following categories:

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• Ephemeral (Intermittent) Streams

< Harsh intermittent: long periods of zero flow and very low flow each year

< Intermittent flashy: high frequency of moderately seasonal floods

< Intermittent runoff: flooded less frequently

• Perennial Streams

< Perennial flash: high frequency of non-seasonal flooding

< Snow and rain: intermediate flood frequency: seasonal, less flow predictability

< Perennial runoff: less frequent flooding, less influenced by subsurface flow

< Winter rain: less flooding and less effect of groundwater flows

< Mesic groundwater: high flow predictability

< Snowmelt: predictable seasonal flooding

The variables used for the classification were:

• Stream setting variables: Basin area, mean annual flow, specific mean annual flow(mean annual flow divided by the basin area)

• Overall flow variability: Mean annual coefficient of variation, Colwell (1974)measure of predictability for periodic phenomena, proportionof total predictability comprised by constancy

• Pattern of the flow regime: Flood frequency, median interval between floods, medianduration of floods, two indices of flood predictability,median days on which floods have occurred over the periodof the record

• Extent of intermittency: Average annual number of zero flow days, average over allyears of the annual 24-hours low flow value divided by thegrand mean of the ln-normalized data

The Poff and Ward classification covers the entire US, i.e., arid desert streams, montane streamsand humid temperate and warm regions. However, the stream classification was mostlygeographical, e.g., all mountain streams were classified as “snow” and in Ecoregion I (glacial)covering the eastern part of the US (east of the Mississippi River), only three categories of streamswere pertinent: mesic groundwater, perennial runoff and snowmelt. The flow patterns of themajority of streams in northeastern and north central regions were characterized by snowmelt orrain. It is possible that the Poff and Ward classification scheme may not be suitable for Ecoregion

54

I because the streams are already categorized by the ecoregion. A new flow variabilityclassification may be necessary using parameters such as:

• Specific mean flow (geometric mean flow divided by basin area)

• Minimum flow characteristics (e.g., minimum seven days in ten years of flow)

• Frequency of bankfull flow

• Low flow variability (minimum low flow/geometric mean)

These parameters must be organized into Level II to IV classes. For example, if flow variabilitydirectly affects the biota, then it would be included in a Layer II risk metric. If the flow affectshabitat metrics (e.g., velocity, embeddedness), it would be considered a Layer III stressor.Establishing such classifications will be a goal of this research.

D. Fragmentation risk

Fragmentation risk is characterized by the interruption of migration of species that can belongitudinal (upstream/downstream) or lateral (channel/riparian-floodplain zone). Fragmentationcan be natural (e.g., waterfalls); however, the majority of fragmentation is caused by humans andincludes impounding and building of barrages for various purposes, diking, bridges and culvertsand channel lining.

The fragmentation risk has not been developed. Its magnitude can be estimated from Figure 6.10,which shows IBIs for streams that have been impacted by low head dams in northern Illinois. Theextent of fragmentation can be very significant in Northeastern US. Figure 6.11 shows low headdams in the New England coastal basin. Over 30 low head dams have been built on the CharlesRiver (Massachusetts) alone. Most low head dams were built more than a century ago and todaythey serve no purpose.

Fragmentation has been recently quantitatively recognized as an important risk (Hanski et al.,1996). Fragmentation can result from any factor (biotic or abiotic) that causes decrease in theability of species to move/migrate among sub-populations or between portions of their habitatnecessary for different stages of their life (e.g spawning migrations) and it can be both physical(e.g., biologically impassable culverts, dams, waterfalls, road crossings and bridges) and causedby pollutants (e.g., localized fish kills or a polluted mixing zone without a zone of passage or athermal plume or stratification). Thermal plumes may create longitudinal fragmentation by creatingzones that fish will avoid. Concrete lined segments (or culverts) may create supercritical flow withvelocities that may be too high for fish to traverse and lack resting places. Loss of riparianvegetation reduces cover along the banks, and increase predation risk for fish. Barriers tomovement of organisms and exchange of food, such as those mentioned above are one of the mostobvious sources of fragmentation. Refugia, serve the purpose of providing a source forrecolonization of disturbed habitats or aquatic systems affected by periodic abiotic stresses (Sedellet al., 1990). Independent abiotic population reductions caused by disturbance events (e.g., floods,droughts, toxic spills) may cause dramatic changes in communities, depending on the severity and

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Figure 6.11 Dams on streams in the New Englandcoastal basin (from Flanagan et al., 1988)

periodicity of their occurrencerelative to the intensity of resourcecompetition and predation. Habitatlinkages for dispersal are the mostimportant type of connectivitybecause the resultant gene flowcounteracts isolation due tof r a g me n t a t i o n ( No s s a n dCooperrider, 1994). Connecti-vity isthe opposite of fragmentation.

