Comments on USEPA’s Proposed Nutrient Water Quality Standards for the State of Florida by
Eldon C. Blancher II, Ph.D. Sustainable Ecosystem Restoration, LLC on behalf of
CF Industries, Inc. Docket ID No. EPA-HQ-OW-2009-0596, Water Quality Standards for the State of Florida's Lakes and Flowing Waters; Proposed Rule Environmental Protection Agency 40 CFR Part 131 [EPA-HQ-OW-2009-0596; FRL-XXXX-X] [RIN 2040-AF11]
26 April 2010
Sustainable Ecosystem Restoration, LLC (SER) 775 North University Blvd. Suite 260A Mobile, Alabama 36608
Executive Summary Florida’s flowing waters, lakes, streams and springs, are ecologically diverse with unique ecological characteristics adapted to the subtropical environment. From Pensacola to Key West, no other State shares the vast array of physical, biological and geochemical conditions found in Florida. Excessive or misplaced nutrient releases into the environment can result in a process called eutrophication, the nutrient enrichment of water bodies. The eutrophication process is complex, with many steps and many factors controlling the outcome. The overlay of this complicated ecological process onto the diverse Florida physiography results in a broad spectrum of complicated responses to nutrient enrichment. The State of Florida has been engaged in the development of numeric nutrient criteria for the State’s waters for several years but has not, to date, promulgated numeric nutrient criteria. The reason for this inability to promulgate appropriate rules is that numerous analyses attempting to link nutrients to observed impacts have not resulted in statistically reliable endpoints. Simply stated, there is not sufficient scientific justification for developing numeric nutrient criteria in many of Florida’s surface waters. In 2008, a citizens’ suit was filed as allowed under the federal Clean Water Act in which several environmental organizations alleged that EPA had failed to discharge its nondiscretionary duty to develop numeric nutrient criteria for the State of Florida in that the State had failed to finalize its own criteria. In 2009, EPA entered a consent decree committing the agency to promulgating numeric nutrient criteria for Florida waters— ostensibly due to the State’s lack of progress. However, we believe that USEPA’s premise that Florida is not making progress toward solving nutrient issues within the State is without foundation. Many of the methods used by USEPA in the proposed rule are not based on scientifically justifiable assumptions, and particularly they do not develop an appropriate cause-and-effect relationship. Cause-and-effect relationships are the cornerstone of most water quality standards, particularly toxicants, and have been used successfully to develop legitimate scientifically based criteria. But, as has been pointed out by USEPA own Science Advisory Board (SAB), the Agency’s current methods and the resulting numeric nutrient criteria do not sufficiently reflect a cause-effect relationship. “The absence of a direct causative relationship between stressor and response is one of the most serious issues raised by the committee.” (SAB, Draft Advisory Report, 2010). The approaches used by USEPA for developing numeric nutrient criteria for Florida ignore those recommendations. The USEPA has proposed grossly restrictive criteria for many of Florida’s systems not in need of protection (particularly pristine reference systems) and will actually cause unimpaired systems to be declared impaired. This will require the State and affected parties to spend valuable resources and time addressing problems where none exist.
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There are numerous technical flaws in the USEPA approach, including: 1. Overall, there is an inadequate classification of Florida’s diverse systems of lakes streams, springs and estuaries, resulting in analyses that have low or in some cases no statistical relationship between cause and effect; 2. The Lakes dataset used by USEPA does not define a lake, and does not take into account many classes of lakes. The proposal lumps many different types of lake systems resulting in a large error in the resulting relationships between cause and effect; 3. USEPA does not use a cause-effect relationship for streams. The agency applies a reference stream approach, although there is no demonstrable relationship between nutrients and the response variable used – i.e., the biological community based on Florida’s Stream Condition Index (SCI); 4. The resulting reference stream approach, used when the Agency cannot demonstrate a valid scientific response between stressor and response variable, thus, is not scientifically valid. It is grossly restrictive in that it declares some reference streams impaired when they are in fact healthy. 5. Use of the Vollenweider model in the USEPA’s analysis of downstream protective values (DPV) for lakes is overly simplistic and totally inappropriate for use in Florida systems for the following reasons: i. ii. iii. iv.
It does not work in wetland, and groundwater dominated lakes; It does not work in lakes with extensive macrophytic growth; Its empirical expressions were not derived for Florida systems; Decades of Florida limnological studies show there are better models available.
6. The proposed criteria developed by the USEPA for clear streams are not based on a cause-effect relationship and therefore also are invalid; 7. The proposed criteria for canals are based on an inappropriate database, also are not based on a cause-effect relationship, and thus are scientifically invalid. The USEPA also has used untested new methods, without appropriate peer review, to derive Downstream Protective Values (DPVs ) for Florida’ s Lakes and Estuaries that are unnecessary and not based on sound science. The USEPA should accept Florida’s current approach and use the existing TMDL and BMAP programs, which have worked well in many of Florida’s estuaries, such as Tampa Bay and the Lower St. Johns River. Consequently, USEPA should withdraw its proposal. Florida’s waters are too diverse, and available data are too limited, to develop regional criteria for the State; as detailed 4/26/2010
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herein, the obstacles to establishing such criteria are evident from the numerous technical deficiencies identified in the proposed rule.
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NOTE: This report includes electronic attachments. This report is accompanied by a Digital Video Disk (DVD) with electronic copies of the documents submitted in support of the comments provided herein. All documents on the enclosed DVD are incorporated herein by reference and are intended to be part the official record under Docket ID No.: EPA-HQ-OW-2009-0596.
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Table of Contents Executive Summary ........................................................................................................ 2 Notice of DVD-ROM Attachment, List of Electronic Reference Materials…….…............ 2 Introduction ..................................................................................................................... 7 Background ..................................................................................................................... 7 USEPA Comments in Public Hearings regarding HABs .............................................. 11 Florida methods highlighted in NAS TMDL Methods review (2001) .............................. 13 Science Advisory Board Report .................................................................................... 14 General Comments on Proposed Nutrient Criteria ........................................................ 17 Specific Comments on proposed Lake Criteria- Proposed 40 C.F.R. § 131.43(c)(1) ........................................................................................................ 20 Specific Comments on proposed Stream and Rivers Criteria- Proposed 40 C.F.R. § 131.43(c)(2) ..................................................................................................... 24 Specific Comments on proposed Reference Stream Approach Proposed 40 C.F.R. § 131.43(c)(2)(i) ...................................................................................... 30 Specific Comments on proposed Downstream Protection for Lakes - 40 C.F.R. § 131.43(c)(2)(ii) .................................................................................................... 31 Specific Comments on proposed Downstream Protection for Estuaries- 40 C.F.R. § 131.43(c)(2)(ii) ...................................................................................... 36 Specific Comments on proposed Criteria for Springs and Clear Streams- 40 C.F.R. § 131.43(c)(3) .......................................................................................... 43 Specific Comments on proposed Criteria for Canals- 40 C.F.R. § 131.43(c)(3) ............ 43 Conclusions ................................................................................................................... 48 References .................................................................................................................... 50 Appendix A.: Authors Qualifications............................................................................. 58 Attachment 1 Discriminant Analysis of Stream SCI data ............................................. 63 Attachment 2: Resume of Dr. Eldon C. Blancher, II ...................................................... 74 Attachment 3: DVD-ROM of Reference Materials 4/26/2010
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List of Figures
Figure1.ThevariouscauseandeffectrelationshipsintheEutrophicationProcessshowingmultiple pointsalongthechainthatmayrespond(Source:KenReckhow,2002)........................................8 Figure2.Circlesrepresentdifferentrelationshipswheremodelscanbeappliedtodeterminevarious aspectsoftheEutrophicationchainofevents................................................................................9 Figure3.ExampleofpopulationgrowthinTampaBaywatershedrelativetochangesinChlorophyllain TampaBay,contradictingUSEPA’sclaimthattheIWRprocessisnotworking.Source:TBNMC CommentstoUSEPAEPAHQOW200905960606.1..................................................................16 Figure4.LoadreductionforTotalPhosphorusandTotalNitrogeninwastewaterprogramsegments....17 Figure5.FDEPregressionanalysisillustratingvariabilityofchlorophyllaresponsetototalphosphorusin thecoloredlakesdataset.Ofparticularconcernisthelarge“rangeofUncertainty”andthe relativelylowR2.Source:R.Frydenborg,August2009.................................................................20 Figure6.CahabaRiverincentralAlabama,shownalongwithsamplingsites..........................................28 Figure7.LowvaluesofRMSEandPredictionErrorsindicatenormalenvironmentalconditions............29 Figure8.HighvaluesofRMSEandPredictionErrorsatSite1ontheCahabaRiverindicatedeclining environmentalconditionscausedbyexcessivealgaebloom........................................................30 Figure9.SimulationofTotalPhosphorusinLakeConwayFloridaillustratingtheimpactofphosphorus loadingfrommacrophytes.(Blancher,1979)................................................................................32 Figure10.Responseoftrophicstateindicator(TSI(sd))inLakeConway,Florida,respondedtoexternal loadofnutrientsandnotlakeTPconcentration.(Source:Blancher1979)..................................33
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Introduction On January 26, 2010, the United State Environmental Protection Agency (USEPA) published proposed numeric nutrient criteria for flowing waters of the State of Florida including lakes, streams and springs. These comments are in response to USEPA’s request for public comment to Docket # EPA-HQ-OW-2009-0596. These comments represent the opinions of Sustainable Ecosystem Restoration, LLC (SER) on the proposed rule. SER’s Statement of Qualifications is provided for reference in Appendix A. Background Setting Because of Florida’s unique geology, climate and location, highly diverse physiographic units characterize the state. Florida is part of the southeastern coastal plain and the highest elevation is 345 feet above sea level with a mean elevation of 100 ft MSL. Most of the state is covered by infertile sand or loam that lies on a thick, soluble limestone layer. Because of the peninsular shape and low elevation of Florida, the riverine systems are generally short, wide and swampy in nature. The state is characterized by a subtropical to tropical climate with mild winters and hot, humid summers (Florida State University, 2010). The South Florida ecosystems have temperate and tropical characteristics and fall within five physiographic regions: Everglades, Atlantic Coastal Ridge, Eastern Flatlands, Western Flatlands and Big Cypress Swamp. These provinces are characterized by diverse ecosystems including freshwater and terrestrial components such as canals and lakes, ponds and sloughs, sawgrass marsh, wet prairies, pine forests, cypress forests, mixed swamp forests, bay heads, hardwood hammocks and palmetto and dry prairies. Coastal ecosystems include sandy beaches, mangroves and salt marshes, estuaries and bays, and the Florida reef tract (McPherson et al., 1976). The Apalachicola National Estuarine Research Reserve (ANERR) and St. Joseph Bay State Buffer Preserve (SJBSBP) are found in the Apalachicola-Chattahoochee-Flint watershed and are located in the gulf coastal lowlands physiographic province. The ANERR includes upland, floodplain, riverine, estuarine and barrier island ecosystems. Within the reserve, Apalachicola Bay provides an important nursery area for species of the Gulf of Mexico (FDEP, 2010a). Habitats within the SJBSBP are comprised of wet pine flatwoods (mesic and xeric), swamp, freshwater marsh and a tidal creek which support threatened and endangered species (FDEP, 2010f). The Manatee River Watershed in west-central Florida drains approximately 360 square miles within the Polk Upland, DeSoto Plain and Gulf Central Lowlands physiographic provinces (The Southwest Florida Water Management District, 2001; White, 1970). The “Bone Valley” area within the Polk Uplands is characterized by phosphate and other rare earth elements although mining generally occurs outside the watershed (The Southwest Florida Water Management District, 2001). 4/26/2010
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Phosphate deposits cover much of the Florida peninsula (Florida Institute of Phosphate Research, 2010). The Bone Valley and Hawthorn Formations are Florida’s two major phosphate deposits. Dolomite, clay, silica phosphate, aluminum phosphate and heavy metals are found in these deposits (El-Shall and Bogan, 1994). The primary Bone Valley mining area is located in Polk County, but mining deposits are also found in DeSoto, Manatee, Hardee and Hillsborough counties. Phosphate deposits from the Hawthorn Formation are also mined in Hamilton, Columbia and Suwannee Counties located in north Florida (Florida Institute of Phosphate Research, 2010). Florida’s biological communities have adapted to this unique and extremely diverse physiographic setting, resulting in some unique environments, particularly those adapted to high natural background nutrient conditions, high nitrogen sources in wetlands areas, and high phosphorus in those areas where the geological conditions have deposited large stores of phosphate rich minerals. Eutrophication Problems are Complex The process of eutrophication at first appears to be a simple relationship between load or concentration of nutrients and a response. But as Dr. Ken Reckhow, chair of the National Academy of Sciences (NAS) Total Maximum Daily Load (TMDL) committee, has pointed out in his TMDL presentation at TMDL 2002 in Phoenix, Arizona (Reckhow et al., 2002) it is anything but straightforward (Figure 1).
Figure 1. The various cause and effect relationships in the Eutrophication Process showing multiple points along the chain that may respond (Source: Ken Reckhow, 2002).
