LINKING POLLUTION TO WATER BODY INTEGRITY

Transcription

1 Center for Urban Environmental Studies Northeastern University, Boston, MA 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. R to Northeastern University from the USEPA/NSF/USDA STAR Watershed Program Bernice L. Smith EPA Program Manager Boston, MA May 2004

2 Acknowledgments The research contained in the technical report was partially sponsored by the US Environmental Protection Agency STAR Watershed Program by Grant No.R to Northeastern University. The author greatly appreciates this support. The findings and conclusions contained in this 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 editing and finalizing the report.

3 Table of Contents Table of Contents... i List of Figures... iii List of Tables... iv Chapter 1 INTRODUCTION...1 History of U.S. Water Pollution Control...1 Non-Point Source (Diffuse) Pollution...2 Chapter 2 IMPACT OF DIFFUSE POLLUTION ON INTEGRITY OF WATERS...5 Watershed Integrity...5 Expressing Integrity of Aquatic Ecosystems Endpoints...5 Chapter 3 RELATING ENDPOINTS TO HUMAN STRESSES...9 Human Effects on Communities...9 Simplistic Relationships...10 Chapter 4 ECOSYSTEM HIERARCHICAL SPATIAL AND TEMPORAL SCALES...17 Ecosystem Hierarchy...17 River Continuum Concept...19 Ecosystem Temporal Scale...21 Expressing Variability...22 Buffering Capacity of a System...24 Biotic Responses to Time-Variable Stresses...25 Substratum (Benthos) Effects...25 Human Impacts on Substrate...26 Chapter 5 MODELING ECOSYSTEMS WITH DYNAMIC MULTIVARIATE APPROACHES...27 Applying Multivariate Models to Ecological Systems...27 Multi-layer Models...29 Selection of endpoints...30 Development of the Model Structure...31 Chapter 6 i

4 MODEL BUILDING...35 Selection of Submodels (Functional Links)...35 Model structure...35 Layer I - Assessment Endpoints...36 Layer II - Risks...39 I. Water Column Risks...40 II. Sediment Risk...44 III. Habitat Risk...45 IV. Fragmentation Risk...54 Layer III - In-stream Exposure Stressors...55 Layer IV - Landscape Stressors...56 Organization of landscape descriptors in watershed vulnerability classification schemes...60 Relating diffuse pollution to water body integrity...61 Chapter 7 WATERSHED VULNERABILITY INDICATORS...63 Vulnerability Analysis...63 Index 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...67 Interactions Among Stressors...70 Uncertainty and Its Sources...71 Parsimony...73 References...75 ii

5 List of Figures Figure 1.1 Watershed, land use changes and other impacts on water quality...2 Figure 2.1 Principal factors and components that comprise the integrity of surface waters.. 6 Figure 3.1 Behavior of IBI metrics along a stressor gradient...9 Figure 3.2 Relationship between macroinvertebrate IBI and percent imperviousness...10 Figure 3.3 Scatter plot of IBI scores vs. percentage of urban land use...13 Figure 3.4 IBI as a function of % urbanization and % urban riparian forest...14 Figure 4.1 Stream ecosystem cross-section...18 Figure 4.2 The River Continuum Concept...20 Figure 4.3 Dissolved oxygen variations in three Wisconsin creeks Figure 4.4 Cumulative probability distribution of annual DO distributions...24 Figure 5.1 Concept of a multivariate/multimetric ecological model...28 Figure 5.2 Conceptual model of the primary external stressors and internal structure of the integrity of stream aquatic biota...33 Figure 5.3 Concept of the stressor-risk-end point propagation model...33 Figure 5.4 Concept of the links between stressors, exposure, and response...34 Figure 6.1 Schematic of the multilayer risk propagation model...36 Figure 6.2 Algae population shifts with temperature...39 Figure 6.3 Plot of a water quality parameter and assessment of probability of compliance with its water quality standard...41 Figure 6.4 Ecological risk assessment for stormwater impacts...42 Figure 6.5 Acute values for determination of risk of copper to aquatic biota...43 Figure 6.6 Dominant fish assemblages related to stream morphology...46 Figure 6.7 Flow, depth and recurrence interval of flows for natural stable channels...46 Figure 6.8 Relationship between the Ohio QHEI and the RBP HQ index...50 Figure 6.9 Performance of Ohio and USEPA habitat indices...51 Figure 6.10 Fish IBI for modified streams in Northern Illinois...52 Figure 6.11 Dams on streams in the New England coastal basin...55 Figure 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...59 Figure 7.1 Overall vulnerability of watersheds in the U.S Figure 8.1 Patterns of Maximum Species Richness lines...68 Figure 8.2 Risk estimation for the mayfly taxa of the macroinvertebrate ICI by clay in substrate parameter for Southeastern Wisconsin streams...68 Figure 8.3 Effect of a single stressor on ICI...70 Figure 8.4 Observed ICI versus ICI predicted by the final layered regression model...70 Figure 8.5 Standard deviation of IBI measurements as a function of IBI values...72 iii

6 List of Tables Table 3.1 Surrogate parameters for pollution used in simple ecological biotic integrity statistical models...12 Table 4.1 Time-variable ecological stresses and their impacts...22 Table 5.1 Characteristics of good assessment endpoints...31 Table 6.1 Metrics used in assessment of fish communities...38 Table 6.2 Metrics of the Index of Biological Integrity for Benthic Macroinvertebrates...38 Table 6.3 Metrics of the Ohio Invertebrate Community Index (ICI)...38 Table 6.4 Description of habitat parameters used in the Rapid Bioassessment Protocol.. 49 Table 6.5 Physical habitat attributes of the Ohio Qualitative Habitat Evaluation Index...49 Table 6.6 Landscape parameters and factors affecting the integrity of surface waters...58 Table 6.7 Land uses and major associated pollutant types...60 Table 7.1 Index of Watershed Indicators...64 iv

7 History of U.S. Water Pollution Control CHAPTER 1 INTRODUCTION More than fifty years ago Aldo Leopold, a pioneer of land and watershed conservation, wrote a paradigm for watershed protection and conservation (Leopold, 2001): A thing is right when it tends to preserve the integrity, stability, and beauty of the biotic 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), whose major goal is restoring and maintaining the chemical, physical, and biological integrity of the Nation s waters. Section 5 of the CWA also defined pollution as anything that downgrades the integrity 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 introduction of foreign species and by other human actions. Leopold also extended the rule of environmental ethics 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 includes interactions between human and nonhuman biotic system. This connection is important to diffuse pollution abatement because it means that land and water are intertwined in the general ecological system 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 use of 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 discharges of pollutants first at the point of discharge to the water body. These actions involved primarily control of point sources by building wastewater collection and treatment systems. The objective of point source abatement was improvement of water quality expressed by chemical parameters such as dissolved oxygen, BOD, ammonium, or suspended solids. During the last quarter of the 20 th century, starting with the passage of Water Pollution Control Act Amendments (Clean Water Act) in 1972 and the international effort to clean up Great Lakes, abatement of non-point sources of pollution became a part of the picture. The creators of Clean Water Act recognized, in Section 208 of the Act, that pollution control efforts must be conducted on a watershed scale. During the 1970s many watershed-wide plans were prepared by states that included considerations of both point and non-point source pollution, and all public wastewater treatment plants proposed in 1970s had to be included in Section 208 plans. Regarding, non-point source pollution, the methodologies of assessment and abatement were in their infancy and very few non-point source pollution controls 1

8 were realized as a result of Section 208 plans. By the end of the last century, non-point pollution was 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 holistic view. Following Leopold s paradigm, a water body and its watershed are part of the same system and streams and rivers reflect the landscape they drain (Hynes, 1975; Poff and Ward, 1990). The spatial 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 and time (Ward, 1989; Poff and Ward, 1990). Hydrologically, pollution can enter surface waters by overland/small channels flow, from atmospheric deposition and by discharge of groundwater from the shallow aquifer. Human action can change the proportions and pathways of diffuse pollution and 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 that downgrades the integrity of the water bodies. Pollution is then transported from the source to the Figure 1.1 Watershed, land use changes and other impacts on water quality (adapted from Novotny, 2003) river overland or via shallow groundwater aquifers (Novotny, 2003). The water bodies are 2

9 themselves used for many purposes including aquatic life protection and propagation, contact and non-contact recreation, water supply, navigation, power production, flood conveyance, and wastewater disposal. Overuse of water resources and overuse or misuse of land and land use conversion throughout the watershed generate pollution that, along with point sources directly discharging into the water body, impairs the integrity and diminishes beneficial uses of the water body (Figure 1.1). Definitions of non-point source pollution with examples were included in Novotny and Olem (1994) and Novotny (2003). Diffuse pollution, the most pervasive type of pollution, is difficult to manage and control. Diffuse pollution can be local, regional and transboundary. Recent definitions of diffuse pollution have attempted to overcome the ambiguities of previous legal definitions of point and non-point sources in 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 seepage of nitrate from agricultural land into underlying groundwater, pollution from farm fields and silviculture, or atmospheric deposition), together with the large number individually minor point sources such as forestry channels, field drains from farmland and urban surface runoff outfalls that collectively deliver significant contamination to the aquatic environment. Mobilization and delivery of pollutants are often dependent on weather conditions and may be influenced by soil type and surface cover. Simplistically, diffuse pollution sources may be individually minor but collectively significant, distributed in a diffuse manner throughout the watershed. Diffuse pollution is therefore associated with many dispersed sources, but there are often aggregations of pollution sources within a catchment and hierarchies of risks can often be constructed. Diffuse pollution should be differentiated from natural loads of chemicals from dissolution of soil minerals, natural erosion or natural content of precipitation. The term pollution has a broader meaning embedded into the CWA, meaning an impairment of integrity caused by humans. In a broader meaning, this term pollution, as defined in the CWA, includes: excessive loads of pollutants from point and diffuse sources (sediment, nutrients, biodegradable organics, toxins, heat); physically adverse alterations of the water body integrity such as channel lining and straightening 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 or temperature magnitudes and variability. Although the latter cases do not include discharges of pollutants, 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 large water 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 of nutrients 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 the Black Sea, the Po River for the Adriatic Sea, the Susquehanna and Potomac Rivers for the Chesapeake Bay, and the Mississippi River for the Gulf of Mexico. In developing countries, increasing population and resulting migration are leading to megacities that have poorly functioning or non-existent sewerage systems, placing severe strains on local water bodies. Furthermore, deforestation of subtropical and tropical forests is a severe diffuse pollution problem. One root cause is population increases that drive impoverished populations to the practice 3