Some Layer II risks themselves maybecome measurement endpoints.For example, in the TMDL process,chemical water quality can simplybe compared to water qualitystandards if the impairment is onlydue to chemical contamination andthe standards are risk based and notpurely administrative.

Layer III - In-stream ExposureStressors

In-stream exposure stressors are themonitored or calculated series ofparameter values that constitute the

risks. In the case of habitat parameters, distinction between the Layer 2 metrics of the habitat indexand Layer 3 stressors is fuzzy and will be investigated. For example, is velocity a Layer 2 or Layer3 stressor? Our research may have to redefine and recategorize Layer 2 and 3 habitat parametersand metrics.

Layer 3 water quality parameters must also be defined and linked to the proper Layer 2 risks. Forexample, suspended sediment concentrations affect the biotic integrity in two ways. First, highsediment concentrations are toxic to aquatic biota, primarily to fish. Second, suspended sedimentmay settle and cause impairment to the habitat which will be reflected as embeddedness (or claycontent/texture of bottom sediments). Embeddedness is also related to flow parameters such asdepth and energy slope that define the bottom shear stress.

For toxic risk, time series and/or statistical characteristics of chemical water quality parameters areLayer 3 stressors.

In general, Layer 3 stressors are:

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• Physical – flow variation and impact of land use changes on flow, habitat impairmentlinked to flow variations (e.g., frequency of bankfull flows, ratio of base flow to meanflow), temperature variation

• Water quality (estimated and/or measured) – acidity/alkalinity, temperature, sediments,nutrients, toxics inputs and levels

• Sediment quality (estimated and/or measured)

The stressors will be stratified by the stream order and simplified by Rosgen’s stream classificationand surrounding land use (e.g., higher slope/higher velocity unmodified streams with be lesssusceptible to sediment contamination and will generally have a better habitat than impounded orlow velocity lowland streams).

Water flow, water quality and sediment stressors will be expressed in probabilistic terms (mean andstandard deviation, arithmetic or logarithmic). For ungauged streams we will use already developed(by USGS) relationships between these parameters (independent variables) and morphological andland use characteristics of the watershed. We will initially plot the flow data on a log-normalprobability graph and use the non-parametric Kolgomorov-Smirnov test to evaluate the adequacyof the log-normal probability distribution and test other distributions only if needed. It should benoted that for biotic effects, the high flows with a recurrence interval of less than five years areimportant while very high floods may not be relevant. Thus, we will develop statistics forrepresenting extremes of low (at the 7Q10) and medium high flows. Ratios among the variousstatistics will be useful for characterizing the flow regimes (for example, a high ratio of theextremes to the mean may indicate a watershed disturbed by urbanization).

Layer IV - Landscape Stressors

Landscape Descriptors and Emissions

The importance of landscape in connection with the integrity or aquatic water bodies is clearlyshown in Figure 1.1. The important connections between landscape and pollution include:

• Emissions of pollutants from various land segments. By definition of pollution, onlyemissions from lands altered by humans or use by humans for production or other uses maycause pollution. Pollutant loads from native unaltered lands are considered natural loads.These loads are increased, often by orders of magnitude, during land use transition such asdeforestation, wetland drainage, irrigation, or urbanization.

• Impact of landscape of storage of pollutants. Soils can safely retain or incorporate into thesoil structure and/or decompose over 99% of potential pollutants, such as nutrients(nitrogen and phosphorus), pesticides, and organic matter. Decomposition can occur bothunder aerobic and anaerobic conditions. The most effective storage is attributed to saturatedsoils - wetlands. However, this storage capacity may be exceeded, either as a result ofexcessive accumulation of a pollutant or pollutants or due to human interference, e.g., asresult of acid rainfall, wetland drainage. A change of the pollutant retention capacity can

57

be slow or sudden (Stigliani et al., 1991; Salomons, 1993; Stigliani and Salomons, 1995).As a result of the change, pollutants are released into ground or surface waters.

• Impact of landscape on assimilation of pollutants in receiving water bodies. Theassimilative capacity of receiving water bodies is also related to morphological landscapefactors. Higher velocity mountain streams have better aeration capacity than low land smallslope sluggish streams. Depth affects the reaeration and light and heat penetration. Bothvelocity and depth impact habitat.