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The complexity of the process is overlain on the geologic, hydrologic and ecologic background, and thus there is a mosaic of effects, which may or may not be expressed in the same way even in seemingly similar situations. This is a problem for scientists studying the process, and even more so for regulators trying to simplify the process in order to apply simple models for regulatory purposes. Depending on the expression of nutrient impacts, it is difficult to decide which model or approach to use (Figure 2, Reckhow, 2002). It may be that for individual systems a single empirical model will work well–one that adequately relates nutrients directly to a measurable response such as an increase in chlorophyll a, as can be the case in certain lakes. However, because of geological, hydrological or biological differences, and other water quality variables, the expression in a similar system can be totally different. Thus, other systems may require regulation based on hypoxic conditions, whereas in still others hypoxia may be a naturally occurring event. Simple univariate empirical methods will not be applicable for all situations.
Figure 2. Circles represent different relationships where models can be applied to determine various aspects of the Eutrophication chain of events (Reckhow, 2002).
The diverse physiography thus poses challenges in trying to regulate nutrient water quality within the State. Overlain on this complex background is an even more complex eutrophication process, making the proper regulation of surface water nutrients a daunting task. Nonetheless, in the last few years, the State of Florida has made significant progress in developing scientifically based numeric nutrient criteria for Florida’s waters. To date, however, scientifically valid criteria have been developed for only a relatively few of Florida’s waters, and numeric criteria do not comprehensively 4/26/2010
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cover all waters. At the same time, the USEPA (USEPA, 2010a - FR:75 No. 16:4175) has moved forward on its court-mandated schedule1 and has developed its own proposal for numeric nutrient water quality standards for Florida. USEPA has erroneously determined that the State’s efforts at curbing problems from nutrients were insufficient to “protect applicable designated uses”. In fact much progress has been made in Florida, possibly more so than any other state in the nation, towards developing comprehensive numeric nutrient criteria. The USEPA’s proposed rule for Florida waters, which SER asserts is not scientifically defensible, will result in many of Florida’s reference water bodies to be declared impaired. This action by USEPA will only delay the State’s ongoing actions by diverting valuable economic and manpower resources to dealing with non-existent impairment problems in lieu of solving real water quality issues in the State. USEPA has made claims that Florida’s program, through its admittedly comprehensive regulatory framework was “insufficient to ensure protection of applicable designated use” (USEPA, 2010a). SER believes these claims are unfounded and erroneous. As pointed out above, the process is exceedingly complex. In spite of that complexity, numerous examples exist of large-scale systems in Florida where control of significant nutrient sources has been achieved by FDEP and other agencies within Florida, and the systems are in a quantifiable recovery sequence. SER presents some examples below. During 2009, the FDEP also invested tremendous effort into the derivation of numeric nutrient criteria for Florida surface waters by summarizing the numerous studies and data collection efforts that had been ongoing for several years with the goal of establishing numeric nutrient criteria ( website: http://www.dep.state.fl.us/water/wqssp/nutrients/tac_archive.htm). FDEP held numerous TAC, stakeholder and other meetings to discuss progress and receive public, peer and stakeholder input. As of summer 2009, the FDEP was nearing completion of a draft rule with numeric nutrient criteria for lakes and springs. The State of Florida thus has made extensive efforts to derive numeric nutrient criteria for managing its water bodies, and it has one of the best overall scientific data collection and TMDL programs, as acknowledged by the USEPA (USEPA, 2010a). In spite of these extensive efforts and one of the most comprehensive databases, the FDEP has concluded that for some systems (e.g., particularly streams) the simple stressorresponse relationship approach does not work (nutrients explain < 10% or less of the variance in the State’s database) (Frydenborg and Bartlett, 2009; Frydenborg and Weaver, 2009). The USEPA has ignored the scientific evidence that these are complex relationships and still insists that simple univariate relationships should be mandated, contrary to existing scientific understanding. SER believes that a univariate relationship for streams is not realistic and will result in the development of erroneous and inappropriate numeric nutrient criteria that will be a scientific and economic disaster for the State. [U. S. District Court Northern District of Florida, Tallahassee Division. 2009. Case: 4:08-cv-00324-RH-WCS. Document 90-2. Filed 08/25/2009. (consent decree)] 1
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USEPA Comments in Public Hearings regarding Harmful Algal Blooms (HAB’s) During the public hearings, and in the background information to EPA’s proposed rule, the USEPA presented several points regarding Harmful Algal Blooms, largely red tide events, and suggested that all of these blooms are the direct result of nutrient loading from inland waters. Some non-scientific lay members of the public echoed this misconception. We take exception to that assertion and provide the following brief review for the record. HAB Impact to Drinking Water and Human Health Factual public information is available online as supplied by the Florida Fish and Wildlife Conservation Commission (FFWCC) Fish and Wildlife Research Institute. The State legislature has recognized the need to monitor and inform the public on HAB’s and in 1998 funded a joint program between the Department of Health, the St. John’s Water Management District (where the Oklawaha chain of lakes resides) and the Florida Fish and Wildlife institute to study the issue HAB’s in Florida (FFWCC, 2010b). While several species of toxic blue-green algae or cyanobacteria are found in many of Florida’s lakes and reservoirs, including some waters used for water supplies, no illnesses related to cyanobacteria have been documented in the State of Florida (FFWCC, 2010b). First and foremost, the vast majority of Florida’s drinking water sources comes from the Floridan aquifer or other groundwater sources (FDEP 2010g). In a 2001 Report to the Florida Harmful Algal Bloom Taskforce entitled Frequently Asked Questions About Blue Green Algae (Cyanobacteria) and their Toxins, authored by scientists at the University of Miami (Fleming, 2001), illnesses from water contaminated by blue green algae were linked to consuming untreated water. The only deaths referenced in the report occurred in Brazil and were due to high levels of blue green algae or their toxins in untreated reservoir water used for dialysis. References to liver cancer were associated with laboratory mice and a mid-1980s study in China where people were drinking untreated water from a pond and ditch. “How many cases of cancer can be attributed to blue green toxins in the US (where drinking water is of high quality) remains unknown.” Red tide blooms Red tide blooms have been known for centuries and have occurred both on populated as well as pristine coastal areas. In fact, it has been reported that the Spanish commented on fish kills in Tampa Bay in the 16th century (Herald Tribune, 2006; Bay Soundings, 2006) Red tide events were scientifically documented in Florida as early as the 1840’s (FFWCC, 2010c). Florida red tide blooms are caused by Karenia brevis a common, photosynthetic dinoflagellate which is common in coastal shelf waters. Although nutrients (nitrogen and phosphorus) can play a role in sustaining in-shore red tide events, numerous other physical, chemical and biological factors are involved in the dynamics of red tide blooms (Milroy et al., 2008; Walsh et al., 2006; Steidinger 2009). 4/26/2010
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Current as well as past research has demonstrated that many factors contribute to red tide development and dynamics and account for variations in the initiation, growth, maintenance and dissipation phases of these events (Milroy et al., 2008; Walsh et al., 2006; FFWCC, 2010a; Steidinger 2009). Red tide is not simply a nutrient problem.
Hu et al. (2004) studied a dark plume event (K. brevis bloom) in South Florida using field, rainfall, river flow and satellite sensor data. Coastal runoff containing high nutrient concentrations resulting from heavy rainfall was linked to the bloom event. The dark plume had higher levels of colored dissolved organic matter (CDOM) (2-3 times) and negligible levels of inorganic nutrient and chlorophyll compared to shelf waters (Hu et al., 2004). The bloom was stimulated by the dark plume (shading) event even though inorganic nutrient levels were lower.
A red tide event offshore of Sarasota, Florida during October 1999 was analyzed using a coupled biophysical model. Factors involved in the process included flowfield transport of offshore K. brevis, grazing pressure, phytoplankton competition, nutrient availability and light levels (Milroy et al., 2008). Upwelling, cross-shelf transport, sufficient nitrogen (estuarine sources and atmospheric deposition) and phosphorus sources, as well as shading (light), played a role in bloom dynamics. Because K. brevis is shade-adapted, high levels of colored dissolved organic matter (CDOM) could provide the shading refuge for K. brevis to avoid UV damage. The occurrence of a wind driven iron-loading event stimulated Trichodesmium nitrogen fixation and subsequently increased dissolved organic nitrogen (DON) within the water column (Milroy et al., 2008).
Walsh et al. (2006) conducted numerical model analyses for Gulf of Mexico red tides, which explained the fundamentals for red tide initiation and persistence. The timeline for red tide requires specific physical, chemical and biological conditions. These conditions include: a mid-shelf nutrient supply of phosphorus with a low dissolved inorganic nitrogen to dissolved inorganic phosphorus (DIN/DIP) ratio (typically upwelled from offshore); delivery of atmospheric (windderived) iron-rich dust; dissolved organic nitrogen (DON) release by diazotrophs utilizing the dust; simultaneous grouping of diazotrophs (sun-adapted) and K. brevis (shade-adapted); K. brevis migration into near-bottom Ekman layers; upwelling transport of K. brevis to coastal waters rich in CDOM; K. brevis release of toxins resulting in fish kills, and: K. brevis growth in self-shaded environment and fed by decaying fish and diazotrophs (Walsh et al., 2006).
Most of these studies indicate that while loading of onshore nutrients may play a role in sustaining some red tide blooms, the events are primarily caused by offshore and atmospheric events that stimulate the growth of the opportunistic K brevis. These opinions have been reinforced in recent presentations by red tide researchers (Heil, 2005; Weisburg, 2010) and associated publications (Heil and Steidinger, 2009; Millroy, et al., 2008).
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By suggesting to the public that HABs result from “nutrient pollution” and implying that such blooms will be abated or eliminated through implementation of the proposed nutrient WQS rule, the Agency is misleading the public and perpetuating scientific misconceptions and misinformation. Florida methods highlighted in NAS TMDL Methods review (2001) The National Academy of Sciences (NAS) committee to assess the scientific basis of the TMDL approach in their review of the TMDL process in 2001 (NAS, 2001) recommended to USEPA that the stressor-response relationship is fundamental to measuring whether a water body can meet is designated use. Specifically they stated “The criterion used to measure whether the condition of a water body supports its designated use can be positioned at different points along the causal chain connecting stressors (such as land use activities) to biological responses in a water body.” This recommendation is highlighted because throughout the history of the Clean Water Act the methodologies applied by USEPA in developing water quality criteria to protect designated uses from excess organic materials (e.g. BOD) and toxins have followed this protocol. The NAS committee also recommended a two-tier approach for managing the TMDL process, where water bodies are first put on a “suspected” list and then after verification are put on a confirmed list for management under the appropriate regulatory actions. The NAS further recognized that the State of Florida’s program for its two-list process under its Impaired Waters Rule (IWR) for determining impaired waters, the first in the nation, is the preferred methodology for the listing-delisting process for impaired waters. While some may view this approach as tedious, it ensures that sound science is being brought to bear on the appropriate waters and that there is defendable evidence to justify the designations (which in turn justify the allocation of often substantial public and private financial resources). The nutrient criteria program proposed by USEPA will negate the progress made under this system and will replace it with an arbitrary and unscientific method for designating surface waters impaired, leading to the erroneous application of an “impaired” label to systems where no biological imbalance exists. Florida can ill afford such resource mismanagement. USEPA has stated in its proposal that the basic analytical approaches for nutrient criteria development include stressor-response relationship, reference condition approach and mechanistic modeling. All approaches should rely on the derivation a cause-response relationship. USEPA’s approach in this proposal, especially the downstream protective values and the reference stream approaches described in more detail below, do not use or rely on a cause-response relationship. USEPA presents only a proposed causal level, i.e. a target nutrient concentration, without any scientifically valid relationship or response variable to justify the level selected for many of appointed relationships in its current proposal. 4/26/2010
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Science Advisory Board Report Recently, in a review of USEPA’s draft guidance document titled Empirical Approaches for Nutrient Criteria Derivation (guidance) USEPA’s own Science Advisory Board (SAB) criticized the agency’s continued reliance on methods not based on sound science. USEPA has used these rejected methods in this proposal. Some key findings by the SAB that directly relate to the current USEPA proposal for numeric nutrient criteria are: 1. USEPA’s guidance should provide more information for data needed to characterize other stressors and constant variables (e.g. high dissolved organic carbon). 2. USEPA’s guidance focuses on total nitrogen and total phosphorus. Additional consideration should be given to the inorganic forms because the response to concentrations (and loads) occurs primarily in response to the inorganic forms. 3. It is not clear why USEPA did not include more information on mechanistic models, because these models can integrate information on the major ecosystem processes to derive quantitative estimates of effects. 4. The absence of a direct causative relationship between stressor and response is one of the most serious issues raised. 5. The reason that load-response models and not empirical stressor-response models are used is that they obviate the need for numeric nutrient criteria because they directly link nutrient load to response variables that represent water quality impairments. In other words, if an appropriate load-response model is available that predicts the eutrophication variable in a dose-response relationship, such as dissolved oxygen or chlorophyll a in response to load, an empirical stressor-response model is not necessary. 6. Single variable stressor-response relationships that explain a substantial amount of variation are likely to be uncommon for most aquatic systems (in particular streams). In other words, univariate relationships cannot adequately explain complex multivariate interactions of physical, chemical and biological characteristics that define aquatic ecosystems. 7. Numeric nutrient criteria developed and implemented without consideration of system-specific conditions (e.g., from a classification based on site types with no response variable, such as the Reference Condition approach) can lead to management actions that may have negative social and economic and unintended environmental consequences without any additional environmental protection or benefit. Many of the SAB comments are exactly the same issues we and others in the scientific community are raising with the USEPA relative to the current rule proposal. USEPA
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continues to move forward using methods that do not have the support of its own scientific advisory committee. USEPA also has claimed that its proposal will result in a more streamlined approach to water quality management within the state. SER believes the opposite is true. Because the USEPA’s proposal will result in many of the State’s most pristine waters being declared impaired, the current proposal will force the use of Site Specific Alternative Criteria (SSAC’s), which will be a more intense and expensive exercise than the State’s existing approaches. This will force the State and the regulated community to spend even more money needlessly in developing management alternatives for systems where none is necessary. In turn, this diversion of limited resources will reduce available funding and at the same time de-prioritize restoration of waters that actually suffer from impairment. State’s methods are working The well documented and developed methods Florida uses for monitoring, assessment and water quality management is not surpassed by any other state or by the USEPA itself. In fact, as noted above, Florida’s approach at regulating impaired waters through their TMDL program has received acclaim from the NAS. Examples: a) Tampa Bay - Nutrient management in the Tampa Bay watershed has been an ongoing effort by both the State, USEPA and local stakeholders for over three decades. The improvement in water quality is evidenced by dramatic decreases in nitrogen loading and the subsequent responses in numerous indicators throughout the system, most notably decreases in algal biomass (as demonstrated by chlorophyll a decreases) and increases in seagrass coverage in the majority of the system. These water quality improvements have been detailed by the Tampa Bay Nitrogen Management Consortium (TBNMC) (2010) in comments submitted to the USEPA under this docket on 8 March 2010 (EPA-HQ-OW-2009-0596-0606.1). SER agrees with the Consortium’s conclusion that “existing loads to the major bay segments of Tampa Bay provide for the full aquatic life protection and support for all designated uses in the estuary and are therefore within its assimilative capacity for nutrients”. SER concurs with the TBNMC that the USEPA should adopt their recommended loads as the protective nutrient loads for the Tampa Bay estuary, rather than USEPA’s more generally developed criteria. A sample of the data presented by the TBNMC is presented in Figure 1.