10 of slash and burn agriculture, sometimes subsidized by governments. Deforestation is also caused by the demand for cheap wood at a price that does not include the cost of damage to the forest and the environment. Population and economic pressures in developing countries lead to intensive and unsustainable 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 resource value of local surface water bodies and groundwater aquifers. At the end of the last century in the US, more than 50% of receiving water bodies were not meeting their water quality goals. An even more severe situation can be found in other countries. Because past cleanup efforts focused primarily on point sources rather than on diffuse pollution, both aquatic life and human health are affected in the present. Many aquifers and drinking water reservoirs have been contaminated by nitrates and surface waters by algae and trihalomethane precursors. Recreation opportunities on rural streams that fifty years ago exhibited good water quality have diminished because of diffuse agricultural pollution. In addition, on a local scale in and around major urban areas, metals and other toxic substances are major contamination issues, especially in sediments. Some problems are attributable to past discharges that have been either reduced or discontinued but remain a legacy issue in sediments and contaminated soils of flood plains and watersheds. Such cases include PCB contamination of sediments. 4

11 CHAPTER 2 IMPACT OF DIFFUSE POLLUTION ON INTEGRITY OF WATERS Watershed Integrity The ecological status or health of the water body, called integrity, has been defined as the ability of the water body ecological system to support and maintain a balanced integrated, adaptive community or organisms having a species composition, diversity and functional organisms comparable 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 aquatic ecosystem, such that an attainment of the pre-disturbance ecologic conditions of the watershed and the water body is impossible (Committee, 2001). Establishing the ecological potential of the water body while considering irreversible and reversible changes in the watershed is the goal of the European Water Framework Directive (WFD) and also of the US watershed management programs required by the Clean Water Act (CWA). There are multiple root causes of damages to the ecological status of surface and groundwater resources (impairment of integrity) and their diminished uses for humans (Figure 2.1). While nonpoint loads of pollutants from the watershed and direct point source discharges are major causes of damage, another major cause is habitat degradation by stream modification and change of lands surrounding the water body. These stressors create a risk or a probability that aquatic species indigenous to the water body will disappear. At the same time, the stressors also may cause increased risk to public health by people eating contaminated fish, drinking contaminated water and contracting gastrointestinal disease after using the water body for swimming and other contact recreation. The ultimate result is the degradation of the aquatic ecological system exhibited as the disappearance of species of organisms that would otherwise thrive in the unimpacted water body and 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 and Rankin, 1999; Yoder et al., 2000). Stressor indicators include activities that add external (allochthonous) loads that impact but may 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 use changes, stream modification, and other large scale influences that generally result from anthropogenic activities. A disruptive stressor that can cause a damage or an adverse change of integrity is called a hazard (Hunsaker et al., 1990). Source terms imply qualitative and 5

12 quantitative descriptions of the stressor. If these stressors are distributed over the watershed and 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 risk to the resident biota. A risk is a numeric value assigned to an exposure stressor that expresses a probability that the population sizes and diversity of the resident organisms will be degraded and some organisms will be lost from the system, due to either acute or chronic toxicity effects or to habitat degradation. Response indicators are the direct measures of the ecological status (integrity) of the water body. 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 of stresses throughout the ecosystem. Figure 2.1 Principal factors and components that comprise the integrity of surface waters (adapted from Karr et al., 1986) 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 be protected or improved. The output of the assessment and modeling effort is a probability that the endpoint will be improved or impaired or remain steady. Reference environments or systems are systems of a similar character to the investigated system with the least human impact. 6

13 Ecosystem endpoints are numerous. In earlier water quality studies when protecting the health of fish was the primary goal, the endpoint was the concentration of dissolved oxygen (DO) because it was known that fish kills were observed if the DO concentration dropped below some threshold value (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 absence of 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 that has these three dimensions: Physical integrity implies habitat conditions of the water body that would support a balanced biological community. Chemical integrity is the chemical composition of water and sediments that would not be injurious to the aquatic biota (and to humans). Biological integrity describes a composition of aquatic organisms that is balanced and resembles or approaches that of unaffected similar water bodies in the same ecoregion without 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, toxicity bioassays Biotic evaluation using standards for pathogens, and biotic indices For aquatic life use considerations, the community and population response parameters represented by the indices of biotic integrity are considered the principal response indicators. Based on the multidimensional 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), which includes flow, hydrology and habitat structure parameters, chemical (water and sediment composition, including temperature), and biological parameters. Indices of Biotic Integrity (IBIs) for assessing this three dimensional integrity have been developed and implemented (Barbour et al., 1997, 1999) in the US, originally in the Midwest, but the use has spread all over the North America. In the US, both fish and macroinvertebrate community composition and habitat assessment 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 and Marson, 1908) has almost a 100 year tradition. Similar to the US, almost every country in Europe uses 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 the IBIs 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 eat contaminated fish, drink water drawn from the resource, or be affected by ingestion or skin 7

14 exposure during contact recreation. Humans are also a cause of degradation of integrity by generating pollution. As human impacts increase, the health of the ecosystem decreases, changing to a sick ecosystem dominated by a few tolerant species that may develop to unsustainable numbers. The endpoint of the maximum human influence is an ecosystem without life. Based on Leopold (2001) and the Clean Water Act paradigms, such a state is not acceptable. On the other hand, many water bodies irreversibly impacted by pollution cannot be fully returned to their predevelopment health. Thus, a measure of what is acceptable for these irreversibly modified and impacted streams must be developed. Karr (1996) and Karr and Chu (1999) define two criteria that would set the threshold for whether a loss of species is acceptable. First, the human activity should not adversely alter the long term sustainability of the resource to provide goods (e.g., fish) and services (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 water body 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 water body. For example, water supply industries and even agencies may not be concerned with biotic integrity of the water body as long as chemical parameters are suitable for water supply or can be adjusted by treatment. Irrigators may worry about salt content and several major chemical parameters. However, a healthy ecology of the water body is a necessary prerequisite for most direct human (drinking, contact recreation) and aquatic life uses. The biotic integrity indices reflect long term natural and anthropogenic impacts and are in a state of equilibrium with allochthonous and autochthonous stresses and the state of the watershed. Chemical integrity assessment has some drawbacks. First, it is not possible to evaluate all chemicals and their synergetic effects on biota. Second, most sampling and monitoring programs are not continuous; in fact, samples are taken and analyzed infrequently and, typically, not during the periods of the greatest stress. However, standards developed for chemicals are related to the risk that sensitive species will be adversely affected and could disappear from the ecosystem. Chemical standards adequately protect the most sensitive species. Biotic criteria, on the other hand, reflect the long term effects of all stresses, but the causative stressors and factors are difficult and sometimes impossible to determine without chemical and physical assessment. Thus, all three categories of assessment must be conducted. US EPA requires an independent applicability of the three categories of assessment; i.e, if any one category of assessment indicates impairment, then the 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 biotic evaluations document that the composition of species resembles the non-impacted reference condition despite some prior violations of chemical standards, the integrity status is attained and the 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 of the number and tolerance of fish species in a pre-specified reach of a stream. The fish IBI includes evaluation 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 similar composition. 8

15 CHAPTER 3 RELATING ENDPOINTS TO HUMAN STRESSES Human Effects on Communities The composition, diversity and density of organisms are related to various stressors. Most previous research related the IBI metrics to a single dominant stressor. Figure 3.1 shows the concept of the effect of human-induced stress on the metrics of the IBI. The human disturbance impacts on various taxa and metrics of the Index of Biotic Integrity were extensively presented and discussed by Karr and Chu (1999). Figure 3.2 shows the relationship of the IBI to the most widely-used surrogate stressor parameter, the percent imperviousness of the watershed. The concept is simple and defensible. With the increased stress, sensitive (intolerant) native species will be replaced by tolerant species that in the undisturbed systems were either present in smaller numbers due to predation and competition or were not present at all (invasive species). Typically, a healthy system will have a large number and diversity of species but smaller number of individuals within each species. A stressed system will have a smaller number of species dominated by those most tolerant to the stress that may develop in larger mass and numbers. At higher levels of stress, the health of all 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; Plafkin et al., 1989; Barbour et al., 1997, 1999) expresses numerically this sequence. Figure 3.1 Behavior of IBI metrics along a stressor gradient (after Yoder, 2002) 9

16 Finding the relationship of a biotic endpoint to hundreds of stressors in the water body, in the atmosphere and throughout the watershed may not be as complex as mapping the DNA sequence but is far from simple. The endpoints are complex assemblages of metrics that were selected to best represent 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 of IBIs. These organisms respond to their immediate stresses such as lack of food, exposure to toxic chemicals, elevated temperature and lack of adequate habitats. The organisms do not directly respond to stresses in the watershed, such as diffuse pollution, or in the atmosphere, such as acid rain or PCBs. These stresses are transmitted through various pathways, modified and attenuated. The stresses may be long-term (steady), transient or random. Figure 3.2 Relationship between macroinvertebrate IBI metric to percent imperviousness of the watershed (from Schueler and Galli, 1992; Schueler, 1994) In some cases, one or a few stresses may appear to dominate; however, looking for a simplistic relationship may, in some cases, illustrate the effect, but it may provide neither the answer nor a remedy for the problem. Nevertheless, there is a large number of articles in the literature that focus on the simplistic relationships of the indices of biotic integrity to one or several parameters and identifying these dominant parameters is useful. Simplistic Relationships Figure 3.2 is an oversimplified but widely-published relation of IBI of macroinvertebrates to the percent imperviousness of the watersheds located in the Washington, DC metropolitan area. Percent imperviousness 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 by Kleindl (1995) for lowland streams in Puget Sound, Washington, and replotted in Karr and Chu 10

17 (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 negative effects of % impervious area on the fish IBI that were even more profound than the effects on the benthic 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 and urbanizing watersheds. The authors concluded that most of the studies listed above and in Table 3.1 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 diffuse pollution. However, it is becoming evident that such oversimplifications can cause more harm than benefit to the understanding of the cause-effect relationship of pollution on the integrity of receiving waters. The percent imperviousness parameter is irreversible in most cases. To bring this relationship to an absurd conclusion, one could argue that every watershed with more than 8% to 12% imperviousness is degraded, therefore all urban development should consist of low-density, scattered subdivisions and no other remedies should be considered, except removing the impervious areas. Investigations by Yoder et al. (2000) in Ohio, shown in Figure 3.3, effectively dispute the notion of a simple relationship between biotic indices and a surrogate stressor such as imperviousness or some other land use parameter. Karr and Chu (1999) observed similar results. Yoder et al. (2000) analyzed data from small urban watersheds (<125 km 2 ) in Ohio. Small watersheds are particularly susceptible to degradation by urbanization. The Ohio program of using biotic integrity determinations 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 Community Index (ICI). Figure 3.3 shows that determining a simple relationship for the Ohio urban areas (similar to Figure 3.1) using a standard regression analysis was not possible. The authors qualify the relationship by identifying other stressors they found significant such as habitat degradation, wastewater and CSO inputs and legacy pollution in sediments. When the data points were separated by identifying the relationship for each individual urban area (Cincinnati, Cleveland/Akron, Columbus, Dayton, Toledo, and Youngstown), four urban areas showed a detectable decreasing IBI relationship with the logarithm of percent urbanization and the two remaining areas did not. However, even for the individual 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 value in regional studies and this relation may be used in GIS-based analyses and modeling of the effects of diffuse pollution and other stresses on the biotic integrity expressed by IBIs or similar integrative indices. Percent imperviousness and percent urbanization parameters have many generic diffuse pollution impacts, most of them being correctable (see Novotny, 2003), such as: 11