The list of potential landscape descriptors is given in Table 6.6. The landscape description, suchas percent of various land uses and covers, is a system parameter describing the system but it canalso be related to emissions of a polluting matter (suspended solids, chemicals). Each descriptorhas some relevance to the integrity of the water body draining the watershed. However, as shownby Moss et al. (1987), reducing the number of descriptors from 28 to 5 did not decreasesignificantly the reliability of the estimates of the biotic community distribution. The closer theemitting land uses are located to a water course, the more they affect the aquatic habitat and theaquatic components. Regarding diffuse pollution the landscape emissions are expressed in variousdegrees of complexity ranging from unit loads, related to the land use character, to annual orseasonal emission rates, ascertained from long term meteorologic factors, soil characteristics, slopeand land cover, to medium complexity models, relating the emissions to the erosivity of individualrainfalls and leaching of chemicals from soil (Novotny, 2003).

The rate of change of the landscape indicators is an important indicator of pollution stresses. Figure1.1 identifies the land use transition or modification process. For example, increasing urbanizationimplies extensive occurrence of construction sites that emit extremely large sediment andassociated pollutant (e.g., phosphorus) loads. Deforestation increases sediment loads by orders ofmagnitude (Walling and Web, 1983). Wetland drainage results in a release of large quantities ofnitrogen by a change of organic nitrogen to nitrate by nitrification, a strictly aerobic process thatcan proceed only in aerated soils (Kreitler and Jones, 1975; Salomons and Stol, 1995). Exposure(aeration) of mine spoils releases acidity by oxidation of pyrite and chemical change from reducedand immobile metal sulfide complexes to oxidized metallic oxyhydrate complexes that release toxicmetallic ions into water (Salomons and Stol, 1995).

The historic changes of landscape parameter and the rates of changes can often be estimated fromaerial and satellite photographs and records that may date as far back as 50 years. In some cases,maps can identify changes over centuries. For example, most of southern Wisconsin and largeportions of Illinois and Indiana were covered by wetlands one hundred fifty years ago. Similarlythe watershed of the lagoon of Venice in Italy contained extensive wetlands transected by canals.The extent of wetland drainage and rate of change of this important ecological landscape indicatorcan be ascertained relatively accurately from maps and, later, from aerial photographs. The sameis true for the extent and rate of change of urbanization.

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Table 6.6 Landscape parameters and factors affecting the integrity of surface waters

Parameter Integrity relevance

Watershed morphological characteristicsArea GeneralAltitude TemperatureLatitude Temperature, snowmelt pollution Slope of land segments Erosion, suspended solids load, embeddednessDistance of disturbed land Delivery of pollutants, suspended solids load

segment from the water body

Watershed pedological and geological characteristicsSoil type and texture Erosion, suspended solids load Bedrock and type of bedrock geology Watershed buffering and vulnerability

Land use (in the watershed and in the riparian zone)% Urban and % Imperviousness Pollutant loads (emission), flow variability and channel/habitat stability,

temperature variability% Agricultural, crops Suspended solids and pollutant loads (emissions)% Forest Watershed buffering% Wetland Watershed buffering and pollutant immobilization % Area under construction Suspended solids and pollutant loads, embeddedness% Transportation Toxic pollutant loads (emission)Mining and mining spoils Source of acidity and metals

Stream morphologyStream order Species diversity and density, pollutant dilution in streams, habitatVelocity Sedimentation and aeration Slope Sedimentation and aeration, habitat, channel stability (erosion), sediment

and substrate texture, composition of organismsDepth Sedimentation and aeration, eutrophication, composition and diversity of

organismsFrequency of bankfull flow Channel stability, stream bank erosion

(channel flow capacity)Pool and riffle sequence Habitat qualityBottom substrate texture Spawning and shelter for fishOrganic content of sediments Sediment oxygen demand, nutrient releaseChannel alteration Fragmentation, habitat degradationRiparian vegetation, stream side cover Temperature, habitat shelter

Emission rates of pollutants from landscape are not easily measurable because they are related tometeorological factors. Therefore, unit loads often represent long-term averages back-calculatedfrom monitoring data in receiving waters or estimated by hydrologic models that were calibratedand verified by measurements from small uniform watersheds (Novotny and Chesters, 1981;Novotny, 2003). A methodology for determining graphical regional unit load estimates calledModel Enhanced Unit Loads (MEUL), shown in Figure 6.12, was developed and used in late 1970s

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Figure 6.12 Simulated sediment unit loads (MEUL) fromresidential land uses related to the totalimperviousness of the area and pervious surfacescovered by lawns (adapted from Novotny andBannerman,1979)

and early 1980s to estimate watershed-wide pollutant loads in the priority watershed program inWisconsin (Novotny and Bannerman, 1979).