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Figure 3. Example of population growth in Tampa Bay watershed relative to changes in Chlorophyll a in Tampa Bay, contradicting USEPA’s claim that the IWR process is not working. Source: TBNMC Comments to USEPA EPAHQ-OW-2009-0596-0606.1
b) St Johns River – The TMDL approach for the Lower St. John s River has resulted in comprehensive basin wide reductions under the plan and significant progress is being made at reducing loads (Lower St. Johns River Executive Committee, 2010). An example of the load reductions to date (2009) is presented in Figure 4 for the wastewater discharge or point source loads. MS4,For municipal separate stormwater systems (s) the target load reduction for total phosphorus in the TMDL has already been achieved. The load reduction for total nitrogen has been more challenging, and while some reductions have occurred, sources of total nitrogen from extensive wetlands seem to be slowing progress for that parameter. The Lower St. Johns is another example that negates the USEPA’s contention that no progress is being made in protecting Florida’s waters.
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Figure 4. Load reduction for Total Phosphorus and Total Nitrogen in wastewater program segments.
These are just two large watershed examples that demonstrate that the State has made remarkable strides in successfully addressing eutrophication and has a comprehensive program in place to protect the designated uses of waters within the State. SER believes the claims and implications made by USEPA and others that all of the States waters are imperiled is unfounded, and further that the development of invalid numeric nutrient criteria will actually damage the ongoing progress that the state has made in curbing the impacts from nutrient pollution. General Comments on Proposed Numeric Criteria USEPA claims that it used best available science to estimate protective concentrations. This simply is not the case. In fact, the inadequacies and problems with many of the methods used in developing the proposed numeric criteria have been identified by the Science Advisory Board (SAB) in its recent review of EPA’s latest guidance. Of particular note is the SAB comment that there is a conceptual problem concerning the selection of nutrient concentrations as a stressor variable. The SAB stated “ A basic conceptual problem concerning selection of nutrient concentrations as stressor variables (as illustrated in the Guidance) is that nutrient concentrations directly control only point-in-time, point-in-space kinetics, not peak or standing stock plant biomass. ” This key conceptual issue brings to question the whole concept of regulation by concentration rather than load (which is the foundation and reasoning of the TMDL (“L for LOAD”) program). As pointed out above, the SAB anticipates the stressor-response relationship will lead to results not scientifically justifiable, such as many of the relationships inherent in USEPA’s current proposal. Another key issue recognized by the SAB is the inappropriateness of using total nutrient concentrations (i.e., TN and TP) rather than their representative inorganic forms. The validity of this issue has been argued for and recognized by scientists for decades (Eisenreich and Armstrong, 1977), but unfortunately it has not gained regulatory application, presumably because TN and TP are conceptually simpler in comparison. 4/26/2010
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However, if we are to approach the issue from a scientific point of view, inorganic forms must be considered in order to make progress. USEPA repeats erroneous assumptions by ignoring the fact that biological responses are best predicted using inorganic and available forms of nutrients and not the total form of the nutrient, even though it is widely accepted that an unknown portion of TN and TP is refractory (i.e. not available). This error adds to the Agency’s inability to derive simplistic concentration/load responses. (Note that it also adds to the overall cost of control without additional benefit.) This mechanistic concept of focusing on the available fraction, i.e. the dissolved or unassociated reactive portion of a pollutant is commonly used in dealing with responses to toxics, such as metals, because it is well established that many factors (such as organics and hardness) alter the relationship between concentration and biological response for these dose-toxic materials. It is no less important that we recognize the same type of phenomena occur with nutrients. We must account for these factors by developing scientific relationships that face reality rather than an overly conservative approach that has a high margin of error. We describe below (section on Florida Lake DPV) why the state-wide approach based on TN and TP will not work. The Florida DEP in its recent comments has also indicated that, particularly for nitrogen, the inorganic forms of nutrients should be considered from a regulatory perspective (see further discussion below). The SAB further pointed out that single stressor relationships explaining a large portion of the variability in the data are likely uncommon for most ecosystems, particularly streams. In fact, this is exactly what USEPA found in Florida streams; the USEPA acknowledged in 40 C.F.R. § 131.43(c)(2) that it was not able to demonstrate an ”available approach” between indicators (chlorophyll a) and TN or TP concentrations. The major reasons for the lack of correlation are cited in the paragraphs above. The SAB recommended that EPA use multivariate methods or mechanistic methods, where the direct relationships are more successfully modeled. However, USEPA has chosen to pursue an unsupported surrogate method, the reference stream approach, instead of pursuing methods that will develop scientifically robust and appropriate applications, such as mechanistic models. Effective nutrient management programs rely on first applying the limiting nutrient concept, which generally can be stated that control of nutrient impacts can be achieved by regulating the most limiting nutrient. The importance of this concept was brought up again by experts at the most recent FDEP nutrient TAC meeting (TAC, Tallahassee meeting 4/7/2010; http://www.dep.state.fl.us/WATER/wqssp/nutrients/). While there are systems that may be controlled by both nutrients, there are many Florida systems that are clearly controlled by one nutrient and not both (Huber et al., 1982). Accordingly, it does not make economic or scientific sense to waste efforts controlling both nutrients. A prime example of where this has worked very well is in the Tampa Bay estuary, where the nitrogen management strategy outlined by the Tampa Bay Estuary Program in its Comprehensive Conservation and Management Plan (CCMP), and the effective public 4/26/2010
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private stakeholder actions of the Tampa Bay Nitrogen Management Consortium, focus on total nitrogen controls alone to successfully achieve measurable objectives of water clarity and seagrass recovery. Note that the Tampa Bay estuary lies within the phosphate rich Bone Valley region of the state, where biological systems have adapted to naturally elevated phosphorous levels. With regard to mechanistic models, we understand that the USEPA has made efforts to apply its peer reviewed model, AQUATOX, for developing numeric nutrient criteria, but these efforts have not been made public as of this date. We recommend that it finalize those results and properly consider those findings within the context of this significant regulatory proposal.
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Specific Comments on proposed Lake Criteria- Proposed 40 C.F.R. § 131.43(c)(1) As described above, Florida is known for its highly diverse lakes reflective of the state’s diverse physiography. The lake criteria proposed by the State of Florida and subsequently mostly adopted by USEPA do not provide sufficient delineation of the characteristics of Florida lakes. Florida lakes are shallow and usually well mixed, highly variable in alkalinity, pH, nutrients, and color. This tends to result in very noisy relationships between load and response, such as observed by FDEP in colored lakes (Figure 5). In particular, Florida lakes tend to have a
Figure 5. FDEP regression analysis illustrating variability of chlorophyll a response to total phosphorus in the colored lakes dataset. Of particular concern is the large “range of Uncertainty” and the relatively low R2. Source: R. Frydenborg, August 2009
high proportion that are nitrogen limited, and for a given total phosphorus, Florida lakes have less chlorophyll a than do temperate lakes, which is true for phosphorus-limited systems as well (Baker et al., 1981). We understand that there are systems in Florida which have seasonal or even longer hydrologic periods that may shift the limiting nutrient in lakes, but in several areas, especially the Bone Valley area, limitation by phosphorus is unlikely. We question the utility in having to manage phosphorus in those systems, especially if it can be demonstrated that phosphorus does not exert any 4/26/2010
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control in them. Moreover, many lakes in the phosphate-rich regions (Bone Valley and North Central regions) are man-made and eutrophic but provide exceptional recreational value through world-class fisheries use. USEPA ignores these unique and valuable water bodies. We have the following technical issues related to the lake numeric nutrient criteria proposed by USEPA. 1. USEPA has not adequately defined “Lake” in its proposal. The vague reference to “open waters” would result in inclusion of wetlands, ephemeral ponds, and seasonally inundated areas not classified as wetlands or lakes by the US Army Corps of Engineers or the FDEP. We recommend that USEPA adopt the definition of lakes as provided by FDEP in its recent comments to the USEPA (FDEP, 2010d). Specifically, the State of Florida uses the 2 acre threshold to differentiate lakes and ponds from wetland systems which have vastly different water quality expectations. EPA’s proposed rule defines a lake as an ambiguous “freshwater water body that is not a stream or other watercourse with some open contiguous water free from emergent vegetation.” Both a size and temporal threshold (e.g. permanent) should be applied in the definition. 2. The criteria are based on concentrations of nutrients and not loading of nutrients, an approach that ignores how lakes respond to nutrient inputs, as pointed out by the SAB in comments above. 3. We question that 20 μg/l chl a is appropriate for lakes. This concern is based on paleolimnological data showing higher TP and chlorophyll a in many Florida lakes historically. For example, paleolimnological studies from Lake Wauberg and Lake Hancock have shown these lakes to be eutrophic long before they were influenced by European settlement (Whitmore and Brenner, 2002; Whitmore 2003). Lakes with high levels of fisheries production typically have higher levels of chlorophyll a, so if fishing is an existing use, a chlorophyll a limit of 20 μg/l may limit fisheries production (Hoyer & Canfield, 1991; 1996). In addition, many lakes in the Bone Valley have historically higher chlorophyll a due to high phosphorus background levels. To suppose that the eutrophic condition is not a natural occurrence in Florida is to deny the facts at hand. 4. The selection of chl a of 6 μg/L for acidic clear lakes is not linked to any biological effect and hence is inappropriate as an endpoint. USEPA’s criterion is based upon an incorrect application of a method based on Salas and Martino (1991), and used a region-specific (St. Johns region) application of the morphoedaphic method, which is not appropriate for application in other Florida lake systems. FDEP had used a chlorophyll a level of 9 μg/L as being protective based on their derivation for Florida systems, and the decision of the State’s nutrient TAC, whose members have extensive and varied experience working with Florida Lake systems.