18 Table 3.1 Surrogate parameters for pollution (landscape and chemical) used in simple ecological biotic integrity statistical models Type of Dependent Variable Y= IBI = f(x 1, X 2...) Independent variable, X 1 Independent variable, X 2-n Authors Macroinvertebrate % impervious area Schueler and Galli (1994) Kleindl (1995) Fish % impervious area Wang et al. (2000) Fish % connected impervious 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 Fish Macroinvertebrate human population density width of forested urban riparian corridor Jones and Clark (1987) Dreher (1997) % urban land use Steedman (1988) May et al. (1997) Fish % agricultural land within 30 meters riparian zone along the entire upstream network 2) % agricultural land within 30 meters immediately adjacent 3) % agricultural over the entire upstream basin Van Sickle (2003) Macroinvertebrate degree of recreational activity Patterson (1996) Fish pollutant concentrations Karr et al. (1985a) Thorne and Williams (1997) Macroinvertebrate saprobien (or Hilsenhoff) index organic pollution (BOD) Sláde ek and Tu ek (1974) Hilsenhoff (1987) Macroinvertebrate taxa in unpolluted streams (British index) physical variables X 1 - distance from source X 2 - mean substrate particle size Chemical variables X 3 - nitrate+nitrite N X 4 - alkalinity X 5 - chloride Moss et al. (1987) The 5 variables were reduced from original 28 without losing reliability of predictions Macroinvertebrate five land-cover parameters Physical channel conditions (substrate texture) Morley and Karr (2002) Puget Sound Basin 12

19 Figure 3.3 Scatter plot of IBI scores vs. percentage of urban land use upstream from the IBI monitoring site for 267 small (<125 km 2 ) watersheds in Ohio (from Yoder et al., 2000). EWH = exceptional habitat criterion, WWH = warm water 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 or unpolluted) groundwater base flows into the receiving waters (correctable by implementing storage and infiltration). Urbanization increases variability of flows and water quality parameters, including salinity and temperature (correctable by implementing storage). Increased variability of urban runoff and magnitude of high flows makes flooding more frequent and causes bank instability and erosion. As a result, sediment loads increase and bank habitats are adversely impacted (correctable by implementing storage and infiltration). Urban runoff is more polluted with toxic compounds such as metals, PAHs, and cyanides in the winter in snow-belt areas (correctable by implementing source controls and treatment). Urbanization is also related to point source loads of pollutants, urban erosion from construction sites that increase sediment and pollutant loads, combined sewer overflows, and loss of flow to satisfy various urban uses and water transfers (correctable by implementing appropriate best management practices). Due to increased high flow and development pressures, urbanization results in diminished riparian zones and stream modifications making them more constricted and faster flowing, including channel lining, straightening and, ultimately, covering (correctable by stream restoration). 13

20 Thus, in addition to the overall surrogate stressor, expressed by percent imperviousness or percent urbanization, other stressors may be significant, including excess flow variability, which can be reduced by application of best management practices. Obviously, for non-urban streams landscape features such as percent forested or agricultural area of the watershed (Wang et al., 2000; Van Sickle, 2003), riparian zone conditions and buffers, geology of the watershed and morphology of the stream, ecoregional attributes (Omernik, 1987; Omernik and Gallant, 1989) or hydrologic stressors such as flow variability (Poff and Ward, 1989) are important. The other surrogates of stressors such as agricultural or forest land become important as the dominating effect of urbanization diminishes at low percentages of imperviousness. Karr and Chu (1999) point out several other factors that express human influence on biotic integrity, 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 to affect conditions in watersheds, water bodies or stream reaches and it is the gradient and type of change that are important. Removal of natural riparian vegetation has an effect, but replacing it with a vegetative bank protection has less impact than reinforcing banks with riprap or stone or concrete embankments. Impounding the stream also has significant effects. These changes, and their gradient, to land and stream use should then be linked to the pollutant inputs, for which discharges of toxic pollutants may have greater effect than the discharges containing nutrients or domestic effluents. 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 as recreation impact on mountain streams (Patterson, 1996). Figure 3.4 presents a contour plot of a two-variate simple regression model incorporating percent urban land use and percent riparian forest. The recent work of Park et al. (2002) is the most comprehensive multivariate analysis and model development. It linked 34 environmental variables to the macroinvertebrate Shannon diversity index (SH) and species richness (SR). The data were collected at 664 sites on 23 different water types in the Netherlands. The water bodies included springs, canals, streams, ditches, lakes and pools. The researchers used the counter propagation Neural Network Model (CPN) (Hecht-Nielsen Figure 3.4 Qualitative bi-variate IBI regression model for IBI as a function of % urbanization and % retention of urban riparian forest (from Steedman, 1988) 14

21 1990); however, in the research they used only feed-forward feature of the model without couterflow. The model consists of supervised and unsupervised learning algorithms that classify the inputs and predict output values. It is a layered model consisting of input, output and one or more internal layers. This approach is very close to that proposed in the Northeastern University/University of Wisconsin research. The 34 environmental variables used in building the model were: Percentage cover emergent vegetation Percentage cover floating vegetation Percentage cover floating algae Percentage sampled habitat: emergent vegetation Percentage sampled habitat: detritus Percentage sampled habitat: floating vegetation Percentage sampled habitat: gravel Percentage sampled habitat: clay Percentage sampled habitat: bank Percentage sampled habitat: submerged vegetation Percentage sampled habitat: silt Percentage sampled habitat: stones Percentage sampled habitat: peat Percentage sampled habitat: sand Dissolved oxygen percent saturation Percentage cover by bank vegetation Percentage cover by submerged vegetation Percentage cover by all vegetation Stream width Width/depth ratio Calcium Chloride Depth Silt thickness Electric conductivity Ammonium Nitrate Oxygen concentration Ortho-phosphate Acidity Flow velocity Water temperature Total phosphate Slope The output of the model fitted the SH and SR measured values well with a high accuracy of prediction (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. Before scaling data, the environmental variables were transformed by natural logarithms to reduce skewed distributions. 15

22 16

23 CHAPTER 4 ECOSYSTEM HIERARCHICAL SPATIAL AND TEMPORAL 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 discussion and model development will deal with the upper hierarchy systems. Watersheds, or stream systems, unify an entire drainage system and represent the highest level in the hierarchy. Watershed characteristics reflect the landscape/morphologic history as affected by geological, tectonic and long-term climatic factors. The scale of change is on the order of thousands of years. Human impacts and interactions affecting watersheds occur more quickly; however, they also can date back one hundred to one thousand years. The earliest deforestation periods by humans can be attributed to Romans (two thousand years ago), Mayans (one thousand years ago) and Venetians (five hundred years ago). Rapid urbanization with significant watershed impacts dates back to the industrial revolution 150 to 200 years ago, but has accelerated in the last forty years with the onset of widespread automobile use and building of freeways. The watershed-receiving water body system has the following components that should be considered when estimating the watershed loads and the receiving water quality and their impact on integrity (Novotny, 2003): Surface flow component that contributes surface runoff, sediments and pollutants to the receiving water body. Top soil components that store most of the contaminants and may contribute on occasion to interflow loads to the receiving water body. Shallow aquifer or subsurface zones that contribute groundwater (base) flow to the receiving water body. Impervious surfaces that contribute to fast surface flow (in rural areas such surfaces would include roads, farmsteads and feedlots or roads in forested areas) Stream segment systems contain the receiving water body, its in-situ deposits and water and the riparian corridor. The segments are bounded by major discontinuities such as a tributary, change of slope, bank materials, human modifications (i.e., low head dam or drop structure), substratum character, riparian canopy or floodplain riparian characteristics. The channel and its corridor are a part of the same riverine ecosystem. The areas outside the main channel but inside of the corridor, called riparian zones, play an important role in the ecological health (integrity) of the aquatic system. The riparian zones contain wetlands and oxbow lakes that 17

24 are primarily abandoned channels, meadows and riparian forests. This system is created by the hydrogeological action of previous floods and sometimes tectonic forces over geological timescales. In urban areas the riparian zones are modified by development and flood control measures that sometimes may eliminate the riparian environment. The length of stream segments are in hundreds of meters. Laterally, the stream segment cross-section is shown in Figure 4.1. The extent of the floodplain to the 100 year level is more or less arbitrary; however, it is wellestablished by flood control regulations and insurance. The actual width of the stream corridor is given 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 and are exemplified by depth and velocity patterns. The pool-riffle structure is restricted to the base flow channel - the thalweg. In larger deeper streams, the pool-riffle structure is replaced by a runbend 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 the habitat quality indices (Plafkin et al., 1989; Barbour et al., 1997, 1999). Channel modifications and impounding 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 less relevance in the watershed classification and the developing of models relating watershed landscape and pollution characteristics to the integrity of the receiving water body. Figure 4.1 Stream ecosystem cross-section. 18

25 River Continuum Concept Poff and Ward (1990) emphasize that the physical hierarchy is important for lotic systems where most 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 of the 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 longitudinal changes in stream ecosystems (Figure 4.2). The concept proposes a relationship between the stream size and order and progressive shift in structure and functional attributes. The conceptual model helps identify the connections to generalize the watersheds, floodplain, and stream system. The concept also describes how the biological community and water quality develop and change from headwater areas to the river mouth. The Continuum Concept hypothesis assumes that many first to third order headwater streams are shaded by riparian forest canopy. The shading limits the growth of algae, periphyton, and other aquatic plants. Since energy cannot be created through photosynthesis, the aquatic community in the stream is dependent on allochthonous materials (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 the streams are uniquely adapted to the use of externally-derived organic inputs and have, for example, macroinvertebrate communities dominate with shredders and collectors. As one proceeds downstream to fourth, fifth, and six order streams, the channel widens, which increases available light and, consequently, primary production. The stream begins to become more dependent on autochthonous materials (materials originating from inside the channel). In these downstream sections, species richness of the biological community increases as the ecological system adapts to 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 rivers develop increased reliance on primary production by phytoplankton. These river sections receive large inputs of dissolved and ultra-fine particles from upstream. The River Continuum Concept is important when interpreting the biotic composition and water quality of rivers. Several biotic indices 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 were calibrated using the ecological (biotic and morphological/habitat) condition of small, wadeable, lower-order streams. 19