Landscape Delivery of Pollutants

Not all lands emit pollution. Referring again to Figure 1.1, emissions from the four native lands,because they are natural, may not be considered pollution because pollution is caused by humans(based on Section 5 definition of pollution of the Clean Water Act). This does not mean that naturalemission may not cause a water quality problem. Erosion rates from arid lands are very high andsuspended solids content in systems draining arid streams can be very high, sometimes exceeding

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tens or even hundreds of thousands of milligrams per liter, as documented by Nordin (1962) on RioPuerco, an ephemeral stream draining an arid sparsely populated watershed in the northwest partof New Mexico. It is also known that streams draining wetlands contain low, but natural,concentrations of dissolved oxygen.

Hazardous lands are lands within a watershed that emit high non-point loads of pollutants, typicallyexceeding natural loads by orders of magnitude (Novotny, 2003). A key step in watershedclassification and finding the links between watershed attributes is locating the hazardous segments.This can be done today in the GIS environment relatively efficiently. The land segments that emitthe highest pollutant loads are listed in Table 6.7.

Table 6.7 Land uses and major associated pollutant types.

Land use Pollutant

UrbanConstruction sites during excavation and landscaping SedimentHigh density transportation Toxics, sedimentIndustrial ToxicsHigh density residential and commercial Toxics

RuralAnimal feedlots Organics, nutrientsIntensive agriculture on high slope (>3%) Sediment, nutrientsClear cutting of forests Sediment, nutrients

The proximity of the land to a water course is another important factor. This is expressed by thedelivery ratio which relates the pollutant yield in the watercourse to the upland emissions of thepollutant. This effect can be included in the model by the inventory of the hazardous lands locatedwithin the riparian zone. The width of the zone may vary from approximately 30 to 100 metersand/or by the existence or lack of a vegetated buffer between the land segment and the watercourse.

Organization of landscape descriptors in watershed vulnerability classification schemes

Detenbeck et al. (2000) identified many landscape descriptors for a watershed vulnerabilityclassification scheme with the goal of identifying those that are related to integrity. Watershedclassification schemes reviewed by Detenbeck et al. were classified as geographically-dependentor geographically-independent. Geographically-dependent classification, as the category implies,can be applied only to one geographic area, e.g., the Northeast, while the geographically-independent classification schemes are not limited to a specific place or region. The authors alsoadded classification schemes that were structurally or functionally based on a combination of thetwo.

Geographically-dependent classification schemes locate watersheds and categorize watershedcharacteristics in ecoregions and ecoregional units (Omernik, 1987; Omernik and Gallant, 1989).

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This system was used to rank the biotic integrity of lakes (Heiskary and Wilson, 1990) andbiological criteria for streams (Yoder and Rankin, 1999).

Geographically-independent classification can be constructed based on structural or functionallandscape, watersheds or ecosystem/community characterization related to the stressors orecosystem functions for aquatic ecosystems. They can include hydrology (e.g., flow variability) orsediment supply (see Table 6.6), or be related to ecological response.

Relating diffuse pollution to water body integrity

Relating point and diffuse pollution loads to water quality is usually done by calibrated and verifiedwatershed models. Water quality in this context is understood as a chemical and bacteriologicalcomposition of numerous water quality parameters while integrity is three dimensional andincludes, in addition to chemical and hydraulic/hydrological attributes, also habitat and aquaticbiota indices. Many attempts have been made to relate biotic integrity indices to diffuse pollutionand watershed impairment characteristics.

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CHAPTER 7

WATERSHED VULNERABILITY INDICATORS

Vulnerability Analysis

For most of the 1990s, the USEPA and its contractors (e.g., Center for Watershed Protection) weredeveloping indicators that could be used to assess the vulnerability of watersheds to degradation.At about the same time, the Water Framework Directive (WFD) of the European Union made theassessment of watershed vulnerability a fundamental requirement of the WFD-mandated waterquality control programs throughout Europe, extending from the original signatories of the EU toall candidate states that will be admitted into the EU in this decade.

A methodology for a simplistic watershed vulnerability analysis was advanced by the Center forWatershed Protection in a report/manual by Zielinski (2002). Zielinski based her methodology ona sole accounting of imperviousness within the watershed, which is based on the concept presentedin Figure 3.2. Essentially, all watersheds that are more than 25% impervious do not support thegoals of biotic integrity. Watersheds and sub-watersheds with imperviousness ranging from 11 to25% are classified as impacted streams. Based on the type of imperviousness (reversible,irreversible), the impacted watersheds also can be classified as restorable or non-restorable. Whilethere are no arguments against considering imperviousness as one of the key parameters, thisapproach and methodology obviously suffers from the inadequacy of using simplistic relationshipsbetween IBIs and a single surrogate parameter, as discussed in Chapter 3. Obviously, urbanizationis not the only stressor.