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5. The very low concentrations of phosphorus and chlorophyll and high water clarity in acidic sand hill lakes likely are related to the “oligotrophication” phenomenon that has been described for acidic lakes (e.g., Ogburn and Brezonik 1986). Phosphorus binding to sediments is especially strong in low pH environments, as shown by Detenbeck and Brezonik (1991a,b), and this may make such lakes less sensitive to phosphorus loading than is the case for comparable lakes with circumneutral pH. Consequently, it is appropriate to establish separate stressorresponse (TP vs. chl a) relationships and separately calibrated phosphorus loading models for such lakes. 6. A chlorophyll a level either of 9 μg/L or 6 μg/L may not be sufficiently protective of those oligotrophic systems that naturally had a chlorophyll a level much less than 6. As a corollary to the above comments, it may be more appropriate to identify a special class of sand-hill lakes, such as those in the Trail Ridge, and develop separate criteria for that very special class of very clear lakes. This would avoid including those lakes in the general population of clear acidic lakes, where a criterion of 9–10 μg/L chlorophyll a would be more appropriate and thus avoid creating overly restrictive criteria for some lakes simply because they were lumped in with a special class of lakes that may need lower criteria. We again suggest that the classification scheme for lakes needs to be more diverse than the one currently proposed by USEPA. 7. The rule does not take into account the limiting nutrient concept and the economy of classifying systems by only one limiting nutrient where appropriate. There is no need to regulate both nutrients in many systems that are clearly N or P limited. This is particularly the case in the Bone Valley where numerous lakes are N limited. The methodology to accomplish this has been developed previously for Florida lakes (Huber et al., 1982) and could be effectively used here to simplify the rule. 8. The datasets used indicate a high level of variability, as pointed out previously in these comments, which demand the need for a finer lake classification scheme. Again, previous classification schemes are available (Huber et al., 1982; Hoyer et al., 2002) that define lakes in multivariate relationships and can be a starting point for a more appropriate lake classification methodology. 9. Neither the data nor the classification methods used in the rule capture reclaimed/manmade lakes in the dataset that was used to classify lake systems. In particular, they do not adequately capture the intended use of artificial lake systems, which is primarily a fisheries/recreational designated use, for which they were designed. These lakes are typically eutrophic and highly productive (Pratt et al., 1985) and are not included in dataset and need better classification. These
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systems need to be eliminated from the rule, or at least treated separately within the rule, so that they are appropriately characterized for nutrient regulation. 10. Once again SER points out the fact that nutrient-chl a response in Florida lakes is different than in north-temperate systems. Sub-tropical and tropical Florida lakes with associated wetlands and submersed macrophytes do not respond in the same way as in many models published outside of Florida. Please see discussion below related to the Lakes DPV for more explanation. As pointed out above, it is our opinion that USEPA and the State of Florida need to develop a more diverse classification system with more appropriate chlorophyll a targets, based on the broader range of designated or existing use. We suggested that the sand-hill lakes may be better served by lower criteria, where exceptional clarity is important to recreation and swimming , whereas manmade lakes, such as those reclaimed lakes in the Bone Valley area, may be better served by choosing chlorophyll a endpoints, likely higher than 20 μg/l, where fishing uses are dominant. This is similar to the application of “existing use” in Alabama where manmade reservoirs were classified based on their use, those reservoirs where fishing was prevalent had chlorophyll a targets of 27 μg/L to protect fishing uses where as a chlorophyll a target of 5 μg/L were developed for reservoirs where water clarity was important to protect swimming uses (ADEM, 2008).
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Specific Comments on Streams and Rivers Criteria Proposed 40 C.F.R. § 131.43(c)(2) Instream Numeric Criteria The USEPA used a database based on a “Stream Condition Index” (SCI) and a statistical analysis of the SCI and data on TN and TP concentrations and TN:TP ratios in Florida streams to develop proposed nutrient criteria for Florida streams. The SCI is based on species composition data on stream benthic invertebrates, and this represents a major scientific flaw in the development of the nutrient criteria. The primary basis for benthic invertebrate indices for streams, such as Beck’s original index and later indices like the SCI, is the sensitivity or lack of sensitivity of various benthic invertebrate species to organic enrichment and related conditions of oxygen status. Such indices, including Florida’s SCI, were not developed to indicate nutrient conditions in streams. Of course, there may be indirect correlations because enrichment of organic matter from wastewater (in the past, the main anthropogenic source of organic matter in streams) also entails enrichment by nitrogen and phosphorus. However, there is no cause-effect relationship in such indices, and high nutrient concentrations in stream water per se will not cause shifts in benthic invertebrate community composition, leading to unacceptably low values of invertebrate indices such as the SCI. Consequently, there is no valid technical basis for the USEPA to base its nutrient criteria on the SCI. In addition to the direct effects of organic content and oxygen status on the composition of stream benthic invertebrate communities, physical (habitat) factors, such as stream velocity and size of bottom substrate particles, are involved indirectly in that they affect the buildup of organic matter in bottom sediments. Concentrations of nutrients in stream water are not a primary driving force for this process in most stream environments, however, even though they stimulate algal growth and thus the build-up of organic matter. This is because planktonic algae tend to be swept downstream by currents; streambeds typically are not long-term depositional environments for organic matter. Another major flaw in the EPA’s development of nutrient criteria for streams is its use of an arbitrary value (the 75th percentile of TN and TP concentrations) to define the criteria for streams meeting the minimum acceptable SCI value of 40. This percentile was based on a statistical analysis to minimize type 1 and type 2 statistical errors, but it does not have any other technical basis. No statistical analysis is presented to show that there is a correlation between N and P concentrations and the SCI, which is critically important if the SCI is to serve as the basis for the criteria. The frequency distribution plots in Figures 2.14-2.16 are not adequate to show that a quantitative relationship exists between nutrient concentrations and the SCI.
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It seems inexplicable that the EPA developed its stream classifications based only on nutrient ratios and not on nutrient concentrations. Although there is a fairly large range in TN:TP ratios among the streams and defined regions, there also is a large withinclass range for the Panhandle and Peninsula classes, and the number of watersheds with data is insufficient for the other two classes to evaluate within-class variability. In addition, the average values presented indicate that all the classes except the phosphorus-rich Bone Valley region (median TN:TP of 9) are potentially P limited or have balanced N:P ratios. Classifications based on TN:TP ratios are of doubtful utility in relation to limiting nutrient conditions in streams, given the fact that large but variable fractions of the TN in Florida streams are bound to humic matter, likely from wetlands and not available for plant growth. The FDEP dataset used by USEPA did not lead to any scientifically justifiable relationship between nutrient levels and SCI response in streams. This criticism was echoed by FDEP in its review of the Agency’s proposal as well (FDEP, 2010e). In FDEP’s analysis, nutrients (TN and TP) alone explain only a minute fraction of the observed variance in the best available dataset of streams based on use of the Stream Condition Index as a measure of impairment. Several reasons the FDEP attributes to this outcome include: 1. Communities in streams and rivers do not respond specifically to nutrient concentrations but to loads (as opposed to toxicants), and, as pointed out by the SAB above, point-in-time or point-in-space kinetics do not adequately characterize these systems, nor do they relate, as the data used by USEPA testify to, any demonstrated response variables. 2. The proposal does not adequately classify the types of streams or the geological regions from where they occur, which also leads to a large portion of the variability in the dataset. USEPA should classify the stream systems into finer subsystems as to physiography, geology ecology and existing use before deriving any applicable nutrient criteria. 3. The regulatory approach considers only TN and TP and not inorganic forms. Eutrophication responses in many Florida wetland and submerged macrophyte dominated systems respond to inorganic forms and not organic nutrient forms. 4. Single stressor relationships fail to account for other variables (e.g., light [shading, turbidity, color], substrate, grazers, etc.) which all contribute to the observed variability in the dataset, and these should be taken into account in the classification scheme as indicated by the SAB in their comments. 5. Mechanistic approaches are available; these were pursued by USEPA but the results were ignored. These approaches need to be considered because they may point to additional factors affecting eutrophication response, including light,
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grazers, habitat and other limitations with have more to do with landscape changes than nutrients. It has been stated by the USEPA, FDEP and many commenter’s to this rule that macroinvertebrates in streams do not respond to nutrients in a simple dose-response manner. In fact, if one looks at the Reckhow model (Figure 1), it becomes clear that macroinvertebrates are several steps down the chain from primary producer response; they do not respond to nutrients but to organic matter and redox conditions. This was adequately demonstrated by the FDEP in their studies and has been echoed by many in public and in written comments. SER analyzed the all stream data (Stressor ID SCI2007 Datafile 09-23-09) obtained from FDEP using step-wise discriminant analysis as an exploratory exercise. This multivariate statistical procedure extracts classification variables that can be used to separate observations on the basis of the multivariable dataset. In stepwise mode, it removes variables sequentially in order of the variables that contribute most to the overall variance in the multivariable dataset. It then recalculates the remaining variation and then repeats the process until all the variables contributing significantly to the overall data variation are removed. This results in an ordered list of the variables that explain statistically significant portions of the variability in the dataset. The results of this procedure to explain the variation in the SCI performed by SER are presented in the following Table: Table 1. Results of Stepwise discriminant analysis to determine response of the SCI to physiochemical parameters in the “SCI” FDEP database. Note that none of the nutrient variables (highlighted) have been included in the analysis, which indicates they do not explain a statistically significant proportion of the multivariate dataset. Effect Steps
Summary of stepwise regression; variable: SCI_2007 Forward stepwise P to enter: .05, P to remove: .05 Degr. of Freedom Step Number 12
Conductivity, field Habitat Assessment Score Bank Stability Score (Right Bank) Dissolved Oxygen Water Velocity Score Substrate Diversity Substrate Availability Riparian Buffer Zone Width (Right Ban pH Riparian Zone Vegetation Quality (Rig Artificial Channelization Score Habitat Smothering Score Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank "NO2NO3N" Riparian Zone Vegetation Quality (Lef Water Kjeldahl Nitrogen Habitat Assmt Secondary_sum Bank Stability Score (Left Bank) AmmoniaN Water Total_P Turbidity Color
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F to remove P to remove F to enter 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
61.80340 54.65500 36.92080 17.89530 12.37960 21.83730 16.43070 8.92510 4.75850 4.27440 3.15000
0.00000 0.00000 0.00000 0.00000 0.00001 0.00000 0.00000 0.00014 0.00872 0.01411 0.04316 0.71410 0.76670 1.36230 1.90730 0.35580 1.91420 0.71980 0.82610 2.05200 1.96850 0.76620 0.39980
P to enter Effect status In In In In In In In In In In In 0.48981 Out 0.46475 Out 0.25642 Out 0.14888 Out 0.70067 Out 0.14785 Out 0.48702 Out 0.43797 Out 0.12887 Out 0.14006 Out 0.46498 Out 0.67051 Out
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It is significant to note that no nutrient variables were selected by the statistical analysis. This corroborates the FDEP findings that nutrients explained less than 10% of the variation in the dataset. In fact, nutrients do not explain a statistically significant portion of the variance in the dataset and thus do not contribute to the SCI response according to the discriminant model. The resulting discriminant functions correctly classify the about 61% of the sites according to the SCI database (Table 2). The classification functions selected 11 physico-chemical variables that explain the statistically significant variation in the dataset. The greatest influence on the dataset is conductivity, which is an indicator of ionic strength, which along with pH, varies with geology, hydrology (i.e. meteorology) and proximity to coastal areas. The second strongest set of variables involves habitat factors, including substrate and streamflow, which likely indicate structural changes in the streams due to landscape changes. The fourth variable selected was dissolved oxygen, which is related to organic content. All of these variables are well known to aquatic biologists as controlling factors for macroinvertebrate community composition. It is also significant to note, perhaps, that color showed no influence on the SCI outcome. Although the selected variables collectively contribute to the variance in the all streams database, they are unable to fully discriminate between the SCI categories with 100% confidence. The output from the analysis is presented in Appendix B. Table 2. Results of stepwise discriminant analysis of the SCI database. The overall model based on 11 variables correctly predicted the SCI category 61.5% of the time overall and did not include any nutrient variable. The analysis performed worst for Category 1 SCI locations, correctly predicting only 20.9 % of the cases correctly.
The proposed numeric nutrient criteria were derived without a valid stressor-response relationship and therefore are seriously flawed. A simple univariate relationship does not adequately describe ecosystem responses to multivariate factors controlling Florida’s aquatic ecosystems. In the opinion of SER, the proposed numeric nutrient criteria are not simply overly restrictive; they are scientifically unsupportable. We recommend that either a multivariate approach or a mechanistic approach be used to classify the streams according to region and physiography and to derive appropriate methods for defining when these systems are impaired. 4/26/2010
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Suggested Alternative Approach for Streams SER proposes that the diverse streams be subdivided, at a minimum, into appropriate state regions and then progressively by geology, ecology and designated use. Once these classifications are determined, samples from those regions should be classified by conductivity, habitat score and dissolved oxygen and other variables. This may be accomplished using multivariate statistical methods. The technology is available to define expected nutrient regimes for each classification type using mechanistic models such as AQUATOX, which can be simulate the stream reach as to hydrology, habitat and primary producer response to a level protective of each stream type. Variations on this approach have been performed on streams in Minnesota (Carleton et al., 2009) and on the Cahaba River in Alabama (Blancher et al., 2002). Based on the model, streams could then be divided into nutrient regimes that are likely to result in biological impacts, and these could be placed on a “suspect” list. Those stream reaches so identified would then be subject to biological verification using the SCI or an algal study such as FDEP’s periphyton procedure. However, this may not be protective enough because macroinvertebrates may not respond to nutrient loadings until the organic load is high enough to impair macroinvertebrate communities; in fast flowing streams this may only occur under extreme circumstances. In fact this has been analytically verified in comments made by others for this rulemaking (Hydroqual, 2010; NCASI, 2010). Independently, we have observed healthy macroinvertebrate communities in streams with high nutrient loads, (e.g. the Cahaba River) that even though there is a very large biomass of periphytic algae at particular reaches (TAI Environmental Sciences, 2001, 2002; Howell and Davenport, 2000, 2001, 2002; Davenport et al., 2005). An additional and very practical verification of community imbalance could be noted in the continuous dissolved oxygen record (Wool et al., 2003; Cox et al., 2007). This also was suggested by Reckhow et al. (2002) because eutrophication impact is often expressed by hypoxic conditions.
Figure 7. 6. Cahaba CahabaRiver Riverin in in central central Alabama, Alabama, shown along shown with along withsites. sampling sites. sampling sites.