26 Figure 4.2 The River Continuum Concept (Federal Interagency Task Force (1998), adapted from Vannote et al. (1980)) 20

27 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 is a short duration change that returns in a short time to its pre-disturbance level. A step is a permanent relatively sudden change (e.g., fast irreversible deforestation, implementation of 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 or descending. Global warming is apparently a trend effect. Periodicity. A cyclic oscillation of a parameter with different and often multiple frequencies and magnitudes. Periodic oscillation of the water body ecosystem can be multiannual (e.g., El Niño meteorological changes, Hurst phenomenon for meteorological patterns and stream flows, see Hurst, 1951; Klemes, 1974), annual (flow, temperature, dissolved oxygen, diffuse pollution 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 or duration of the parameter (system characteristic) magnitude. Random fluctuation can be characterized as wide bends (slow and fast variations) or narrow bends (primarily slow frequency variations). Causes of random fluctuations are numerous. Table 4.1 presents examples of time-variable ecological stresses and their impacts. None of the components of the temporal variations can be exactly predicted. Even annual variations vary from year 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 are unpredictable. Characteristics of random fluctuations can be revealed from the past data but the future fluctuation cannot be exactly predicted. Together, these components form a stochastic time series and the system they reflect is a stochastic system that can only be described in probabilistic terms. Depending on the relative importance of the components in the series, the systems can range from partially predictable to unpredictable; however, even unpredictable random systems, where random components predominate, can be characterized in probabilistic terms (e.g., mean and probability ranges and probability distribution of the values of the parameter). The cyclic, trend and random fluctuations (wide or narrow band) can be quantitatively ascertained by time series analysis using, 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. 21

28 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) Step Treatment process shut down or start up Cutting down riparian tree cover Cyclic Flow, temperature, algae photosynthesis Acute toxicity, die-off of sensitive organisms Scour of bottom habitat, flushing of organisms DO depletion Change of pollutant loads Change of habitat, increase of temperature 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 and relatively stable as far as further development is concerned. Quaas Creek, located about 45 km northwest of Milwaukee, is about 30% urbanized and urbanization is expanding. Nichols Creek is a reference rural stream. Quaas and Lincoln Creeks exhibit dissolved oxygen oscillations typical of nutrient and algae enriched streams. The oxygen saturation value is approximately 10 mg/l. Due to photosynthetic oxygen production during the day time and respiration during darkness, supersaturation was reached in the late afternoons and oxygen depletion occurred in the early morning hours. The magnitude of the oscillations is proportional to the degree of enrichment, in this case expressed by the surrogate parameter of percent urbanization. Of note is the steep crash of 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 as autocorrelation functions and spectral and Fourier analyses. The most simple is probabilistic plotting, as in Figure 4.4, which plots the annual DO variability for two of the creeks shown in Figure 4.3. In the probabilistic plotting, the data are fitted to the cumulative Gaussian normal probability distribution. The probabilistic plotting can be either arithmetic or logarithmic where the stressor values are entered in the analysis as their logarithms. If the data, either in the original or transformed 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 and their 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 22

29 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. Dissolved Oxygen (mg/l) Nichols Creek Quaas Creek Lincoln Creek /12/00 10/13/00 10/14/00 10/15/00 10/16/00 10/17/00 10/18/00 10/19/00 10/20/00 10/21/00 10/22/00 10/23/00 10/24/00 10/25/00 10/26/00 Figure 4.3 Dissolved oxygen variations in fully urbanized Lincoln Creek (Milwaukee, WI), partially urbanized Quaas Creek (West Bend, WI) and reference Nichols Creek. Source: Tim Ehlinger. If variability is significant and is caused by high frequency fluctuation of a potentially critical water quality parameter, development of a relationship between organism sensitivity (adaptability) and variations at different frequencies could be researched. Clearly, as shown in Figure 4.4, Lincoln Creek DO concentrations are more variable than those of Quaas Creek. In the most simple way this can also be expressed by the coefficient of variation: CV = standard deviation mean A first derivative of the time series of the magnitudes of a parameter is the rate of change. Many ecological processes and biotic compositions reflect the temporal changes of the parameters such as temperature, sunlight, nutrient and food availability, and frozen and ice-free periods. Buffering Capacity of a System 23

30 cumulative percent Figure Dissolved Oxygen (mg/l) Cumulative probability distribution of the annual DO distribution in 2002 for Quaas and Lincoln Creeks. Source: Tim Ehlinger. It is important to distinguish between variability of the stressors that act on the boundary of the system and internal response/stressors in the water body. Terrestrial and aquatic systems have the ability to buffer variability, meaning that the response of the system to a variable external stress will 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 fluctuations will be reduced. The buffering capacity of the system is proportional to the size of the system and is related to the frequency of the fluctuation (both cyclic and random) of the outside stressor. It is also 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, a lake that has a retention time of one year will remove most daily fluctuations in concentrations of a conservative toxic compound resulting from a time variable discharge while a plug flow river or an impoundment with a residence time of one day will not. The same large lake will also buffer short term higher frequency random fluctuations. The estimate of buffering capacity of aquatic ecosystems to various transient conservative and nonconservative 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 equations apply to plug flow, dispersed flow, and completely mixed aquatic systems. In general, large water bodies and completely mixed water bodies will have the largest buffering capacity, followed by the larger dispersed flow bodies, and the dispersed flow river will have the least amount of buffering, depending again on the size. 24

31 It can be assumed that the aquatic living organisms may also have a similar buffering system for assimilating and adapting to transient changes; however, this resistance or adaptability to changes in 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 stressors and/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, the organism will undergo a physiological adaptation to the new condition. For example, higher temperature will result in lower growth rates. If the stress is brief, the organism will return to its previous conditions. If the changes continue, the organisms will adapt to the change with a new physiological state. If adaptation cannot occur, the organisms may, after a certain period, disappear from the area either by avoidance or due to chronic effects of the stressor. Most organisms are adapted to annual or daily fluctuations; however, if the frequency or magnitude of the fluctuations changes, this would also represent a change of the stress. The biotic species that cannot adapt to the variability will be replaced by species that are more resistant to the variability. Watersheds that are naturally or anthropogenically flashy, based on precipitation and landscape characteristics, will contain fish and macroinvertebrate communities that have evolved to recover quickly from repeating 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 of organisms (Southwood, 1977, 1988; Poff and Ward, 1990). Surface roughness and embeddedness affect colonization dynamics of benthic organisms and feeding, refuge and spawning of fish. Insect diversity is positively correlated with the surface substratum complexity and particle size heterogeneity. Periodic and random events that disrupt the substrate will have an effect on the quality and diversity of aquatic life residing in a water body. Species requiring stable substrata for growth will not exist successfully in a water body where the substratum is constantly disrupted by navigation or transient large 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 of benthic species. Generally, the texture and composition of the benthic layer is related to the shear stress of the flow that is expressed as J = ( R S e where ( is the specific weight of water (9810 N/m 3 ), R is the hydraulic radius, which for streams is approximately equal to the depth of the stream (meters), and S e is the water surface slope. The unit of shear stress, J, is N/m 2. The bottom sediment has a resistance to scour that is related to the grain size of the sediment and sediment type. Cohesive fine texture sediments composed of clay, silt and organic matter are more 25

32 amenable to scour erosion than coarser, non-cohesive sediments of sand and gravel with less or no organic matter present in the sediment. Cohesive sediments exist only in slow moving or nearlystagnant lowland streams and impoundments. Literature data (Mehta et al., 1989) indicate that the critical shear stress for deposition and accumulation of cohesive sediments is about J c = 0.06 to 0.08 N/m 2. Deposition and formation of cohesive sediments will not occur if the shear stress at flows less than the mean annual flow is greater than the critical shear stress, J c. For streams at steady state, the slope of the water surface coincides with the channel slope. Non-cohesive sediments (sand and gravel) exist mostly when the shear stress is greater than 1 N/m 2. Between J = 0.1 and 1 N/m 2, the sediment composition will be mixed. In polluted or nutrient-enriched water bodies (also considered polluted if the enrichment is not natural), 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 undergo anaerobic 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 stream impoundment, for navigation purposes and/or power production. Impoundment changes the substrate texture and increases sedimentation of fine texture sediments and organics that can then form deep layers. These sediments exhibit sediment oxygen demand and may be resuspended by barge traffic. Bhowmik et al. (1981) studied the effect of barge traffic on resuspension of sediment in 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 navigational channel. The increase is more significant when the ambient suspended sediment concentration is low. 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 but very transient resuspension of sediments during barge tow passage. The increases lasted between a few minutes and ten minutes, at most. Typically, sediment concentrations increased during the barge tow passage by as much as 90 mg/l but the concentration subsided to its pre-passage value within 10 minutes after the passage. In addition, Butts and Shackleford (1992), who studied the Upper Illinois River, did not find significant differences in sediment concentrations with and without traffic. However, constant resuspension may disrupt the habitat for benthic invertebrates and feeding of fish that will have an effect on biotic integrity. Even streams that were impounded for purposes other than navigation exhibit diminished species diversity and composition that is subsequently reflected in the magnitude of the Index of Biotic Integrity (AquaNova/Hey Associates, 2003). 26

33 CHAPTER 5 MODELING ECOSYSTEMS WITH DYNAMIC MULTIVARIATE APPROACHES Applying Multivariate Models to Ecological Systems Multivariate methods are now widely accepted by ecologists and many treatises have been written regarding their application to ecological systems (Green, 1980). Measuring similarities among samples 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 biological variable caused by more than one stress. Green states that multivariate analyses often represent the most appropriate and the most powerful approaches to both the description of the ecosystem and hypothesis-testing. Every univariate model used in ecology is only a part of a general multivariate model and the latter is more appropriate for most ecological problems. However, parsimony of the model, i.e., using fewer important variables over a multiplicity of variables when significance is less than the noise, is counterproductive. Thus, the development of a multivariate model must include 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 the same level of hierarchy) and vertically (between the stressors at the upper and lower layers of 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 the endpoints 6. Conduct sensitivity analyses and make the model parsimonious by eliminating insignificant stressors 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 internal stresses 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 extensively covered in the literature (e.g., Chapra, 1997; Jørgensen and Bendoricchio, 2001). Such models primarily describe water quality concentrations and the mass (or concentration) of the lower trophic level overall biotic composition or surrogates (e.g., chlorophyll a, phytoplankton and zooplankton biomass and growth). The development of models that describe fish or macroinvertebrate species 27

34 or even genera biomass has not been successful using deterministic, strictly functional mathematical, 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 models in 1990s, to models developed by learning software such as Neural Networks and Genetic Algorithms (Jørgensen and Bendoricchio, 2001). The application and development of the most recent 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 an understanding of the entire ecosystem, giving more insight than the sum of knowledge about its parts relative to the structure, metabolisms and biochemistry of the landscape. An ecosystem is organized, but also includes a degree of randomness. The more that is known about the processes and stresses that affect the composition of the biotic assemblages, the more uncertainty is introduced into the description of the system because each subprocess has its own randomness and uncertainty. Uncertainty is not identical to randomness. Uncertainty, as the term implies, includes both the randomness inherent in each process and the lack of precise knowledge about the process and its complexity. Jørgensen and Bendoricchio (2001) distinguish structural complexity, defined as 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). Figure 5.1 Concept of a multivariate/multimetric ecological model A multivariate ecological system is rarely in a stagnant invariant state. Even for unimpacted watersheds covered by native vegetation (see Figure 1.1), the biotic composition may respond to long- and short-term meteorological variations, seasons and other factors. Such inherent variability 28