Index of Watershed Indicators

In 1997, US EPA’s Office of Water published the Index of Watershed Indicators, which wasrevised in 2002 (Spooner and Lehman, 1998; USEPA, 1997, 2002). The Index of WatershedIndicators contains two groups of parameters (Table 7.1): (1) those that describe the condition ofa water body and its watershed, and (2) those that describe vulnerability.

Condition parameters reflect the current status of the watershed and water quality. However, mostof the parameters that have been proposed by the USEPA in the Index of Watershed Indicatorsfocus on compliance of key water quality indicators with the existing standards or violations ofexisting NPDES permits (see Table 7.1). This follows the established philosophy of relyingprimarily on chemical standards in the assessment of the integrity status of a water body. A waterbody can be in compliance with the chemical water quality standards, yet the watershed and waterbody can be vulnerable and not meet the integrity status required by the Clean Water Act (e.g.,because of not having a balanced biota).

Vulnerability parameters, as defined in USEPA (1997, 2002) documents, are expected to showwhere discharges and other stressors impact the watershed and could, depending on the natural andmanmade factors present in the watershed, cause future problems to occur. Vulnerability, therefore,

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implies future problems while the condition parameters reflect the present status. Although theIndex of Watershed Indicators yields two single overall numbers (one for condition and the secondfor vulnerability characterizations), it can also be used to identify a specific category of a problemor problems. The EPA manuals provide charts and results in national maps of watershedvulnerability that can be viewed on and downloaded from the Internet (www.epa.gov/iwi/). Theindex, as it is designed, should not be used to place water bodies and watershed on the TMDLrequiring Section 303(d) list. A more rigorous assessment is necessary to justify TMDL(Committee, 2001).

Table 7.1 Index of Watershed Indicators (USEPA, 2002)

Condition Indicators

1. Assessed waters meeting all designated uses set in water quality standardsBased on the percentage of waters within a watershed that are meeting all uses, as established in 1994or 1996 EPA reports under the Clean Water Act Section 305(b)

2. Fish and wildlife consumption advisories Based on number of advisories for limits or prohibitions on consumption of fish and wildlife from the area

3. Indicators of source water quality for drinking water reservoirsBased on three sets of data describing the quality of drinking water sources:• State assessments of whether “water supply” designated use, as described in Section 305(b), is being

attained for surface water • Water system treatment and violation data as indicators of source water quality • Chemical concentration data indicating occurrence of chemicals regulated under the Safe Drinking

Water Act in source waters

4. Contaminated sediments Based on sediment chemical analyses, toxicity data and fish residue data (National Sediment Inventory)

5. Ambient water quality data (toxic pollutants)Based on ambient water quality data of exceedences of national criteria levels (1990-1996) of 4 toxicpollutants: copper, chromium (hexavalent), nickel, and zinc

6. Ambient water quality data (conventional pollutants)Based on ambient water quality data of exceedences of national reference levels (1990-1996) of 4conventional pollutants: ammonia, dissolved oxygen, phosphorus, and pH

7. Wetland loss index Based on data of losses of wetland area over and an historic period (1870-1980) and more recent period(1986-1996)

Vulnerability Indicators

1. Aquatic/wetland species at riskBased on number of species known to be at risk

2. Pollutant loads discharged above permitted discharge limit (toxic pollutants) Based on percentage of loads in excess of permitted loads for toxic pollutants

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Figure 7.1 Overall vulnerability of watersheds based on the Index of WatershedIndicators (from USEPA, 2002) expressing conditions and vulnerability.

Table 7.1 - Cont.

3. Pollutant loads discharged above permitted discharge limits (conventional pollutants)Based on percentage of loads in excess of permitted loads for toxic pollutants

4. Urban Runoff Potential Based on estimates of the percentage of impervious surface area

5. Index of Agricultural Runoff Potential Based on 3 parameters:• Nitrogen runoff potential index• Modeled sediment delivery to rivers and streams• Pesticide Runoff Potential

6. Population growth rateBased on degree of population growth

7. Hydrologic modification -- damsBased on relative volumes of impounded water

8. Estuarine pollution susceptibility indexBased on physical properties of an estuary and its likelihood of accumulating pollutants