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Dissolved oxygen (DO) is a widely used indicator of river condition and water quality. DO in a water body is affected by many physical factors (surface reaeration, winds, tides, vertical mixing, etc.) and biogeochemical (plant photosynthesis, respiration, etc.) processes. Under normal conditions, DO can be modeled using parameters such as water temperature and conductivity, which are regularly measured concurrently with DO. However, when environmental conditions decline, such as when nutrient concentrations increase in the river, algae are able to grow very rapidly resulting in large-scale fluctuations in DO levels that lead to stress on aquatic life and a decline in ecological conditions in the aquatic system. The Cahaba River (Figure 6) and its surrounding watershed provide recreation and Page 28
drinking water to the surrounding population around Birmingham, Alabama. Habitat changes due to sediment loading and algal blooms have negatively impacted the ecosystem in portions of the river, threatening the water quality and life sustainability of the river. In an effort to detect large scale algae blooms by monitoring diurnal DO variations, TAI Scientists (TAI Environmental Sciences, 2001, 2002) conducted a study where values of DO, temp, pH, specific conductivity, and turbidity were continuously sampled at several sites along the Cahaba River. A dynamic Partial Least Squares (dPLS) model was developed that used the causative parameters of temp, pH, specific conductivity, and turbidity as the input to the model, which then predicted the values of DO. Under normal conditions, variations in DO were explained by the variations in the causative parameters (Figure 7). However, when large scale algae blooms occurred, DO variations could not be explained by the causative parameters, and the resulting high values of the model Root Mean Square Error (RMSE) and prediction error indicated abnormally high variations in DO caused by excessive algal bloom (Figure 8).
Figure 8. Low values of RMSE and Prediction Errors indicate normal environmental conditions.
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Figure 9. High values of RMSE and Prediction Errors at Site 1 on the Cahaba River indicate declining environmental conditions caused by excessive algae bloom.
As stated previously, nutrients do not typically cause stress to ecosystems but initiate a chain of production events, which in turn stimulate respiration and decomposition. This in turn may cause stress in ecosystems, when hydrologic conditions permit excess production materials to remain, and reduce oxygen to stressful levels. Thus, the key to regulating nutrients is to define when nutrients stimulate excess production beyond the capacity of the system to regulate. The methods described above, first using mechanistic or probabilistic models to identify streams where nutrients may be at levels that could cause problems, followed by verification of impacts by observing biology and dissolved oxygen, would provide a more robust and scientifically valid way to manage nutrients in Florida’s streams. Reference Stream Approach Proposed 40 C.F.R. § 131.43(c)(2)(i) The reference stream approach utilized by USEPA is not valid because it incorrectly attempts to classify all streams within a diverse set of regions, geology and ecology on the assumption that nutrients are controlling system response. These approaches and assumptions are scientifically invalid and inappropriate, as we have pointed out above. There is no valid scientific basis for relating the SCI to nutrients in the State’s database.
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FDEP has commented extensively on their view of the limitations of the reference stream approach used by the USEPA (FDEP, 2010e) to designate a stream ecosystem as impaired, based on its nutrient regime. This is why FDEP introduced the concept of a verification procedure using the SCI. In particular, as stressed many times by FDEP staff in numerous meetings, the reference stream approach must be backed up by biological validation of impairment. The major failings of the reference stream approach used by USEPA are as follows. a) It results in declaring an excessive number of un-impacted streams to be impaired by nutrients when there is no valid relationship to demonstrate that nutrients are even a factor in the vast number of streams. b) The selected percentile is not based on the actual status of the aquatic environments but is an arbitrary decision or one based on statistical concerns about type 1 and type 2 errors. The selection of the 75th percentile has been used by USEPA, but has been criticized by both FDEP as well as many scientists giving comments during the USEPA public hearings. c) Again, the reference stream approach uses no valid response variable which relate to nutrient concentrations, which leads to the assumption that certain sites are impaired where no evidence of impairment exists. Downstream Protective values USEPA presented two methods to provide downstream protection of surface water bodies, one each for lakes and estuaries. The approach for lakes is derived on the Vollenweider (1976) model, and the one for estuaries uses an unpublished (and unvetted) method proposed by USEPA for the first time in this proposal.
Proposed Downstream Protection for Lakes Proposed 40 C.F.R. § 131.43(c)(2)(ii) The USEPA developed downstream protective values for lakes based on the Vollenweider (1976) relationship, which was developed for temperate lakes. Overall, we found the analysis presented by USEPA to be simplistic, superficial and not scientifically robust. The model has numerous shortcomings for application for Florida systems which have been pointed out by many authors in the past. The primary issues are that the original relationship was developed for temperate lakes that generally are large and deep (thermally stratified), and it uses critical loading criteria based in part on faulty chlorophyll a regressions. The lake DPVs based on the Vollenweider model are not appropriate for use in Florida because of the following limitations. 1. Vollenweider’s (1976) model is based on assumptions that are not valid for many Florida Lakes. These assumptions are: totally mixed tank reactor; lake concentration is equal to outflow concentration; and the 4/26/2010
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system is at steady state. It also assumes that the major primary producers in the system are planktonic algae and that there is a proportional biomass response, expressed as chlorophyll a, to nutrient loading. These assumptions are not true for a large number of Florida lakes. The relationship does not work in macrophyte dominated lakes or in shallow wind mixed lakes which are common in Florida. Of particular note is the fact that many of the Florida systems are dominated by macrophytes. The Vollenweider approach cannot address nutrient issues in these systems. Use of phosphorus models in macrophyte dominated lakes has demonstrated that algal response of the macrophyte dominated systems is more related to external loads then from the non-reactive phytins (likely inositol polyphosphates - structural components in cell walls) which do not stimulate plant growth since algal enzymes such as alkaline phosphatase cannot cleave multi-phosphate bonds. For example, in constructing a mechanistic model for phosphorus for Lake Conway, in Orange County Florida, a chain of fused mesotrophic dolines with macrophytes covering approximately 50% of the surface area, Blancher (1979, 1980) found that until internal loading of total phosphorus from macrophytes was accounted for, the model could not explain the level of phosphorus from external sources alone (Figure 9). In fact, the level of total phosphorus loading from macrophytes to balance the model
Figure 10. Simulation of Total Phosphorus in Lake Conway Florida illustrating the impact of phosphorus loading from macrophytes. (Blancher, 1979)
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comprised 75% of the total load (0.66 g m-2 yr-1 for internal vs. 0.22 g m-2 y-1 from external loading). More importantly, the trophic response of the lake was not related to total phosphorus concentrations because much of the total particulate phosphorus was tied up in structural plant components that could not be used directly for phytoplankton growth (Figure 10). Thus, a serious problem with Vollenweider’s approach, as demonstrated in Lake Conway, is that it cannot be used to predict average phosphorus concentrations in Florida systems dominated by macrophytes.
Figure 11. Response of trophic state indicator (TSI(sd)) in Lake Conway, Florida, responded to external load of nutrients and not lake TP concentration. (Source: Blancher 1979)
The above findings have serious implications for the development of numeric nutrient criteria. The trophic response, in this case chlorophyll a, especially in lakes with significant macrophyte populations, responds to external load but has no relationship to the total phosphorus in the lake. 4/26/2010
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Thus, there is no relationship between the response variable (chlorophyll a) and total phosphorus, invalidating any numeric nutrient criteria based on total phosphorus concentrations. 2. Another serious issue with the Vollenweider (1976) model is the sedimentation term, which was developed using data from north temperate lakes in North America and Europe. Vollenweider provided a check for the sedimentation term, the concept of relative phosphorus residence time r defined as:
r
=
p/w =
1/w__ ~ ____1_____ (Vollenweider, 1976) 1/w + p
1 + w
For Lake Conway, r calculated from the third term is 0.053, but if it is calculated from the fourth term, the value is 0.231 (Blancher, 1979). According to Vollenweider (1976), this may indicate that accumulation of phosphorus in the sediments occurs at a greater rate than in the lakes he used in his study. This corroborates the findings of Shannon and Brezonik (1972a, 1972b) and of later studies (Baker et al., 1981) that Florida subtropical lake systems are able to assimilate more nutrients than would be expected for temperate systems. This result should not be surprising given that the higher temperatures in Florida lakes would explain why Florida systems process nutrients faster than temperate systems, and this has a dramatic effect on the sedimentation rate. 3. Extensive work by Florida limnologists (Baker et al,. 1981, Huber et al., 1982) has shown that the modified Dillon-Rigler relationship provides a better fit for predicting both phosphorus and nitrogen. These studies are well known and have been cited for more than two decades. The modifications were necessary because these studies showed that the Vollenweider models did not account properly for the higher productivity (per unit phosphorus and nitrogen) in Florida’s shallow and more highly colored lakes present. 4. The USEPA’s approach does not account for all loads. Atmospheric sources and surficial groundwater (seepage) are not accounted for in the model, and these are significant, particularly in Florida’s karst regions. In fact, USEPA did not consider loadings from diffuse sources such as shallow subsurface flows. As USEPA points out in the proposed rule, only 20% of Florida lakes have defined stream inflows (Fernald & Purdum, 4/26/2010
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1998). This is a particularly serious limitation because many of the systems USEPA is trying to protect will not be described by the Vollenweider approach. USEPA’s application of Vollenweider’s model has additional shortcomings, many of which have been pointed out in FDEP’s comments (FDEP, 2010c). Another significant shortcoming is the selection of the critical loading values based on a loading chlorophyll a predictive regression derived from streams. It is unclear in the USEPA technical support document (TSD; USEPA 2010b) how the Vollenweider model performs in terms of predictive value, using the critical TP and TN values proposed by USEPA. It appears that the USEPA mixed regressions using an analysis from streams and applying it to lakes without comparing the model outcome to any observations. Thus, we cannot judge how the equation performs relative to actual lake observations. Again, USEPA failed to provide an appropriate dose-response relationship to illustrate how lakes are classified with the new relationships. Although the FDEP provided some comparisons for eutrophic systems (FDEP DPV, 2010c), where the USEPA relationship does not work, the ability of the model across the broader range of lakes has not been tested. The use of the Vollenweider single reactor model for large shallow lakes (such as Lake Okeechobee) and wetland-linked lake ecosystems is not appropriate. These systems do not respond as a single reactor and often show varying responses from one lake to another and even within a single lake. For example, in Lake Okeechobee, the northern portion of the lake responded differently during wet-dry seasons because of differential loadings from different portions of the watershed. The northern part exhibited phosphorus limitation while the southern portion exhibited nitrogen limitation during the same dry season and the trend reversed during the wet season (Brezonik, et al. 1979). Although the USEPA apparently was familiar with these Florida-specific (and more appropriate) models, they chose not to use them. SER recommends that the USEPA revisit existing models specific to Florida and take these into account when developing models for Florida lake systems. In particular, models of the Dillon-Rigler type should be evaluated, using modified criteria, and the models should be tested by taking recent observations in a set of lakes to evaluate how well they predict nutrient conditions. USEPA asked for input on reversing the sign on the “sedimentation” term because some lakes periodically have negative sedimentation. As FDEP (2010c) has pointed out, at steady state, lakes are net sinks, and to reverse the sign on the sedimentation term is not reflective of either the model constraints or lake systems in general. A bigger issue is when does one decide whether to change the sign or not? This is a very subjective decision, and does not fit with either the basic assumptions of the 4/26/2010
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Vollenweider model or the behavior of Florida lake systems. A simple model applicable to Florida lakes that explicitly considered internal phosphorus loading (from bottom sediments of lakes) was described by Brezonik and Pollman (1999). Finally, none of the Vollenweider type models or available input-output loading models appropriately address nutrient concentrations in lakes where either groundwater or macrophyte inputs are significant sources of nutrient input, such as exists with reclaimed or lakes associated with wetlands and large expanses of macrophytes. Unless FDEP can craft a classification scheme that adequately categorizes the various lake systems in Florida, we cannot support any of the lake proposals for numeric nutrient criteria because they are not scientifically credible. Neither the USEPA nor FDEP considered reclaimed lakes, i.e. manmade lakes when developing their classification schemes. Additional significant refinement of the classification schemes for lakes is necessary. Specific Comments on Proposed Downstream Protection for Estuaries – 40 C.F.R. § 131.43(c)(2)(iii) We understand that the downstream protective value (DPVs) approach for estuaries will be postponed until next year when the State of Florida and USEPA propose estuarine numeric nutrient criteria. Nonetheless, it is appropriate to comment on our understanding of the DPV proposal and suggest alternatives for USEPA’s consideration, particularly because some of the proposed methods have not been subjected to formal review (to our knowledge) by any USEPA peer review group and because these methods use some concepts that we believe do not adhere to the SAB recommendations. General comments on application of the SPARROW model for derivation of a DPV The USEPA has used the USGS SPARROW model, which for this application was derived for the entire southeastern US and has applied the derived formulae in a novel method to derive DPV for Florida estuaries. We regard this as an inappropriate application of the SPARROW model and point out the following issues with USEPA’s application. a) The analytical approach used by USEPA to derive the DPV is fundamentally flawed because no response variable was used by the agency to compare with the loads/concentration (i.e., again the USEPA did not provide a demonstrated cause-effect relationship, which again violates recent SAB recommendations). The USEPA needs to define an appropriate response variable(s) to be used in estuarine systems. Further, as a requirement of that analysis it is SER’s opinion that the USEPA must define a scientifically justifiable and demonstrated linkage between the causal parameters and the response variable chosen by USEPA. 4/26/2010
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b) There has been no external peer review of the approach, which is a totally new conceptual framework for regulatory purposes. Modeling approaches are generally required to have significant internal and external peer review, and it appears in their rush to get this approach “to press”, USEPA ignored its own protocols in the application of a new method. c) The DPV’s are predicated on an arbitrary and non-validated endpoint, the decision that ½ existing loads was picked out of the air and has no relationship to any response variable in the downstream estuaries and hence scientifically invalid and grossly inappropriate. Again SER reiterates that the USEPA must develop a scientifically justifiable stressor-response relationship as recommended by the SAB. d) The FDEP has noted numerous errors in the SPARROW including the fact that it is not based on cause & effect and other serious issues which seriously impacts the validity of the SPARROW model results. SER specifically refers to the comments prepared by Wayne Magley of FDEP and the USGS response to those comments (Magley, pers. comm., 2-1-2010; FDEP, 2009; FDEP, 2010b). There were many serious technical issues with the model runs made by USGS, particularly data errors, including misplaced sampling stations, outfall locations not represented correctly, especially where known interbasin transfers were known to exist, and other data related issues. FDEP comments highlight significant modeling issues such as the treatment of some reservoirs in the RF1 level database as simply stream reaches with no retention and with velocities calculated in SPARROW which exceed median observed velocities in some stream reaches by as much as 10 times (i.e. 0.15 fps calculated by SPARROW as 1.5 fps) While USGS is preparing a corrected dataset at a better resolution (1:100,000 representation versus 1:500,000 representation currently being used) and will rerun the model, many questions regarding the accuracy of the results are still in question by FDEP. SER respectively suggests that the USGS run the SPARROW model using Florida specific watershed and using data at a scale that reflects its intended use. The current implementation of the model is not suitable for use in the regulatory arena in Florida. It is unlikely that any application of the SPARROW model will be meaningful.