35 is the reason why the biotic integrity of disturbed watersheds should always be related to or normalized by that measured at reference, unimpacted water bodies of similar character located in the same ecoregion. The biotic integrity may be in equilibrium with the long-term invariant stressors that can be expressed by invariant surrogates (e.g., percent imperviousness) or stressors that cause a downward temporal or permanent change such as the rate of deforestation in the watershed or a change of a regional diffuse pollution load by one or more pollutants such as an increase in acidity of rainfall. Multi-layer models An ecological model linking stressors to biotic endpoints is often hierarchical, where the impacts of stressors propagate through several structural layers. Such a model has been proposed by Allen and Starr (1982). In Allen and Starr s concept, the hierarchical model is defined in terms of stems and holons. A holon is a structural element of the model and a stem is a functional connector of the holons. In a nested case, the span of a given holon is the sum of the parts of which the model is made. 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 to advanced neural net models (Hecht-Nielsen, 1990; Lek and Guégan, 2000). An artificial neural network model (ANN) is a layered multi-regression model that can resolve and learn both linear and nonlinear relationships. ANN is a computer algorithm that responds to a problem in a fashion similar to the human brain, including association, generalization, parallel search, learning and adaptability (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, even seasonal 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 are commonalities 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 response that may then act upon another component. The components often include a storage feature that acts when the output is constricted so that the excess input is stored and can be processed inside the component. Commonly, the storage capacity has a limit that can impact the magnitude of the output. Mathematically, the system can be linear or nonlinear, steady (invariant) or dynamic (time variable). A component of the ecosystem can by either physical or biological. Physical components include soil 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 that strongly affects the composition of the biotic species but has a relatively small effect on the transfer of chemicals and nutrients unless it is part of the riparian buffer. Biological components are numerous and can be categorized biologically along trophic levels from autotrophic and heterotrophic microorganisms to invertebrate and vertebrate communities. The components exchange energy, nutrients and other chemical mass. The most measured ecosystem properties are the 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 29

36 to have an impact on the density and composition of the biotic endpoints that can themselves also form a component of the system. For example, the macroinvertebrate community that is commonly used as an indicator of the biotic integrity endpoint is linked in a hierarchical manner to the higher trophic level fish ecosystem response indicator/endpoint. The components themselves and their storage are related to landscape and water body morphological and ecological parameters. These characteristics are readily measurable and in most cases are invariant. Although they are not a stress themselves, the changes in these parameters and emissions of pollutants may result in stressors. They also affect the capacity of the system components 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 two Indices of Biotic Integrity (fish and benthic macroinvertebrate), it may be useful to reiterate that there are numerous endpoints that have been proposed for watershed and water body ecological classifications. The term endpoint has an analogous meaning in environmental system analysis that deals with decision variables and criteria. For example, the endpoint of the simple dissolved oxygen model is the DO concentration in the river that is then, in the decision phase, compared with the established DO standard. In this sense, the comparison with the water quality standard is the endpoint. Ecological endpoints are more complex but based on the same principle. Endpoints have been categorized as assessment and measurement endpoints (Suter, 1990; Simon and Davis, 1992). An assessment endpoint is an environmental characteristic that should have societal relevance that is understood and valued by the public and by decision makers. Suter (1990) presented the criteria for assessment endpoint selection, as shown in Table 5.1. The Indices of Biotic Integrity fit into this category because (1) they express the goals of the Clean Water Act to provide for a balanced aquatic biota and (2) they are becoming widely acceptable by decision makers and are gradually being accepted by the public as well. Measurement endpoints should correspond to or be a predictor of an assessment endpoint, i.e., of the IBIs. Suter (1990) states that measurement endpoints should be correlated either to the assessment endpoint or be one of a set of measurement endpoints that predicts an assessment endpoint through a statistical or mathematical model. If fish IBI is selected as the main assessment endpoint, then the measurements endpoints are those lower-level variables to which the fish IBI will be correlated, e.g., habitat endpoint (index), concentrations of contaminants in water and sediments expressed as a risk, and even the macroinvertebrate endpoint (IBI) because fish belong to 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 evaluating compliance with the goals of the Clean Water Act (chemical integrity), and the habitat suitability index serves for assessment of actions that would lead to water body restoration. This indeed leads to a hierarchy of endpoints and to a hierarchical model of linking the stressors (lowest layer of variables of the model) to the lower level measurement endpoints and then to the highest level endpoint, fish. The well-being of humans could become an endpoint if humans are linked to the 30

37 ecosystems model through drinking water, eating fish and skin exposure to contaminated water by primary recreation. Generally, in risk assessment, endpoints reflect regional societal values, such as crop health, human health, and fish composition. Detenbeck et al. (2000) divides the endpoints into structural and functional. 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-stream and riparian characteristics, and periphyton, macroinvertebrate, and fish community structure (sampled once during baseflow conditions in late summer). Functional endpoints derived from these static measurements include macroinvertebrate and fish guilds, fish reproductive and flow tolerance guilds, and percentage of motile biraphid diatoms in periphyton communities. The identification of the multivariate classification template in Detenbeck et al. (2000) is not much different from the endpoint identification of the Indices of Biotic Integrity. According to these definitions, the Indices of Biotic Integrity are functional endpoints. Table 5.1 Characteristics of good assessment endpoints (from Suter, 1990) Social relevance Biological relevance Unambiguous operation definition Accessible to prediction and measurement Susceptible to the hazard Development of the Model Structure Karr et al. (1985b) provided the first insight into the complex structure of the relationships between the landscape and other stressors and fish communities. These relationships formed the basis for the formulation of the biotic integrity concept expressed in Figure 2.1. This concept is shown in Figure 5.2. The structural components were described as: Energy source: allochthonous organic matter vs. primary production in the stream, particle size 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 water availability, flood frequency Biotic interaction: competition, predation, disease, and parasitism 31

38 Figure 5.2 Conceptual model of the primary external stressors and internal structure 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 links and endpoints that are being developed in the STAR research project by Northeastern University and partners. The first step for the a priori model development from the measured databases is to organize the ecosystem model structure into structural and functional components. The concept of the model concept shown in Figure 5.3 is almost identical to the structure of the stress progression proposed by Karr and Yoder (2003), shown in Figure 5.4 and derived partially from the conceptual model 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 of model components and functions will be presented in Chapter 6. The lowest layer, past and ongoing landscape and channel modifications by humans (pollution but not pollutants) and emissions of pollutants from point and non-point sources, represents the root causes of the problem. The landscape and emission parameters have to be quantified. Several traditional 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 variability can 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 reliable yet simple functional models. Examples of functions include the dilution model, delivery ratio function, simple sedimentation functions, and simple sediment partitioning. A function for dissolved oxygen variability as a result of nutrient enrichment can also be developed. 32

39 The third layer is the estimation of risks. A risk is a numeric probability that some species will be adversely affected by the exposure to the contaminants and habitat impacts and will disappear from the system. In most cases, only the most sensitive species are in a danger. The risk (probability) can be 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 risks and consider their synergy, additivity or antagonism. The risks are the measurement endpoints. Figure 5.3 Concept of the stressor-risk-endpoint propagation model based on Novotny et al. (2001) and Novotny (2003). Because they express the same measure, the probability that species can disappear, they can be compared and prioritized. Risk can also be linked to the probability of exceeding established water quality standards. The risks can be related to the biotic endpoints expressed by the metrics of the Indices of Biotic Integrity (fish and macroinvertebrate). If human impacts are considered, risks of priority pollutants can be linked by pollutant partitioning and biomagnification models to the risk effects on human health. Given the many complex interactions involved in modeling the relationships between stressors and endpoints, it is clear that univariate or multivariate single layer models may not work, except in some localized single stressor situations. Multi-layer models, as will be described in the next chapter, represent a more realistic approach. 33

40 Pollution (specific human activities) Stressor Pollutant (P and NP) loading for all sources (source specific) Land use effects Channel and flow alterations Exposure (landscape) Ambient pollutant levels in water body (chemical specific) Riparian and in-channel effects Exposure (in-stream) Human health (health outcomes including disease) Ecological health (cumulative effects on biological condition) Response Designated use (water body specific) Endpoint Figure 5.4 Concept of the links between stressors, exposure, and response (end points) by Karr and Yoder (2003) modified from the Committee to Assess 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 include stream hydraulic modifications by impoundments, lining, drop structures, or ripraps, which can cause siltation by excessive sediment inputs, habitat loss or degradation, intensive navigation and loss of riparian vegetation. Habitat fragmentation and interruption of migratory routes by dams or thermal barriers caused by thermal discharges are also important stressors. Other exposure links are related to the effects of pollutants on water quality. Such links include eutrophication, dissolved oxygen levels and fluctuations, temperature and its fluctuations, and salinity fluctuations and transient inputs during winter in snow-belt areas. When the dissolved oxygen reaches certain low levels, 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 anaerobic microorganisms). CHAPTER 6 34

41 MODEL BUILDING Selection of Submodels (Functional Links) The models (hierarchical links) describe the variability of the stressor with respect to a threshold at which a breakpoint in continuity (discontinuity) will occur. The discontinuity may not be as simple as a deterministic crash of part of the system; rather, it is a probability that the system or a component will be adversely affected to a point that it cannot function at a level necessary for the sustainability of the system. Effects-based threshold models or submodels are based on the clustering of community-level responses 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 from the lowest level stressors to the highest level endpoints. It is not the task of this research project to carry out the progression all the way to humans; however, adding a human component certainly has potential. At this level of knowledge about the ecosystem, modeling a four-layer model, shown in Figure 6.1, has been proposed and will be investigated. The hierarchical layered model is different and, obviously, more complex than the one layer univariate (e.g., IBI vs. imperviousness) or multiparameter watershed classification systems proposed 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 aquatic system 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, such as elevated or fluctuating temperature or toxic contaminants in water or damaged habitat. This layered approach allows for the identification of the stresses one or two layers down that are significant and should or could be managed. A sensitivity analysis of the model can accomplish this 35

42 task. Some functional links may have a buffer component that reduces the variability and magnitude 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 lines of the model, are described in the following sections. Layer I - Assessment Endpoints Figure 6.1 Schematic of the multilayer risk propagation model. The objective of the research is to determine the factors that affect the integrity of the receiving water bodies. While Suter (1990) and others have listed several biotic endpoints (see the discussion in the preceding section), there are only two established and widely accepted Indices of Biotic Integrity that will be used in the research as the main assessment endpoints. These are the fish Index of Biotic Integrity, originally proposed by Karr et al. (1986), and the macroinvertebrate Index, originally proposed by Hilsenhoff (1987) and further expanded and modified (e.g., Karr and Kerans, 1992). These indices were included in the national USEPA guideline documents (Plafkin et 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 new indices for benthic systems have been developed (Karr and Chu, 1999). 36