________________________________________________________________________________

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The watershed assessment using the US EPA’s Index of Watershed Indicators relies primarily onthe State’s Section 305(b) reports and databases. The 305(b) water quality information is weightedmore heavily than the other indicators to emphasize its importance. This decision would imply thatthe weight of the indicators is administrative rather than factual. Values were selected for eachcondition indicator, which, in the EPA’s professional judgment, represent an appropriate basis todescribe the aquatic resources within the watershed as having good quality, fewer problems or moreproblems. Similarly, for each vulnerability indicator, the EPA selected values that were believedappropriate to differentiate “lower” from “higher” vulnerability. For most indicators, the EPAestablished a minimum number of observations necessary to assign a score. An overall index iscalculated for each condition and vulnerability groups of indicators. Indicator #1, Assessed RiversMeeting All Designated Uses, is weighted 6 times more than the other 14 layers of indicators. Allother indicators have the same weight. The watershed vulnerability map is shown in Figure 7.1.

The Index of Watershed Indicators was conceived of by experts and not by analyzing and modelingthe stressor-endpoint relationships. Some parameters should be considered in developing the modellinking stressors to biotic endpoints.

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CHAPTER 8

RISK PROPAGATION FROM STRESSORS TOASSESSMENT ENDPOINTS – A MAZE OF

PROBABILITIES

Linking Stressors to Biotic Endpoints – Risk Propagation Model

This model is based on the layered hierarchical concept shown in Figure 6.1. In this model, eachindividual metric of the IBI is related by a number of stressors and risks, including the risk (orindex) of habitat impairment (metric by metric), water quality/pollution risk (parameter byparameter) and sediment contamination risk (parameter by parameter). As previously defined, anecological risk is defined as the probability that a species will be eliminated from the system oradversely affected as a result of the stressor (Parkhurst et al., 1996; USEPA, 1996; Novotny, 2003).Similar definitions of risk can be applied to sediment contamination where the affected speciescould be limited to benthic fauna and periphyton. Water and sediment quality parameters that meetthe established water quality standards can be excluded from the analysis. A modified andexpanded concept of the Maximum Species Richness (MSR), originally proposed by Faush etal.(1984) to account for the effect of stream orders on the species number and richness, providesthe link between the Layer II measurement endpoints (risks) and the Layer I IBI.

This concept was later expanded by Lyons (1992), Rankin and Yoder (1999), and others to otherparameters, including urbanization. In the MSR evaluation, the effect of a single stressor isretrieved by drawing a 95-percentile line through the IBI data that would, on a plot, range from zeroto some maximum value. Since these effects are not linear, as shown on Figure 8.1, simplestatistical analyses (e.g., multiple regression) may not reveal the effect. Various patterns of MSRrelated to drainage area (not a stressor) were included in a chapter by DeShon (1995). Rankin andYoder (1999) then provided a methodology for MSR estimations with graphs for the MSRinvolving drainage area, habitat quality, dissolved oxygen and copper. MSR recognizes the factthat, under certain single stressor conditions, there is a limited maximum number of species withina taxa that will be found in the stretch of the river. However, because the number of species is aresponse to multiple stresses, the actual number of species can be anywhere between zero and themaximum. The actual number is then related to the effects of the other risks and also includes arandom component.

This concept was applied to linking Layer I (IBI) and II (risks) parameters by Bartošová (2002).A general response of an IBI metric to a risk or stressor is shown in Figure 8.1. The MSRrelationship A is the most common. Below a certain magnitude of the stressor, the effect is minimaland after a threshold is exceeded, species richness and compositions are adversely affected. Suchimpacts are typical for toxic compounds. Situation B is similar, but the negative effect has a

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Figure 8.1 Patterns of Maximum Species Richness lines forvarious stresses

Figure 8.2 Risk estimation for the mayfly taxaof the macroinvertebrate ICI by clayin substrate parameter forSoutheastern Wisconsin streams.From Bartošová (2002)

constant value after a certain limit. This case may occur with stressors such as impoundments,quality and/or presence of riparian vegetation, and bank modification by lining. Situation C istypical for metrics representing tolerant species or effects of nutrients. Function D may be typicalfor the direct DELT (disease, erosion, lesions, and tumors) risk metric.

Bartošová presented several examples of the MSR concept in analyzing the effect of urbanizationon the key taxa of the benthic macroinvertebrate index. An example is shown in Figure 8.2. Theline represents the 95 percent envelope as proposed in Fausch et al. (1984). The MSR line,normalized by the intercept at the zero stress, represents the probability of the taxa survival. Sincethe risk is the probability of survival, the MSR line could also be considered the risk to taxa by asingle stress.