Statistical Concerns with the Methods used to Estimate Protective Estuary Loads and DPVs Error of Model – 35% While the R-square appears to indicate a “good fit” of the model, the basic model underlying the estimated estuary TN loads results in a Root Mean Square Error of 35%. This means that on average, a point prediction will have an error of 35%. This leads to wide confidence intervals for the TN load predictions. Because this is the first step of the 4/26/2010
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methodology that leads to the DPVs, this much error in the predictions could lead to highly erroneous TN protective loads for the estuaries and therefore erroneous DPVs. Error of predictions – Individual errors often much larger than 35% The estimated estuary TN loads are found by adding the TN loads from the terminal reaches and shoreline reaches leading to each estuary. Each of the estimated loads for the reaches is derived from the SAGT SPARROW model with 35% error, on average. Many of these reaches have a much higher error of prediction due, in part, to the size of the watershed associated with the reach. The standard error for the Withlacoochee River Estuary – which consists of one terminal reach estimate – is 45%. This means that while the point estimate for the TN load for the estuary is around 450,000 kg/y, the 90% confidence interval spans from approximately 200,000 kg/y to over 800,000 kg/y. Recall that the interpretation of a 90% confidence interval is that we are 90% certain that the true TN load is between 200,000 kg/y and 800,000 kg/y. This is a broad range. Basing further analysis on a point estimate with this much error may lead to invalid results. This relatively large RMSE is likely in part due to the large variability in the nitrogen loads for the calibration data set. This variability may be lessened if the calibration dataset is limited to Florida sites. In addition factors in the model to account for the aquifer factor and/or other Florida specific effects would likely decrease the error. It is absolutely necessary for the model to be based on a Florida specific database. Model Specifications and Data The SAGT-SPARROW model uses a regional variable to allow for differences in the landscape characteristic effects. The inclusion of these regional-landscape variables reduces the spatial correlation of the model which is a positive result. Essentially, each local-scale landscape characteristic effect is altered by the coefficient of the regional landscape variable. However, because the regional variables were not interacted with the local variables, the region must have the same effect on each local-landscape characteristic. For example, the coefficient estimate for region HLR2 is -0.29. Thus, each of the physical landscape variables effect estimates will be reduced by -0.29. That is, effect of soil permeability, depth to bedrock, and mean annual precipitation are all reduced by 0.29. If interactions had been included, the region would be able to have a different effect on each physical landscape variable. It does not seem reasonable to assume that the physical landscape characteristics are all impacted in the same way. This is particularly true in many areas of Florida where we have pointed out previously , and is explicitly acknowledged by USEPA (USEPA 2010a, pg 4180). have intimate interactions with groundwater, particularly in Karst areas, but also true in areas with highly permeable soils and shallow groundwater. 4/26/2010
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As mentioned above, the SAGT-SPARROW model data omits much of Florida in its calibration dataset. The North-Central area of Florida is impacted by the complex aquifer system. The impact of the aquifer is unknown on the nitrogen attenuation. Thus, estimates for streams in this area may be riddled with error. The SAGT-SPARROW model is being extrapolated beyond the inference space of the data which may result in significant errors. Including this data in the calibration data along with a factor for aquifer may potentially solve this problem. Binning of “Fraction Delivered” to determine DPVs The binning of the fractions delivered into the ranges described in #9 above results in overly restrictive estimates of the DPVs. For example, a FracDelTar for a stream estimated at 81% is rounded to 90% to calculate that DPV for that stream. Consider in addition that smaller streams not captured by the stream network used in the SAGT-SPARROW model (ERF1) are mapped to larger streams in the ERF1 network, assigning the smaller streams FracDelTar of the larger stream. As stated by the USEPA2, smaller streams often have a smaller fraction delivered due to the higher level of instream losses of TN. In the example of a stream with a FracDelTar of 81%, smaller streams assigned to this ERF1 reach could have even smaller FracDelTars; however, these streams would be assigned a FracDelTar of 90% in determining the DPV. Error of FracDelTar point estimates In addition to the conservative nature of the procedure described above, the SAGT-SPARROW model results in a wide range of error for the point estimates of FracDelTar for each ERF1 reach. For example, Pensacola Bay and reach MRB_ID = 8509 has a point estimate for FracDelTar of 0.71. In the USEPA procedure this would be rounded to .8 for the purposes of calculating the DPV. The 90% confidence interval for this stream’s FracDelTar is (0.58 to 0.88). The interpretation of this confidence interval is that we are 90% certain that the true value for FracDelTar falls between 0.58 and 0.88. Thus the true value for the fraction delivered for this stream could be as low as 0.58 or as high as 0.88. The wide range of this confidence interval is due to the significant error in the point estimates for the FracDelTar. If indeed the true value for the Fraction Delivered is 0.58, then the EPAs procedure is using a value that is over 20 percentage points higher than should be used to calculate the DPV, resulting in a speciously restrictive estimate for the DPV. Thus, USEPA has developed and proposed downstream protective values for the protection of estuaries which are not grounded in good science. While the technical limitations of the USEPA - SPARROW implementation are listed above, the USEPA 2
USEPA 2010a. Water Quality Standard for the State of Florida’s Lakes and Flowing Waters, EPA-HQOW-2009-0596, p. 110.
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exacerbates those problems by applying a convenient but crudely derived load reduction factor based on an unsupported assumption, that a protective value is represented by a 50% reduction in anthropogenic load of Total Nitrogen (derived from the questionable SPARROW load estimates). There is no justification of this load reduction for all estuaries and no dose-response relationship. USEPA attempts to justify the assumption by pointing out that their number falls within the range of loads established by current TMDL’s and PLRG’s for Florida estuaries, that have been established by a variety of site specific studies. Surely, the load in the TMDL studies could range from 0% to 100% reduction and thus 50% would certainly fall within that range. The assumption that this justifies the USEPA methodology is untenable.
Suggested Alternative approaches for Estuaries With Florida’s lakes and streams, it will be necessary to appropriately subdivide the systems depending on their respective physiographic provinces and geological based chemistry, since the various systems respond in vastly different manners. The same is true for Florida’s estuarine waters, and USEPA decision to provide downstream protective values is premature, and does not reflect the differences that are seen throughout Florida’s estuarine systems. More importantly, as pointed out by FDEP, much progress has been made in managing Florida’s estuarine systems, and given the number of estuarine systems is far less when compared to lakes, does it make sense to vary from a site specific approach for estuaries and develop a poorly fitting (analytically) approach to apply to all Florida’s estuarine waters? The numbers of estuaries are far less, individually more highly variable and dynamic when compared to stream segments or lakes (more than 7,700) and, we would assert that it makes no sense to develop a poorly fitted empirical model when site specific approaches through the TMDL program are working already (as demonstrated in the Tampa Bay, Sarasota Bay, etc). Further, the effort to produce the hydrodynamic models has already been accomplished and models are available for many estuaries (see Weisburg, 2010) that have been linked to the operational HYCOM system developed by the US Navy. Would it not make more sense and be more scientifically valid, in estuarine areas where significant work has not been done, to start with existing efforts and models? However, since the USEPA will be working on numeric nutrient criteria for estuaries in the coming months, SER expressly provides its opinion regarding issues raised by the direction the USEPA has chosen to take. The key features of estuarine systems that must be considered by both the State and USEPA when attempting to predict response to nutrients are presented below. a) Physiographic diversity of Florida estuaries Estuaries are highly diverse physiographic units and the application of a uniform criterion derived from a single model for all systems in Florida is not justifiable. Existing validated programs should be utilized over concocted surrogate 4/26/2010
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approaches. Just as the geology and watershed characteristics have shaped many of Florida’s lakes and streams, the same forcing functions have shaped the ultimate development of Florida’s estuaries. Many estuarine studies are designed to first classify the systems and then assess the biological community in the framework of its underlying geology and hydrology (Boyer et al., 2009). b) Hydrologic Flushing A key factor in estuaries that determines the system’s response to nutrients is hydrologic flushing from riverine and tidal (and offshore) inputs. As with lakes (see Vollenweider discussion for lakes above) the response of the system is in large part determined by its hydrologic residence time. This has been pointed out for many estuarine systems (Monbet, 1992; Lowery 1996, 1998; Bricker et al., 2007) and particularly for Florida systems (Steward and Lowe, 2010; Bledsoe and Phlips, 2000; Bledsoe, et al., 2004; Weisberg, 2010). Any method to derive nutrient criteria should account for hydrologic flushing. c) Salinity-Stratification Many researchers have noted that the estuarine eutrophication response, and especially hypoxic conditions, occurs within the oligo-mesohaline portion of the estuary (Lowery 1996,1998). The response is also exacerbated by salinity stratification allowing for the settling of organic load resulting from production (Smith, 1993, 2003, 2006). d) Grazing and Secondary Productivity The high secondary productivity in some estuaries, (i.e. oysters and other shellfish), is not reflected in its planktonic primary production (i.e. high chlorophyll a), typically because of high turbidity from clays and detritus. However, these economic and socially desirable systems are driven by its naturally high nutrient load. The assumption that this high nutrient load is necessarily bad is a ridiculous regulatory construct (Livingston – Apalachicola Bay). More recently, as USEPA has recognized, the Vollenweider concept has been successfully applied to both lacustrine and estuarine systems in Florida (Steward and Lowe, 2010). We believe that the relatively good fit using the Vollenweider approach was in part fortuitous because it represented a rather small portion of Florida’s estuarine and lacustrine systems. However, the model does illustrate the importance of using estuarine flushing, which is residence time, in empirical equations, to derive a better empirical fit between load and response in linear regressions. However, many of the same limitations of the Vollenweider approach we pointed out for Florida lakes are also valid for estuaries. USEPA was aware of these empirical alternative models but chose not to invest in further analysis. They acknowledge that the Steward and Lowe (2010) model was available but ignore the approach based on the fact that their loads from SPARROW were arguably within the range of loads for all estuaries predicted by Steward and Lowe. This superficial analysis is insufficient given the consequences of the 4/26/2010
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promulgation of this rule. Further, the fact that the ranges are similar does not give justification for the EPA methods as they imply, with no comparison of how the model predictions compare to known conditions in individual estuaries. If the upper and lower ranges are predicted poorly, in terms of estuarine response, then a simple comparison of ranges does not justify the validity of the method. In addition, more robust empirical models incorporating flushing and other factors were also available to USEPA which they chose not to use. These include Lowery (1996, 1998) which integrates both watershed load (derived from NOAA’s efforts – Bricker et al., 2007) in relationship with flushing, N:P ratio and salinity to determine a response variable (hypoxic condition) which does a reasonable job (89.9% concordance with observed response, hypoxic condition) of predicting the response of major Florida Gulf Coast estuaries. Lowery also did sensitivity analysis on the model and the model also showed how the various estuaries would respond to increases and decreases to loading, and which estuaries appeared to be sensitive to changes in loadings. The SAB has suggested that USEPA use a more probabilistic approach for deriving criteria for nutrients. The Lowery estuarine model presents a useful probabilistic approach, based on multivariate logistic regression, one of the methods preferred in EPA’s own guidance. We suggest this approach be investigated to classify all Florida’s estuarine systems, as characterized by FDEP in their ongoing studies and supplemented with data from Bricker et al. (2007) and then use those classifications for those systems which are not currently (or planned ) to receive site-specific treatment under Florida’s rotating basin planning. The would perhaps provide a more scientifically based approach for filling in the knowledge gaps for systems currently not under direct study for TMDL’s or site specific studies. To respond to that suggestion, and if a single nutrient load-response model must be developed, we would recommend using available data from FDEP and NOAA’s assessments (Bricker et al., 2007), to derive a probabilistic model based on Lowery’s approach. This would likely have a high likelihood of success and result in a suitable empirical model of Florida’s estuaries to be applied in systems without active TMDL’s or ongoing management programs. It would include nutrient load estimates that relate to a response variable, the oxygen condition of the estuary (normoxic, hypoxic etc) , taking into account the modifying factors of flushing (tidal and riverine), salinity, spatial area, and N:P ratio into predict a probability of impairment from which management decisions could be reasonably made.