43 The most recent manual on the Rapid Bioassessment Protocols (Barbour et al., 1999) also contains the IBI methodology for periphyton (benthic algae), which are primary producers and an important foundation of many stream food-webs. Periphyton, as well as zoobenthos, have been used as indicators in Europe for at least twenty years (Marvan, 1991; Whitton et al., 1991) and the methodology 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 the concept of biotic integrity modeling is unclear and still unknown. Periphyton have been used for monitoring water quality only in few states and there may be a paucity of data in the national databases. Furthermore, the periphyton evaluation has not yet been fully developed into a quantitative index. Nevertheless, the periphyton box has been added to the model concept presented in Figure 6.1 for future references and research. Both fish and macroinvertebrate indices are scored evaluations of composition, health and abundance of the organisms. They have to be applied within an ecoregional context, in most cases by 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 there may be some interrelationships between the invertebrate index and fish index. These interrelationships can be correlative where the invertebrate quality and composition affect the fish population or cross-correlative where both groups of organisms responds in a similar fashion to a stressor or stressors. Even though no such relationships have been identified in the literature, they are likely to occur. Each index is a summation of the valuation of its metrics. The fish index has 12 metrics separated into three categories: (1) species richness and composition, (2) trophic composition, and (3) fish abundance 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) used a 10 metric IBI (Table 6.2) with four categories: (1) Taxa richness and composition, (2) Population attributes, (3) Tolerance and intolerance, and (4) Feeding and other habits. The Ohio Invertebrate Community 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 will be affected differently by the lower level stressors. For example, sediment contamination risk will have a greater impact on benthic macroinvertebrates than on fish populations, while for water column contamination will have a greater impact on fish. Similarly, fragmentation risk will affect fish and macroinvertebrate populations differently. 37

44 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 region and Composition Number and identity of darter species Varies with stream size and region Number and identity of sunfish species Varies with stream size and region Number and identity of sucker species Varies with stream size and region Number and identity of intolerant species Varies with stream size and region Proportion 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% Cyprinids Proportion of individuals as top carnivores >5% 1-5% <1% Fish Abundance and Number of individuals in sample Varies with stream size and region Health 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 richness Stonefly taxa richness Caddisfly taxa richness Long-lived taxa richness Tolerance and Intolerance Intolerant taxa richness Tolerant taxa % Feeding and Other Habits Clinger taxa richness Predators % 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 composition Number of mayfly taxa Percent tribe tanytarsini midge composition Number of caddisfly taxa Percent of other dipteran and non-insects composition Number of dipteran taxa Percent tolerant organisms Percent 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. 38

45 Linking macroinvertebrate IBI to fish IBI. The database containing both B-IBI (ICI) and fish IBI will be investigated to identify correlations between the macroinvertebrate and fish metrics. These correlations may be affected by habitat quality and chemical risks. The project could be a MSc thesis or group research project for a limnology class. The research should use simple multiple regression methodologies and plotting to identify potential relationships. Improvement and development of a quantitative periphyton index and linking it to stressors and fish and macroinvertebrate integrity. Periphyton can be a very useful indicator of integrity, especially with respect to nutrient and organic enrichment, temperature, and current. Algae usually do not develop in zones of higher organic pollution mainly due to predation by heterotrophic decomposers, even in zones of high nutrient enrichment. When high organic pollution is removed, filamentous and other attached algae may grow in-stream at high densities. The composition of the algal species is also related to temperature; lower temperatures tend to favor diatoms, while higher temperatures often lead 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, current velocity 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 (including temperature) 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 Figure 6.2 Algae population shifts with temperature (Cairns, 1955) 39

46 interstitial sediment-water layer will disappear as a result of the upper sediment layer contamination C. Habitat risk: A risk that the habitat has been modified or disturbed by humans to a degree that life functions of the organisms such as spawning, feeding, or shelter cannot be supported D. Fragmentation risk: A risk due to fragmentation of the system by impoundments, hydraulic modifications, drop steps, bridges and culverts, or diking that prevents natural migration of the organisms (e.g., to and from natural spawning areas). The fragmentation can be longitudinal (upstream/downstream) or transverse (between the channel and the riparian 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 the most sensitive species by a stressor is exceeded. This is a methodology that can take advantage of the definition and development of water quality standards. The standards are based on the sensitivity of the organisms at the 5 th percentile sensitivity (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 be adversely affected. To provide a close to full protection, the standard (criterion) is selected as CMC = " (FAV), where the recommended value for " = 0.5. Because most water quality (concentration) data are log-normally distributed, the probability of exceeding 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 the probability. The risk can then be calculated as a joint probability (Novotny and Witte, 1997) p. p 1 p 2 p ww " where p is the overall joint probability of adverse toxicological-ecological effect, p 1 is the safety factor incorporated in the numeric criteria from the 96-hour bioassays using the USEPA procedure (this factor has a value of approximately 0.001), p 2 is the probability of exceedance of the water quality criterion (which should consider the biological availability effects as expressed in the water effect ratio, WER), p ww is the probability of wet-weather flow (for the Central United States, this probability is about 0.065) or dry weather flow, and " is a factor that considers the effect of the difference between the 96-hour duration of the test exposure and the expected duration of storm events (for an average storm of 9-hour duration, " = 0.3). If wet and dry weather flows are not separated then p ww =

47 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 as the 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 will disappear first. The species also differ in tolerance to the different stressors causing the risk. Fish are very sensitive to low dissolved oxygen concentrations while sludge worms (e.g., Tubifex tubifex) are not. However, in contrast, sludge worms are far more sensitive to concentration of toxic metals, such as copper, than fish. The risk for an individual species of organisms is linked to the probability that the stressor will reach a threshold value that will result in an adverse effect that can be either acute (lethal) or chronic (loss of mobility or reproduction). The overall risk that some or all of a species will disappear is a cumulative probability (integral) of the individual risks. The concept of the ecologic risk for a single stressor (e.g., concentration of a toxic compound) or risk is 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 and Witte (1997). This concept is based on a direct consideration of the joint probability of two probability functions: (1) the probability density function (pdf) of the stressor (C) adjusted for the appropriate dilution Figure 6.3 Log-normal probability plot of a water quality parameter and assessment of probability of compliance (noncompliance) with its water quality standard 41

48 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 be adversely affected by the exposure to the stressor C modified by DR and WER. The joint probability is: hrc (, ) = ( CgR ) ( C) The integration over all probabilistic values of the stressor, as summarized in Figure 6.4, will then yield the total risk, r. Figure 6.4 Ecological risk assessment for stormwater impacts (from Novotny and Witte, 1997). The r value, however, expresses the total risk due to one stressor only. Therefore, the total risk due to all the relevant stressors will be: R = Σ The stressors may exert a combined effect on an organism (additive), they may interfere one with another (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 cadmium to fish, though their toxicity is synergistic to algae. Calcium (hardness) is antagonistic to heavy metals. r i 42

49 Statistical Specifications An example of the risk function, r, for copper is shown in Figure 6.5. For metals, the sensitivity of organisms depends on the hardness of water and should be adjusted accordingly. Given that there are more than 700 species of fish in North America (Suter, 1993) and tens of thousands of other aquatic species, the expectation that the limited set of species that have been tested contains the most sensitive species would be quite naïve. This assumption is avoided by assuming that the sensitivity of species follows some probability distribution. Figure 6.5 Plot of genus mean acute values for determination of risk of a pollutant (copper) to aquatic biota The risk function shown in Figure 6.5 for copper extends over several orders of magnitude of copper concentrations. Other parameters will have a more narrow impact range (e.g., temperature, dissolved oxygen). 43

50 Selecting Distribution for Risk Function The choice of distribution can have a profound effect on the final risk estimation, especially in the area 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 numerous studies that the ambient concentration of most parameters follows a log-normal distribution. The mean and standard deviation of log-concentrations provide sufficient information to fit the distribution, given that sample size is sufficiently large. On the other hand, the toxic response curve is defined rather poorly. Depending on the specific chemical tested, sometimes only as few as 10 data points are available. B. Sediment risk Risk to benthic organisms caused by sediment contamination may be ascertained in a similar way as the aquatic risk. However, there are several key differences: (1) The variability of the sediment concentrations is more spatial and not as temporal as that for the water column; (2) Only the upper layer 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 direct functional link between the sediment risk and benthic IBI; and (4) Acute effects are of lesser importance 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 only considered, exposure route is through the interstitial pore water. Applied to sediments, the joint probability function gives the probability that (1) a particular concentration will occur as a result of partitioning in sediment and (2) an indigenous benthic organism will be adversely impacted by a given concentration of a contaminant. The estimation of pore water concentrations is done by using the sediment partitioning concept: K p Cs = C d where K p is the partitioning coefficient, C s is the concentration in the solid phase and C d is the dissolved concentration in pore water. There are two major obstacles that complicate the use of ecological risk methodology for estimating the effects of contaminated sediment. First, data on sediment quality are often limited. This is especially true for data available from USGS or STORET, where the sediment is sampled only once so the distribution of concentration, both spatial and temporal, is unknown. Second, the exact value for the partition coefficient used to estimate pore water concentration from sediment contamination is unknown. 44

51 C. Habitat Risk Habitat risk can be related to a habitat index. Most habitat indices are numeric summations of several metrics and do not express a risk as defined above, i.e., a probability that indigenous species will disappear from the water body. Such effects can be implicitly considered in cases such as human modifications by channelization, flow withdrawals (lack of flow) or by habitats inhospitable to 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 some cases, the habitat parameter metrics describing substrate and riparian conditions can be correlated to the numeric values of the metrics of the macroinvertebrate index. The Habitat Quality index (RBP HQ) was included in the Rapid Bioassessment Protocols (Barbour 1997, 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 application of the habitat index must be adjusted to regional or ecoregional conditions. This requirement was the reason for developing the Ohio Index. The habitat index is closely related to the hydrology and morphology of streams. These characteristics 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. This classification scheme was preceded by twenty years by Huet (1949), who proposed a system for classification 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 is related to the energy of the streams that affects habitat substrate, gravel and rock for high slope mountain streams, gravel and sand for medium slope piedmont streams, alluvial deposits and fine sediments for low slope valley/plain water bodies, and organic fine sediments for the flat slope lowland and coastal waters. Impounding the streams changes the energy and the substrate. Barbour and Stribling (1991) modified the original RBP habitat procedure (Plafkin et al., 1989) by including separate methods for high gradient (riffle/run dominated) streams and low slope, large streams dominated by pool/bend sequence. Rosgen (1994) presented a stream and river classification system based on the premise that dynamically stable channels have a morphology that provides appropriate distribution of flow during storm events. He identified 8 major variables, each of which affect the stability of channel morphology but are mutually independent: channel width, channel depth, flow velocity, discharge, channel slope, roughness of channel materials, sediment load and sediment particle size distribution. When streams have one of these characteristics altered, some of their capability to dissipate energy is lost. Leopold et al. (1994) show that stable channels have enough capacity to accommodate flows that have a recurrence interval of 1½ to 2 years (Figure 6.7). 45

52 Figure 6.6 Dominant fish assemblages related to stream morphology (from Huet, 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 46