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The MSR lines can be converted into the probability of taxa survival, pS, by dividing the numberof taxa by the number of taxa found at a reference site. The probability of taxa survival in a habitat,pS

(taxa/habita),can be calculated for each individual metric (taxa) and habitat measure (habitat). Thejoint probability of taxa extinction, pE

(taxa),is then estimated assuming an independent effect ofselected habitat measures as:

( )p pEtaxa

Staxa hab i

i

N( ) ( | _ )= −

=∏ 1

1

where pE(taxa) is the joint probability of taxa extinction, pS

(taxa|hab_i) is the probability of taxa survivaldue to habitat I and N is the total number of habitat characteristics influencing the taxa. Theassumption of independence of individual habitat characteristics is used as a starting point. Theproper relationship between risks due to individual factors will be identified during further research.

The first-cut layered model for the invertebrate ICI was then developed from the database providedby the Wisconsin Department of Natural Resources for Southeastern Wisconsin. The layeredhierarchical risk propagation model is

Top layer ICI ap bp cWQ dSed eEtaxE

Egavg

c= + + + +

where ICI is the index of biotic integrity, pEtaxE and pE

gavg are the risks due to habitat impairment tomayfly taxa and a geometric mean of all habitat risk components respectively, WQc is thesummation of chronic risks due to water column contamination, and Sed is the summation of thechronic risks due to contamination of sediments. These risks were found to be significant. Note thatICI increases with the impairment while IBI (fish) decreases. This model assumes that the risks areadditive. The possible synergy of the risks should be investigated.

Lower layer - The probability of taxa survival has been calculated for each individual metric andhabitat measure. The joint probability of taxa extinction has been estimated assuming anindependent effect of selected habitat measures as:

( )( )( )p p p pEtaxT

StaxT a

StaxT c

StaxT r( ) ( | ) ( | ) ( | )= − − −1 1 1

( )( )( )p p p pEtaxTotal

StaxTotal a

StaxTotal c

StaxTotal r( ) ( | ) ( | ) ( | )= − − −1 1 1

( )( )( )p p p pEtaxE

StaxE a

StaxE c

StaxE r( ) ( | ) ( | ) ( | )= − − −1 1 1

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R2 = 0.2385

0

5

10

15

20

25

30

35

40

-4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0log (sediment risk)

IBI S

core

Figure 8.3 Example of theeffect of a single stressor (risk)on ICI.

predicted

obse

rved

0 10 20 30 400

10

20

30

40

Figure 8.4 Plot of observed ICI versus ICIpredicted by the final layered regression model(Bartošová, 2002).

where pS(taxE|a) is the joint probability of mayfly taxa survival due to percent aquatic vegetation a,

pS(taxE|c) is the joint probability of mayfly taxa survival due to percent clay in substrate c, and pS

(taxE|r)

is the joint probability of mayfly taxa survival due to percent of rubble or smaller substrate r.Similarly, pE

(taxT) is the joint probability of caddisfly taxa extinction and pE(taxTotal) is the joint

probability of total taxa extinction.

The methodology and the model are described in detail in Bartošová (2002). Figure 8.3 shows anexample of the relation of the IBI (benthic) to a single stressor parameter, and Figure 8.4 shows thatthe complete risk propagation model provides a relatively accurate estimation of the benthic ICIon a regional scale (southern Wisconsin). A Neural Network model is being used to reveal theserelationships for more complex relationships and larger source databases by the NortheasternUniversity/University of Wisconsin-Milwaukee research team. The results of this research will bepublished subsequently.

Interactions among stressors

The key interactions are:

• Sediment-nutrient interactions. Increased turbidity reduces the water body light penetrationand suppresses eutrophication; controlling sediments may trigger a switch from algae tomacrophytes. Elevated nutrient levels in the shallow upstream reaches create suspendedbiomass that settles in the impounded reaches, resulting in more algal growth, dailydissolved oxygen fluctuations and sediment oxygen demand in slow reaches.

• Sediment-toxic interactions. Sediment adsorbs and inactivates availability of toxiccompounds; however, presence of insecticides and insecticide by-products in the sedimentmay affect the benthic macroinvertebrates.

• Nutrient-toxin interactions. Toxic compounds can affect the growth of phytoplankton andmacrophytes.

• Altered habitat-flow-sediment-pollutant interactions. Channelization and impounding ofthe river diminish the conditions for early life forms. Navigation by large barges stirs the

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sediment and moves it downstream at a rate that is faster than that without navigation. Flowfluctuation and peaking, caused by peaking hydropower plants and lock operation, flushsediment and periphyton from the system.

• Temperature. Temperature affects many biotic and chemical water quality parameters, suchas dissolved oxygen, toxicity, rates of biochemical reaction in the water body, feeding,oxygen uptake and reproduction of fish, benthic invertebrate and others. See theTemperature chapter of USEPA’s (1986) water quality criteria manual or Krenkel andNovotny (1980).