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Comments on Spring Criteria- Proposed 40 C.F.R. § 131.43(c)(3) The USEPA essentially adopted the proposed FDEP proposal regarding nitrate-nitrate limits for springs. Differences of opinion regarding the calculation of the data used to compare to the criteria (median vs. geometric mean) were noted in FDEP’s comments on the numeric criteria for springs (FDEP, 2010e). While we acknowledge the application of various scientific studies in support of these criteria, we believe the proposed criteria will only partly solve the eutrophication problems observed in springs. The nitrate criteria may apply to springs with algal impacts; we do not believe nitrate levels control macrophyte problems in these systems. Additionally, we question whether the application of a numeric nitrite-nitrate criterion for springs and spring runs will foster appropriate management actions. In other words, the nitrite-nitrate issue is a legacy watershed issue, and for the most part, cannot be managed through the current regulatory constructs. We also question the validity of applying the nitrate criteria in the remaining Florida streams since it is not supported by a similar dataset as produced for springs. It is unclear that there is a suitable database that has demonstrated a direct cause and effect relationship in the clear stream systems, as should be the requirement for any numeric nutrient criteria promulgated. Comments on Canal Criteria - Proposed 40 C.F.R. § 131.43(c)(4)3 Overall, the provisions of the proposed rule regarding Florida canals were not developed using sound scientific principles, and we thus conclude that they are arbitrary. The document provides no clear statement regarding what ecosystem qualities and designated uses the USEPA was trying to protect by developing nutrient criteria for Florida canals. Although the document acknowledges that a very large diversity exists in types of canals in Florida and their range of existing uses, the analysis performed by the USEPA lumped together data from a variety of sources on a variety of canal types, making it highly unlikely that the resulting criteria could actually be protective of any designated or intended uses. The EPA eliminated extensive amounts of data, and sites, and sources of data in developing its final data base, but that resulted in a data base with unknown and undescribed characteristics relative to the population of canals (or perhaps populations of canals types) in south Florida. It is not possible for reviewers to determine whether the final data base is representative of all or some canal classes (regional or otherwise) in South Florida. The EPA document also is unclear regarding what the proposed nutrient criteria actually are or whether a specific set of numeric criteria are being proposed. The bases for these criticisms is presented in the following paragraphs. 3
The comments here were adapted with permission from comments prepared by P.L. Brezonik, 2010.
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The document begins by describing the diversity of canal types in Florida and the wide range of intended and designated uses—from water conveyance for irrigation or flood control to aesthetic enjoyment and navigation (finger canals in coastal residential areas). South Florida canals are not commonly used for contact recreation, but fishing is common. It is highly unlikely that one set of nutrient criteria would be applicable to all the types of canals and uses, but the EPA did not address this issue. In addition, beyond mentioning the above types and uses, the EPA did not develop information on appropriate response indicators to protect these designated or intended uses or describe appropriate limiting (threshold) values for such indicators. The EPA used chlorophyll a as a response indicator in section 4c (“Alternative Approach Considered by U. S. EPA”) but provided no data to support the use of any given chlorophyll concentration as a threshold value to protect canal ecosystem health or intended/designated uses, and it provided no documentation to support the use of chlorophyll a as an appropriate response indicator. Two issues are of specific concern regarding the deletion of data and sites by the EPA. First, there is no obvious scientific reason to delete sites from the data base with fewer than four measurements per year. This would be appropriate if one were developing specific criteria for such (data-limited) individual sites, but the EPA presented no evidence that the data from such sites are invalid for use in a pooled data set. Second, the limits used to exclude TN (100 mg/L) and TP (15 mg/L) data are much too high. Raw sewage has a TP of ~7-8 mg/L or less and TN < ~35 mg/L. It is most unlikely that canal samples with TN or TP concentrations higher than raw sewage could represent accurate data. It is not possible to determine from information provided in the report whether this resulted in a significant number of suspect data being retained in the data base. The EPA grouped the canal sites into four geographic regions, but the basis for using this approach was not explained adequately. Ultimately, it appears that the EPA did not use the four regions to develop separate criteria. It would be much more appropriate from a scientific perspective to group the canals based on physical characteristics like use (e.g., conveyance canals versus residential “finger” canals), major soil category for the landscape in which the canal occurs, or a hydrologic characteristic like average water residence time. The EPA document is not specific regarding which of the sets of nutrient criteria described in the report is the one that the EPA intends actually to use. The first set (p. 4-4) is given without comment simply as the 75th percentile values for TP, TN, and chlorophyll a in canal sites that were not listed as impaired. No direct statement is provided that these values are intended to be the proposed nutrient criteria for canals. One must refer back to the top of page 4-2, where it is stated that because of the lack of reference or benchmark sites, the EPA proposes to use this criterion (the 75th percentile 4/26/2010
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data) to establish nutrient criteria for canals. The impression is given that the authors of the report were lacking confidence in their results. The EPA also described a series of alternative approaches it considered and presented results from those analyses. Section 4.a describes an approach using canals near Highway 41 in South Florida (between the Everglades Protection Area and Big Cypress Swamp) as “reference canals.” Based on a statistical analysis of data from these canals, criteria are presented, without comment or discussion, for TP, TN, and chlorophyll a. No comparison was made with the values developed by the first method, and no reasons were given for not using these values instead of those derived by the first method. The report also describes the development of nutrient criteria based on a “stressor-response” approach (p. 4-6, Section 4c.i), in which TP and TN data were regressed against chlorophyll a data. TP and TN criteria were developed from the regression relationships using a target chlorophyll value of 20 μg/L.The line of best fit between ln TP and ln chl a (or between ln TN and ln chl a) was not used to establish the criteria, and instead EPA used a line representing the 90% confidence interval for the relationship. Why this value was selected was not explained or justified,, and the report does not explain how (or whether) intended/designated uses of canals or their ecological health would be impaired at higher chlorophyll concentrations. No discussion is provided regarding the quality of the relationships or accuracy/reliability of the calculated results as nutrient criteria; no comparison was made with the criteria values developed by the earlier methods; and no reasons were given for why these values were not used to establish the nutrient criteria instead of those derived by the earlier methods. Similarly, two other approaches were described in later sections of the document, and in each section there is a lack of comparison of results from the different methods and a lack of any statements about the appropriateness of the criteria developed by the method. In the end, the reader is left with the feeling that none of the methods was considered to be a valid way to develop nutrient criteria for Florida canals. Aside from these concerns, several specific technical issues and shortcomings were found in the report, as described below. Use of the 75th percentile is arbitrary and does not represent the use of sound science. This arbitrary approach could lead to under-protection or over-protection, and it is not possible to evaluate which is more likely. From comments made by USEPA in the second paragraph on page 2, it appears that EPA is acknowledging that the proposed criteria based on 75th percentile values cannot be defended as representing threshold values for impairment of aquatic life or intended uses in canals. 4/26/2010
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The maps in Figure 4-1 provide no evidence that the soils of the EAA and EvPA are inherently different. In addition, is it not clear from the maps of soil orders that the soils on the east and west coasts are inherently different from each other. Rather than using a geographic classification, the USEPA should have grouped canals in similar soil types on the east and west coasts and in the two Everglades regions together. The period of record used for the data base is too long ( > 35 years). For purposes of data comparability, the EPA should have used just the two-thirds of the data from 1999-2009. Analytical methods and reliability of data have changed substantially over the period of record. Environmental conditions also have changed. Inclusion of data from such a long period leads to greater heterogeneity in the data base, making it more difficult to analyze and more difficult to defend. The nutrient and chlorophyll numbers presented on p. 4 do not make sense in that the chlorophyll a concentration is very low relative to the TN and TP concentrations. In fact, the chlorophyll a concentration (4.0 μg/L) is lower than the value associated with unimpaired water quality conditions in natural surface waters. The chlorophyll limit that the EPA used for alkaline lakes is 20 μ/L, a factor almost 5 higher. As a result, the associated nutrient concentrations of 1.6 mg/L (TN) and 0.042 mg/L (TP) would not appear to be appropriate as nutrient criteria for canals. Use of the 25th percentile as the criterion (p. 4, bottom line) has no scientific basis and is arbitrary. Why EPA would propose such an approach, particularly in South Florida, where nearly all canals are affected by human activity, is difficult to understand. Page 5. The use of TN values is as inappropriate for canals as it is in the development of nutrient criteria for lakes and streams: in Florida a large but variable fraction of the TN is tied up in dissolved humic matter and is biologically unavailable. Allowing the criteria to be exceeded one-out-of-three-years is not protective of ecosystem health. Although the criteria themselves are appropriate to protect ecosystem health,, we express this concern because it applies if EPA were to develop appropriate criteria and apply the same one-in-three-year allowance. A value of 20 μg/L for chlorophyll was used to compute apparent TP criteria from the regression relationships, but no foundation is laid that this value is an appropriate limit for chlorophyll in canals. Consequently, , it is not possible to interpret the TP values calculated by EPA from the regression relationships as valid nutrient criteria. The chlorophyll-TP regressions show considerable scatter, and less than half of the variance in chlorophyll is explained by the regressions. In addition, only one or two of the data points shown in Figure 4-6b equal or exceed 20 μg/L for chlorophyll; this adds additional uncertainty to the extrapolation of an associated TP concentration. It is 4/26/2010
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not clear why the EPA chose a 90% confidence level as the basis for calculating the TP concentration rather than the value associated with the regression line itself (the line of best fit). A value of 75% confidence was used by EPA to develop lake nutrient criteria. The regression analyses for chlorophyll versus TN show essentially no useful relationships. The low r2 values indicate that only a very minor portion of the variance in chlorophyll is explained by TN. No useful (or defensible) criteria can be developed for TN from these regressions. In summary, the EPA has presented no scientific basis to establish nutrient criteria for Florida canals. If numeric nutrient criteria are to be developed for canals, the EPA will need first to establish goals for the conditions it seeks to protect by establishing nutrient criteria for canals. To do this properly, it must develop a rational basis for classifying canals according to their uses and these goals. The EPA needs to establish appropriate response variables indicate that those conditions are being protected, and it needs to determine appropriate threshold values of the indicators related to impairment of the intended/designated uses or aquatic life conditions that are to be protected. Finally, the EPA needs to establish reliable and defensible relationships between the threshold indicator values and nutrient concentrations. Unfortunately, the EPA did none of these steps in the Chapter 4 document. Consequently, the proposed nutrient criteria should not be implemented.
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Conclusions On January 26, 2010, the United State Environmental Protection Agency (USEPA) published proposed numeric nutrient criteria for flowing waters of the State of Florida including lakes, streams and springs. We believe this proposed rule is neither necessary, nor based on sound scientific relationships between nutrients and observed water quality impacts. We conclude that the proposed rule is scientifically flawed to the extent that it is not workable and should be abandoned. Our basis for this conclusion is as follows: The State of Florida has sound scientifically based programs in place to address nutrient impact issues within the state. The USEPA’s presumption that the State has failed to provide sufficient protection for flowing streams within the State of Florida is invalid. Further, the assertion by USEPA that the State is progressing too slowly in developing nutrient criteria and the management of nutrients in its surface waters is also erroneous. The methods used by the USEPA do not adequately classify the systems within the State for a sufficient scientific analysis. While the State of Florida has made efforts to improve water body classifications, there are still excessive consolidation of systems even within their approach. The classification schemes used by both the State of Florida and USEPA do not completely account for the broad spectrum of Florida’s unique natural systems such as springs and sand-hill lakes in Florida, nor account for existing uses in manmade lakes and canals. The USEPA has used methods that do not demonstrate a cause and effect relationship between nutrients and impairment, a key criticism of USEPA’s own SAB committee charged with review of the Agency’s methods. The USEPA continues to utilize methods that are not based in sound scientific principles and have not produced statistically defensible cause and effect relationship between stressor and response. The USEPA continues to ignore the fact that the eutrophication response in many systems is not simply dependent on nutrient concentrations. In fact, nutrients are rarely the stressors, but rather the eutrophication process is initiated by nutrients, which may result in stressed conditions, typically because of low dissolved oxygen. Numerous multivariate factors contribute to the ultimate expression of a nutrient impact, and simple nutrient criteria, based on empirical univariate relationships for large groups of waterbodies, particularly streams, is simply not possible. The USEPA continues to ignore existing mechanistic methods that can be used to develop sound site specific nutrient criteria which can be used to effectively control eutrophication and develop site specific criteria that are based in tested and proven scientific principles. The methods used by the EPA to derive its proposed criteria were not based on scientifically justifiable assumptions, do not result in scientifically defensible endpoints, 4/26/2010
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and, therefore, represent a flawed, non-scientific approach. The USEPA should work with the FDEP in improving its current TMDL and BMAP programs, which provide sufficient mechanisms to regulate nutrients within the State of Florida.