53 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 important characteristics) The Rapid Bioassessment Protocols (Barbour et al., 1999) that describe methodology for development of the Index of Biotic Integrity list ten habitat parameters. Some of them can be correlated to flow characteristics. Each parameter is ranked poor, marginal, suboptimal and optimal and assigned a numerical value that ranges from 0 to 20 or 0 to 10, depending on the relative importance of the parameter. The index is then a summation of the numeric ranking. The maximum value of the RBP habitat quality index is 170. The parameters for physical habitat evaluation are shown 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 RBP documents (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 regional modifications are mostly needed to adjust weight of the habitat metrics and not the selection of the parameters in the metrics. Figure 6.8 shows the relationship between Ohio s QHEI and the RBP HQ index (based on Plafkin et al, 1989). The 1989 RBP HQ index has 9 metrics with a maximum number of points of 135. One may anticipate that there should be a very high degree of correlation between the two indices. Nevertheless, Rankin (1995) has proven that the regionalized QHEI outperformed the original RBP HQ index (Plafkin et al., 1989) as shown in Figure 6.9. No such comparison was found in the literature for the latest improved version of the RBP HQ index in Barbour et al (1999). Table 6.4 Description of habitat parameters and scoring scheme used in the Rapid Bioassessment Protocol to assess habitat quality (Barbour et al., 1999). Maximum total score is Epifaunal substrate/available cover. Range of scores: 0-20 Includes 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 functions of aquatic macrofauna. 2.a Embeddedness Range of scores: 0-20 Measures the degree to which boulders or gravel are surrounded by fine sediments (silt, mud, or fine sand). As rocks become embedded, the surface area available to macroinvertebrates and fish (shelter, spawning, and egg incubation) is decreased. Embeddedness measurements are taken in the riffle and cobble portion of the stream 47

54 Table 6.4 Continuing 2.b Pool substrate characterization Range of scores: 0-20 Expresses 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: 0-20 Patterns of velocity and depth are included for high gradient streams as an important feature of diversity. The best streams will have 4 patterns present (1) slow-deep, (2) slow-shallow, (3) fast-deep and (4) fast-shallow. The division 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: 0-20 The 4 basic pool types in a stream are large-shallow, large-deep, small-shallow and small-deep. A pool is large if any of the pool dimensions (i.e., length, width, oblique) are greater than half the cross-section of the stream. One meter depth separates deep and shallow pools. 4. Sediment deposition parameter Range of scores: 0-20 Expresses the amount of sediment that has accumulated in pools and the changes in stream bottom resulting from the accumulation. High levels of sediment deposition is a symptom of an unstable system that becomes unsuitable for many organisms. 5. Channel flow status Range of scores: 0-20 Expresses 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: 0-20 A 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: 0-20 Expresses heterogeneity of the stream. Riffles provide a high quality habitat. 7.b Channel sinuosity Range of scores: 0-20 Evaluates the meandering of the stream. A high degree of sinuosity provides for diverse habitat and fauna. The absorption of high flow energy by bends protects the stream from excessive erosion and flooding and provides refuge for benthic invertebrates and fish during storm events. This parameter can be rated from maps. 8. Bank stability Range of scores: 0-10 Measures whether the stream banks are eroded or have a potential for erosion. 9. Bank vegetative protection Range of scores: 0-10 Measures the amount of vegetation on the bank and the near-stream portion of the riparian zone. 10. Riparian vegetative zone width Range of scores: 0-10 Measures the width of natural vegetation from the edge of the stream bank out through the riparian zone. The vegetative zone serves as a buffer for pollutants, controls erosion and provides habitat and nutrient input into the stream. An optimum width of the riparian zone is 4 times the stream width or greater than 18 meters. 48

55 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 pts b. Number of substrate types c. Substrate origin d. Extensiveness of substrate embeddedness e. Extensiveness of silt cover 2. Instream cover a. Presence of each type in the reach 20 pts b. Extensiveness of all cover in reach 3. Channel quality a. Functional sinuosity 20 pts b. Degree of pool/riffle development c. Age/effect of stream channel modifications d. Stability of stream channel 4. Riparian quality a. Width of intact riparian vegetation 10 pts b. Types of adjacent land use c. Extensiveness of bank erosion/false banks 5. Pool/riffle quality a. Maximum pool/glide depth 20 pts b. Pool/riffle morphology c. Presence of current types d. Average/maximum riffle/run depth e. Stability of riffle/run substrates f. Embeddedness of riffle/run substrates 6. Local stream gradient 10 pts. 49

56 Figure 6.8 Relationship between the Ohio QHEI and the RBP HQ index (from Rankin, 1995). Figure 6.9 shows that although there is a correlation of the overall fish IBI to the habitat integrity indices, the relationship is very poor when habitat is the only stressor (similar to the more remote relationships listed in Table 3.1). Again, relating IBIs to habitat indices may only be a futile exercise. The weights of the metrics in the Ohio QHEI were calibrated to the regional conditions and better reflected the modified stream conditions typical for Ohio. However, the 95% line, called Maximum Species Richness relationship (see the subsequent section), does follow the increasing trend of fish IBI with the improved habitat. The effect of stream modifications by impoundments is qualitatively depicted on Figure 6.10 showing the fish IBIs for Northern Illinois mostly modified water bodies: the Upper Des Plaines River s unmodified and impounded Dresden and Brandon pools, the Lockport pool on the Chicago Sanitary and Ship Canal (CSSC), impounded and flowing reaches 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 Des Plaines River and the CSSC are part of the Illinois River waterway, one of the busiest waterways in the nation. The impounded Des Plaines River is an effluent-dominated water body because it carries most of the wastewater effluent and urban runoff flows from the entire Chicago metropolitan area. The Brandon pool has problems with the low dissolved oxygen and the Upper Dresden pool is impacted by thermal discharges from a 1.3 MW power plant that is using once-through cooling with a capacity cooling flow comparable to the entire flow in the river. Thus, the IBIs in the Lower Des Plaines River navigational pools are affected by effluent and urban runoff discharges and habitat modifications (see AquaNova/Hey Associates, 2003). 50

57 Figure 6.9 Performance of Ohio and USEPA habitat indices (from Rankin, 1995). Of note are the results of the USEPA study on the Fox River that compared fish IBI 0.5 km downstream (free-flowing) and 0.5 km upstream (impounded) sampling points with respect to location of dams. The comparison of free-flowing and impounded sections of the river shows that there is a penalty in IBIs due to impounding of approximately 12 to 15 IBI points. This is also supported by the IBI measurements of the reference Green and Rock Rivers, which had fish IBIs in 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 are somewhat 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 the metrics and their weights. Rankin s (1995) assertion that metrics of habitat indices, and by the same reasoning those of the macroinvertebrate and fish indices (Miller et al., 1988), must be regionalized is 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 research should answer the question of weights and of the underlying (Level I and II) stressors. 51

58 60 Ohio Boatable IBI Lower Dresden Upper Dresden Brandon Lockport DesPlaines Fox Impounded Fox Flowing Green Rock Figure 6.10 Fish IBI for modified streams in Northern Illinois (from AquaNova/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 flow variability. 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 flow variability 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 (intermittent flow and flows exceeding an index of bankfull discharge). They pointed out that the ecologically relevant temporal scale is multiannual or less (intraannual) for flow variability, i.e., extremely rare hydrological events (e.g., 25 or 100 year recurrence flood or drought flows) are not relevant. Based on their conclusions, with the reasoning for the biologically-based frequency of allowable excursions of water quality standards (once in three years) (USEPA, 1994), an ecological system can 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 almost every state (Florida, Idaho and four New England states were not represented). All flow values were transformed by a natural logarithmic function (ln[x+1]) so that the flow series would be approximated by the log-normal probabilistic distribution. Each set of transformed daily flow data was then normalized by the logarithm of the long-term mean for the entire series. The authors divided the streams based on their hydrology into the following categories: 52

59 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, proportion of total predictability comprised by constancy Pattern of the flow regime: Flood frequency, median interval between floods, median duration of floods, two indices of flood predictability, median days on which floods have occurred over the period of the record Extent of intermittency: Average annual number of zero flow days, average over all years of the annual 24-hours low flow value divided by the grand mean of the ln-normalized data The Poff and Ward classification covers the entire US, i.e., arid desert streams, montane streams and humid temperate and warm regions. However, the stream classification was mostly geographical, 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 streams were pertinent: mesic groundwater, perennial runoff and snowmelt. The flow patterns of the majority of streams in northeastern and north central regions were characterized by snowmelt or rain. It is possible that the Poff and Ward classification scheme may not be suitable for Ecoregion 53

60 I because the streams are already categorized by the ecoregion. A new flow variability classification 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 variability directly affects the biota, then it would be included in a Layer II risk metric. If the flow affects habitat 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 be longitudinal (upstream/downstream) or lateral (channel/riparian-floodplain zone). Fragmentation can be natural (e.g., waterfalls); however, the majority of fragmentation is caused by humans and includes impounding and building of barrages for various purposes, diking, bridges and culverts and 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. The extent of fragmentation can be very significant in Northeastern US. Figure 6.11 shows low head dams in the New England coastal basin. Over 30 low head dams have been built on the Charles River (Massachusetts) alone. Most low head dams were built more than a century ago and today they 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 the ability of species to move/migrate among sub-populations or between portions of their habitat necessary 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 caused by pollutants (e.g., localized fish kills or a polluted mixing zone without a zone of passage or a thermal plume or stratification). Thermal plumes may create longitudinal fragmentation by creating zones that fish will avoid. Concrete lined segments (or culverts) may create supercritical flow with velocities that may be too high for fish to traverse and lack resting places. Loss of riparian vegetation reduces cover along the banks, and increase predation risk for fish. Barriers to movement of organisms and exchange of food, such as those mentioned above are one of the most obvious sources of fragmentation. Refugia, serve the purpose of providing a source for recolonization of disturbed habitats or aquatic systems affected by periodic abiotic stresses (Sedell et 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 54

61 Figure 6.11 Dams on streams in the New England coastal basin (from Flanagan et al., 1988) periodicity of their occurrence relative to the intensity of resource competition and predation. Habitat linkages for dispersal are the most important type of connectivity because the resultant gene flow counteracts isolation due to fragmentation (Noss and Cooperrider, 1994). Connecti-vity is the opposite of fragmentation. Some Layer II risks themselves may become measurement endpoints. For example, in the TMDL process, chemical water quality can simply be compared to water quality standards if the impairment is only due to chemical contamination and the standards are risk based and not purely administrative. Layer III - In-stream Exposure Stressors In-stream exposure stressors are the monitored or calculated series of parameter values that constitute the risks. In the case of habitat parameters, distinction between the Layer 2 metrics of the habitat index and Layer 3 stressors is fuzzy and will be investigated. For example, is velocity a Layer 2 or Layer 3 stressor? Our research may have to redefine and recategorize Layer 2 and 3 habitat parameters and metrics. Layer 3 water quality parameters must also be defined and linked to the proper Layer 2 risks. For example, suspended sediment concentrations affect the biotic integrity in two ways. First, high sediment concentrations are toxic to aquatic biota, primarily to fish. Second, suspended sediment may settle and cause impairment to the habitat which will be reflected as embeddedness (or clay content/texture of bottom sediments). Embeddedness is also related to flow parameters such as depth and energy slope that define the bottom shear stress. For toxic risk, time series and/or statistical characteristics of chemical water quality parameters are Layer 3 stressors. In general, Layer 3 stressors are: 55