Uncertainty and its sources

Hierarchical models of biotic integrity have a high degree of randomness. All parameters andfunctions in the model are essentially probabilistic entities that cannot be accurately predicted andcan, at best, be expressed in terms of their means and standard deviations. Many are cross-correlated. Uncertainties are relevant for the input stressors as well as the functions linking thestressors to the endpoints and, finally, in determining (monitoring) or predicting the endpoints.Uncertainties are temporal and spatial.

Uncertainty is inherent in all models that attempt to represent natural phenomena. Uncertainty andvariability are almost synonymous terms if the time series or the simulated variables contain arandom unpredictable component. The random variation is caused by many factors, such as errorsin monitoring and field measurements, and by random noise in the inputs of the model of thesystem. Within the system, the random fluctuations are modified and buffered by the system'sstructural and functional components and by autocorrelations.

Sometimes the source terms for a stressor are not well-defined, which introduces another level ofuncertainty, especially with prediction of future levels (Hunsaker et al., 1990). If the system isanalyzed as being at equilibrium (steady state), uncertainties will increase because they will nowinclude the temporal fluctuations. These fluctuations both transient and periodic input variations.For example, a transient toxic shock on a water body or daily fluctuations of the dissolved oxygenin a nutrient-enriched stream may have significant effects on biotic compositions, which could beaccounted for in a quasi-steady system by introducing a “variability” parameter. Purely randomfluctuations of inputs and system functional parameters may propagate and be modified from purelyrandom (white noise) to autocorrrelated series. High frequency random fluctuations can be bufferedby the system and dissipate as they pass thorough the system (Bendat and Piersol, 1971; Box andJenkins, 1976).

To begin the discussion and analysis of the randomness and uncertainty, Figure 8.5 shows themagnitude of the standard deviation of the IBI measurements. The figure shows that the standarddeviation decreases with the magnitude of the IBI, i.e., as the ecological water body systembecomes better, more healthy, it also becomes more stable.

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Figure 8.5 Standard deviation of the measurements of theIBI as a function of the magnitude of IBIvalues of two water bodies in Illinois. Eachvalue represents one site. From Karr et al.(1987).

SS

Kttty

x22

1 2=

+∆

The model, as its prototype, receives inputs, generally in a form of time series of stimuli, fromoutside of the system. Concurrently, the parameters of the system are continuously changing. Asstated previously these changes are made of pulses, steps, trends, periodicities and random wideand narrow bend components.

The system buffering is due to accumulation and autocorrelation, lag times as the stimulus passesthrough the system and attenuation. For example, Novotny (1977) developed a simplified formulaefor attenuation of input variability into a completely mixed system with decay (in this particularpaper the completely mixed system was an activated sludge unit; however, the formula is applicableto any completely mixed system such as a lake). The formula is presented herein as an example.

If X is an input time series of a random signal with a bandwidth (e.g., duration of compositesampling) of )t and variance of the signal of Sx

2, then the variance of the output signal, Sy2, from

the system is

where t is the residence time in the system (e.g., volume/flow) and K is the decay (attenuation)coefficient in time-1.

73

Such relationships may be useful when one analyzes mass balance processes, such as waterquality of a water body. However, in a hierarchical ecological modeling, the model itself isessentially a system of "black" and "gray" box subunits, whereby a black bock denotes asubmodel with an unknown internal structure and gray box is a subunit for which the structureis known only partially and such unit is strongly influenced by the randomness of its behavior intime.

The variability and its attenuation or gain in the hierarchical model will be investigated by theArtificial Neural Network model describing the system. In most simple applications, sensitivityanalysis can be performed to reveal the input parameters that have the greatest impact (seeworks of Moss et al. (1987) and Park et al. (2002)). More advanced methods are also availableand will be investigated.

Parsimony

Moss et al. (1987) found that the relationship between the landscape parameters and the bioticendpoints gives about the same results and only slightly reduced accuracy if 5 input parametersare used instead of the original 28 parameters. Similarly, Bartošová (2002) was able, by aprocess of elimination and sensitivity analysis, to use only a few key parameters to describerisks and their impacts on the macroinvertebrate Index of Biotic Integrity and its key metrics.

The model must be parsimonious. Some parameters, due their attenuation will have minimalimpact on the biotic endpoints or their impact can be expressed by a constant. Other parametersimpact may be orders of magnitude less than that of key determining parameters. The researchmust identify the key inputs and functions and develop a parsimonious model.

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