62 Physical flow variation and impact of land use changes on flow, habitat impairment linked to flow variations (e.g., frequency of bankfull flows, ratio of base flow to mean flow), 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 classification and surrounding land use (e.g., higher slope/higher velocity unmodified streams with be less susceptible to sediment contamination and will generally have a better habitat than impounded or low velocity lowland streams). Water flow, water quality and sediment stressors will be expressed in probabilistic terms (mean and standard deviation, arithmetic or logarithmic). For ungauged streams we will use already developed (by USGS) relationships between these parameters (independent variables) and morphological and land use characteristics of the watershed. We will initially plot the flow data on a log-normal probability graph and use the non-parametric Kolgomorov-Smirnov test to evaluate the adequacy of the log-normal probability distribution and test other distributions only if needed. It should be noted that for biotic effects, the high flows with a recurrence interval of less than five years are important while very high floods may not be relevant. Thus, we will develop statistics for representing extremes of low (at the 7Q10) and medium high flows. Ratios among the various statistics will be useful for characterizing the flow regimes (for example, a high ratio of the extremes 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 clearly shown in Figure 1.1. The important connections between landscape and pollution include: Emissions of pollutants from various land segments. By definition of pollution, only emissions from lands altered by humans or use by humans for production or other uses may cause 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 as deforestation, wetland drainage, irrigation, or urbanization. Impact of landscape of storage of pollutants. Soils can safely retain or incorporate into the soil structure and/or decompose over 99% of potential pollutants, such as nutrients (nitrogen and phosphorus), pesticides, and organic matter. Decomposition can occur both under aerobic and anaerobic conditions. The most effective storage is attributed to saturated soils - wetlands. However, this storage capacity may be exceeded, either as a result of excessive accumulation of a pollutant or pollutants or due to human interference, e.g., as result of acid rainfall, wetland drainage. A change of the pollutant retention capacity can 56

63 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. The assimilative capacity of receiving water bodies is also related to morphological landscape factors. Higher velocity mountain streams have better aeration capacity than low land small slope sluggish streams. Depth affects the reaeration and light and heat penetration. Both velocity and depth impact habitat. The list of potential landscape descriptors is given in Table 6.6. The landscape description, such as percent of various land uses and covers, is a system parameter describing the system but it can also be related to emissions of a polluting matter (suspended solids, chemicals). Each descriptor has some relevance to the integrity of the water body draining the watershed. However, as shown by Moss et al. (1987), reducing the number of descriptors from 28 to 5 did not decrease significantly the reliability of the estimates of the biotic community distribution. The closer the emitting land uses are located to a water course, the more they affect the aquatic habitat and the aquatic components. Regarding diffuse pollution the landscape emissions are expressed in various degrees of complexity ranging from unit loads, related to the land use character, to annual or seasonal emission rates, ascertained from long term meteorologic factors, soil characteristics, slope and land cover, to medium complexity models, relating the emissions to the erosivity of individual rainfalls and leaching of chemicals from soil (Novotny, 2003). The rate of change of the landscape indicators is an important indicator of pollution stresses. Figure 1.1 identifies the land use transition or modification process. For example, increasing urbanization implies extensive occurrence of construction sites that emit extremely large sediment and associated pollutant (e.g., phosphorus) loads. Deforestation increases sediment loads by orders of magnitude (Walling and Web, 1983). Wetland drainage results in a release of large quantities of nitrogen by a change of organic nitrogen to nitrate by nitrification, a strictly aerobic process that can 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 reduced and immobile metal sulfide complexes to oxidized metallic oxyhydrate complexes that release toxic metallic ions into water (Salomons and Stol, 1995). The historic changes of landscape parameter and the rates of changes can often be estimated from aerial 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 large portions of Illinois and Indiana were covered by wetlands one hundred fifty years ago. Similarly the 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 indicator can be ascertained relatively accurately from maps and, later, from aerial photographs. The same is true for the extent and rate of change of urbanization. 57

64 Table 6.6 Landscape parameters and factors affecting the integrity of surface waters Parameter Integrity relevance Watershed morphological characteristics Area Altitude Latitude Slope of land segments Distance of disturbed land segment from the water body Watershed pedological and geological characteristics General Temperature Temperature, snowmelt pollution Erosion, suspended solids load, embeddedness Delivery of pollutants, suspended solids load Soil 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 morphology Stream order Velocity Slope Depth Frequency of bankfull flow (channel flow capacity) Pool and riffle sequence Bottom substrate texture Organic content of sediments Channel alteration Riparian vegetation, stream side cover Species diversity and density, pollutant dilution in streams, habitat Sedimentation and aeration Sedimentation and aeration, habitat, channel stability (erosion), sediment and substrate texture, composition of organisms Sedimentation and aeration, eutrophication, composition and diversity of organisms Channel stability, stream bank erosion Habitat quality Spawning and shelter for fish Sediment oxygen demand, nutrient release Fragmentation, habitat degradation Temperature, habitat shelter Emission rates of pollutants from landscape are not easily measurable because they are related to meteorological factors. Therefore, unit loads often represent long-term averages back-calculated from monitoring data in receiving waters or estimated by hydrologic models that were calibrated and verified by measurements from small uniform watersheds (Novotny and Chesters, 1981; Novotny, 2003). A methodology for determining graphical regional unit load estimates called Model Enhanced Unit Loads (MEUL), shown in Figure 6.12, was developed and used in late 1970s 58

65 and early 1980s to estimate watershed-wide pollutant loads in the priority watershed program in Wisconsin (Novotny and Bannerman, 1979). Figure 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 (adapted from 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 natural emission may not cause a water quality problem. Erosion rates from arid lands are very high and suspended solids content in systems draining arid streams can be very high, sometimes exceeding 59

66 tens or even hundreds of thousands of milligrams per liter, as documented by Nordin (1962) on Rio Puerco, an ephemeral stream draining an arid sparsely populated watershed in the northwest part of 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, typically exceeding natural loads by orders of magnitude (Novotny, 2003). A key step in watershed classification 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 emit the highest pollutant loads are listed in Table 6.7. Table 6.7 Land uses and major associated pollutant types. Land use Urban Construction sites during excavation and landscaping High density transportation Industrial High density residential and commercial Rural Animal feedlots Intensive agriculture on high slope (>3%) Clear cutting of forests Pollutant Sediment Toxics, sediment Toxics Toxics Organics, nutrients Sediment, nutrients Sediment, nutrients The proximity of the land to a water course is another important factor. This is expressed by the delivery ratio which relates the pollutant yield in the watercourse to the upland emissions of the pollutant. This effect can be included in the model by the inventory of the hazardous lands located within the riparian zone. The width of the zone may vary from approximately 30 to 100 meters and/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 vulnerability classification scheme with the goal of identifying those that are related to integrity. Watershed classification schemes reviewed by Detenbeck et al. were classified as geographically-dependent or geographically-independent. Geographically-dependent classification, as the category implies, can be applied only to one geographic area, e.g., the Northeast, while the geographicallyindependent classification schemes are not limited to a specific place or region. The authors also added classification schemes that were structurally or functionally based on a combination of the two. Geographically-dependent classification schemes locate watersheds and categorize watershed characteristics in ecoregions and ecoregional units (Omernik, 1987; Omernik and Gallant, 1989). 60

67 This system was used to rank the biotic integrity of lakes (Heiskary and Wilson, 1990) and biological criteria for streams (Yoder and Rankin, 1999). Geographically-independent classification can be constructed based on structural or functional landscape, watersheds or ecosystem/community characterization related to the stressors or ecosystem functions for aquatic ecosystems. They can include hydrology (e.g., flow variability) or sediment 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 verified watershed models. Water quality in this context is understood as a chemical and bacteriological composition of numerous water quality parameters while integrity is three dimensional and includes, in addition to chemical and hydraulic/hydrological attributes, also habitat and aquatic biota indices. Many attempts have been made to relate biotic integrity indices to diffuse pollution and watershed impairment characteristics. 61

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69 CHAPTER 7 WATERSHED VULNERABILITY INDICATORS Vulnerability Analysis For most of the 1990s, the USEPA and its contractors (e.g., Center for Watershed Protection) were developing 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 the assessment of watershed vulnerability a fundamental requirement of the WFD-mandated water quality control programs throughout Europe, extending from the original signatories of the EU to all 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 for Watershed Protection in a report/manual by Zielinski (2002). Zielinski based her methodology on a sole accounting of imperviousness within the watershed, which is based on the concept presented in Figure 3.2. Essentially, all watersheds that are more than 25% impervious do not support the goals of biotic integrity. Watersheds and sub-watersheds with imperviousness ranging from 11 to 25% 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. While there are no arguments against considering imperviousness as one of the key parameters, this approach and methodology obviously suffers from the inadequacy of using simplistic relationships between IBIs and a single surrogate parameter, as discussed in Chapter 3. Obviously, urbanization is not the only stressor. Index of Watershed Indicators In 1997, US EPA s Office of Water published the Index of Watershed Indicators, which was revised in 2002 (Spooner and Lehman, 1998; USEPA, 1997, 2002). The Index of Watershed Indicators contains two groups of parameters (Table 7.1): (1) those that describe the condition of a water body and its watershed, and (2) those that describe vulnerability. Condition parameters reflect the current status of the watershed and water quality. However, most of the parameters that have been proposed by the USEPA in the Index of Watershed Indicators focus on compliance of key water quality indicators with the existing standards or violations of existing NPDES permits (see Table 7.1). This follows the established philosophy of relying primarily on chemical standards in the assessment of the integrity status of a water body. A water body can be in compliance with the chemical water quality standards, yet the watershed and water body 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 show where discharges and other stressors impact the watershed and could, depending on the natural and manmade factors present in the watershed, cause future problems to occur. Vulnerability, therefore, 63

70 implies future problems while the condition parameters reflect the present status. Although the Index of Watershed Indicators yields two single overall numbers (one for condition and the second for vulnerability characterizations), it can also be used to identify a specific category of a problem or problems. The EPA manuals provide charts and results in national maps of watershed vulnerability that can be viewed on and downloaded from the Internet ( The index, as it is designed, should not be used to place water bodies and watershed on the TMDL requiring 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 standards Based on the percentage of waters within a watershed that are meeting all uses, as established in 1994 or 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 reservoirs Based 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 ( ) of 4 toxic pollutants: 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 ( ) of 4 conventional pollutants: ammonia, dissolved oxygen, phosphorus, and ph 7. Wetland loss index Based on data of losses of wetland area over and an historic period ( ) and more recent period ( ) Vulnerability Indicators 1. Aquatic/wetland species at risk Based 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 64

71 Table 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 rate Based on degree of population growth 7. Hydrologic modification -- dams Based on relative volumes of impounded water 8. Estuarine pollution susceptibility index Based on physical properties of an estuary and its likelihood of accumulating pollutants Figure 7.1 Overall vulnerability of watersheds based on the Index of Watershed Indicators (from USEPA, 2002) expressing conditions and vulnerability. 65