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REFERENCES Alabama Department of Environmental Management. 2008. Alabama’s 2008 303(d) list fact sheet. Prepared by Alabama Department of Environmental Management, Montgomery, AL, 184 p. Alexander, R. B., J. K. Bohlke, E. W. Boyer, M. B. David, J. W. Harvey, P. J. Mulholland, S. P. Seitzinger, C. R. Tobias, C. Tonitto and W. M. Wollheim. 2009. Dynamic modeling of nitrogen losses in river networks unravels the coupled effects of hydrological and biogeochemical processes. Biogeochemistry 93:91-116. Baker, L. A., P. L. Brezonik and C. R. Kratzer. 1981. Nutrient loading – Trophic state relationships in Florida lakes. Publ. No. 56, Water Resources Research Center, University of Florida, Gainesville, FL., 126 p. Bay Soundings Website. 2010. Red Tide “As predictable as Weather”, published Fall 2006. Accessed 4/23/2010. Available online: http://baysoundings.com/fall06/redtide.asp. Blancher, E. C., II. 1979. Lake Conway, Florida: Nutrient Dynamics, Trophic State, Zooplankton Relationships. Dissertation. The University of Florida, Gainesville, FL., 146 p. Blancher, E. C., II. 1980. Impacts of stormwater runoff on a Florida lake ecosystem: Effects on water quality and biota. In, Y. A. Yousef, M. P. Wanielista, W. M. McLellon, J. S. Taylor (eds.), Urban Stormwater and Combined Sewer Overflow Impact on Receiving Water Bodies. Proceedings of National Conference, Nov. 26-28, 1980, Orlando, FL., EPA-600/9-80-056, p. 90 – 114. Blancher, E. C., II, S. A. Sklenar, R. A. Park and J. L. Wood. 2002. Determining the linkages for a nutrient TMDL in a stream listed as use-impaired for endangered species. National TMDL Science and Policy Conference, Proceedings of the Water Environment Federation, November 13-16, 2002, Phoenix, AZ, 900-918 p. Bledsoe, E. L. and E. J. Phlips. 2000. Relationships between phytoplankton standing crop and physical, chemical, and biological gradients in the Suwannee River and plume region, U.S.A. Estuaries 23(4):458-473. Bledsoe, E. L., E. J. Phlips, C. E. Jett and K. A. Donnelly. 2004. The Relationships among phytoplankton biomass, nutrient loading and hydrodynamics in an inner-shelf estuary. Ophelia 58:29-47. Boyer, J. N., C. R. Kelble, P. B. Ortner and D. T. Rudnick. 2009. Phytoplankton bloom status: Chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA. Ecological Indicators 95:556-567. 4/26/2010
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Brezonik, P. L., E. C. Blancher, II, V. B. Myers, C. L. Hilty, M. K. Leslie, C. R. Kratzer, G. D. Marbury, B. R. Snyder, T. L. Crissman and J. J. Messer. 1979. Factors affecting primary production in Lake Okeechobee, Florida. Report No. 07-79-01. Prepared by Department of Environmental Engineering Sciences, University of Florida, Gainesville, FL. Prepared for Florida Sugar Cane League, Clewiston, FL., 296 p. Brezonik, P.L. and C.D. Pollman. 1999. Phosphorus chemistry and cycling in Florida lakes: global issues and local perspectives, pp. 69-109 in: Phosphorus Biogeochemistry in Subtropical Ecosystems, K. Reddy et al. (Eds.), Lewis Publ., Boca Raton, FL. Bricker, S., B. Longstaff, W. Dennison, A. Jones, K. Boicourt, C. Wicks, and J. Woerner. 2007. Effects of Nutrient Enrichment in the Nation’s Estuaries: A Decade of Change. NOAA Coastal Ocean Program Decision Analysis Series No. 26. National Centers for Coastal Ocean Science, Silver Spring, MD., 328 p. Carleton, J. N., R. A. Park and J. S. Clough. 2009. Ecosystem modeling applied to nutrient criteria development in rivers. Environmental Management 44:485-492. Cox, R., E. C. Blancher and M. Misra. 2007. Determination of eutrophic conditions in the Cahaba River, Alabama by using a dynamic partial least squares model. Abstract. 2007 AIChE Annual Meeting, November 4-9, 2007, Salt Lake City, UT. Davenport, L. J., W. M. Howell, K. Morse, K. Yancie, and J. L. Wood. 2005. Fishes and macroinvertebrates of the Cahaba River: A three-year study. J. Alabama Acad. Sci. 78. Available online: http://www.accessmylibrary.com/article-1G1-135759742/fishes-and-macroinvertebratesupper.html. Detenbeck, N. E. and P. L. Brezonik. 1991a. Phosphorus sorption by lake sediments. 1. Comparison of equilibrium models. Environ. Sci. Technol. 25: 395-403. Detenbeck, N. E. and P. L. Brezonik. 1991b. Phosphorus sorption by lake sediments. 2. Effects of pH and other solution variables. Environ. Sci. Technol. 25: 403-409. Eisenreich, S. J. and D. E. Armstrong. 1977. Chromatographic investigation of inositol phosphate esters in lake waters. Environmental Science and Technology 11:497-501. El-Shall, H. and M. Bogan. 1994. Characterization of Future Florida Phosphate Resources. FIPR Publication #02-082-105. Prepared for Florida Institute of Phosphate Research, Bartow, FL. Accessed April 7, 2010. Available online: http://www1.fipr.state.fl.us/fipr/fipr1.nsf/129fc2ac92d337ca85256c5b00481502/5138f39b 78f2c39985256b2e00780b7d!OpenDocument.
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Fernald, E. A. and E. D. Purdum. 1998. Water Resources Atlas of Florida. Tallahassee: Institute of Science and Public Affairs, Florida State University. Fleming, L. E. 2001. Frequently asked questions about blue green algae (cyanobacteria) and their toxins. Report to the Florida Harmful Algae Bloom Taskforce, December, 2001, 6 p. Available online: http://www.rsmas.miami.edu/groups/ohh/science/pdf/Q_A_draft.pdf. Florida Department of Environmental Protection. 2009. Florida Department of Environmental Protection Review of SPARROW: How useful is it for the purposes of supporting water quality standards development? Submitted to Water Docket, U. S. Environmental Protection Agency, Docket ID No. EPA-HQ-OW-2009-0596-1065, Washington, DC, 10 p. Florida Department of Environmental Protection. 2010a. About the Apalachicola National Estuarine Research Reserve and Associated Sites. Available online: http://www.dep.state.fl.us/coastal/sites/apalachicola/info.htm. Florida Department of Environmental Protection. 2010b. Evaluation of Florida benchmark site exceedance frequency relative to EPA’s proposed in-stream protection values (IPVs) and Downstream Protection Values (DPVs). February 1, 2010. Prepared by the Bureau of Assessment and Restoration Support, Florida Department of Environmental Protection. Florida Department of Environmental Protection. 2010c. Review of Proposed Criteria for the Protection of Downstream Lakes - Draft. Proposed January 26, 2010 by the Environmental Protection Agency. March 17, 2010. Prepared by Division of Environmental Assessment and Restoration, Florida Department of Environmental Protection. U. S. Environmental Protection Agency, Docket ID No. EPA-HQ-OW-20090596, Washington, DC. Florida Department of Environmental Protection. 2010d. Review of Proposed Criteria for the Protection of Lakes - Draft. Proposed January 26, 2010 by the Environmental Protection Agency. March 16, 2010. Prepared by Division of Environmental Assessment and Restoration, Florida Department of Environmental Protection. U. S. Environmental Protection Agency, Docket ID No. EPA-HQ-OW-2009-0596, Washington, DC. Florida Department of Environmental Protection. 2010e. Review of Proposed Criteria for the Protection of Streams and Springs / Clear Streams - Draft. Proposed January 26, 2010 by the Environmental Protection Agency. March 12, 2010. Prepared by Division of Environmental Assessment and Restoration, Florida Department of Environmental Protection. U. S. Environmental Protection Agency, Docket ID No. EPA-HQ-OW-20090596, Washington, DC.
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Florida Department of Environmental Protection. 2010f. St. Joseph Bay State Buffer Preserve Information Page. Available online: http://www.dep.state.fl.us/coastal/sites/apalachicola/stjoseph_buffer.htm. Florida Department of Environmental Protection. 2010g. Groundwater Program. Accessed: April 26, 2010. Available online: http://www.dep.state.fl.us/water/groundwater.htm Florida Fish and Wildlife Conservation Commission. 2010a. Fish and Wildlife Research Institute Website. HAB General Information. Accessed March 23, 2010. Available online: http://research.myfwc.com/features/category_sub.asp?id=1799. Florida Fish and Wildlife Conservation Commission. 2010b. Fish and Wildlife Research Institute Website. HAB General Information. Accessed April 23, 2010. Available online: http://research.myfwc.com/features/view_article.asp?id=25327 Florida Fish and Wildlife Conservation Commission. 2010c. Fish and Wildlife Research Institute Website. Red Tide FAQ General Information. Accessed April 23, 2010. Available online: http://research.myfwc.com/support/view_faqs.asp?id=13 Florida Institute of Phosphate Research. 2010. Phosphate Primer: Accessed April 7, 2010. Available online: http://www1.fipr.state.fl.us/PhosphatePrimer. Florida State University. 2010. Florida Climate Center. Accessed April 7, 2010. Available online: http://www.coaps.fsu.edu/climate_center/index.shtml. Frydenborg, R. and D. Bartlett. 2009. Stream Nutrient Criteria. FDEP Division of Resource Assessment and Restoration. Presentation at Public Workshop, Tallahassee, FL, June 17, 2009. Available online: http://www.dep.state.fl.us/WATER/wqssp/nutrients/meeting-arch.htm. Frydenborg, R. and K. Weaver. 2009. Development of Numeric Nutrient Criteria to Protect Florida’s Flowing Waters. FDEP Division of Environmental Assessment and Restoration. Presentation at Technical Advisory Committee Meeting, Tallahassee, FL, May. 5, 2009. Available online: http://www.dep.state.fl.us/WATER/wqssp/nutrients/meeting-arch.htm. Heil, C. A. 2005. Florida Red Tide: (History, Health, Nutrients & Status). Presentation to TBEP Management Board, August, 2005. Heil, C. A. and K. A. Steidinger. 2009. Monitoring, management, and mitigation of Karenia blooms in the eastern Gulf of Mexico. Harmful Algae 8:611-617. Herald Tribune. Timeline of Red Tide Blooms. Published July 16th, 2006. Available on HT website: http://www.heraldtribune.com/apps/pbcs.dll/article?AID=2006607160583
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Howell, W. M. and L. J. Davenport. 2000. Report on fishes and macroinvertebrates at four sites on the Cahaba River, Jefferson County, Alabama. Prepared by Department of Biology, Samford University, Birmingham, AL. Prepared for Jefferson County, Barton Laboratory, Homewood, AL. Howell, W. M. and L. J. Davenport. 2001. Report on fishes and macroinvertebrates of the upper Cahaba River and three additional sites. Prepared by Department of Biology, Samford University, Birmingham, AL. Prepared for Jefferson County, Barton Laboratory, Homewood, AL. Howell, W. M. and L. J. Davenport. 2002. Report on fishes and macroinvertebrates of the upper Cahaba River and four additional sites. Prepared by Department of Biology, Samford University, Birmingham, AL. Prepared for Jefferson County, Barton Laboratory, Homewood, AL. Hoyer, M. V., T. K. Frazer, S. K. Notestein and D. E. Canfield, Jr. 2002. Nutrient, chlorophyll, and water clarity relationships in Florida’s nearshore coastal waters with comparisons to freshwater lakes. Canadian Journal of Fisheries and Aquatic Sciences 59:1024-1031. Hu, C., F. E. Muller-Karger, G. A. Vargo, M. B. Neely and E. Johns. 2004. Linkages between coastal runoff and the Florida Keys ecosystem: A study of a dark plume event. Geophys. Res. Lett., 31, L15307. Huber, W. C., P. L. Brezonik, J. P. Heaney, R. E. Dickinson, S. D. Preston, D. S. Dwornik and M. A. DeMaio. 1982. A classification of Florida lakes. Report ENV-05-82-1, Water Resources Research Center. Prepared by Water Resources Research Center, Department of Environmental Engineering Sciences, University of Florida, Gainesville, FL. Prepared for Florida Department of Environmental Regulation, Tallahassee, FL.
Hydroqual, Inc. 2010. Comments on EPA’s Water Quality Standardsfor the State of Florida’s Lakes and Flowing Waters (EPA-HQ-OW-2009-0596) 19 April 2010. 28 p. Lower St. Johns River Executive Committee. 2010. 2009 progress report Lower St. Johns River basin management action plan. Final report presentation, Jan. 13, 2010. Available online: http://www.dep.state.fl.us/northeast/stjohns/TMDL/docs/Jan2010/Annual_report_presen tation_011310_Final.pdf. Lowery, T. A. 1996. Contributions to Estuarine Eutrophic Modeling: Watershed Population Estimation Methodology, Estuarine Flushing Model, and Eutrophication Model. Dissertation. University of Maryland at College Park, College Park, MD., 164 p.
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Lowery, T. A. 1998. Modelling estuarine eutrophication in the context of hypoxia, nitrogen loadings, stratification and nutrient ratios. Journal of Environmental Management 52:289-305. McPherson, B. F., C. Y. Hendrix, H. Klein and H. M. Tyus. 1976. The Environment of South Florida, A Summary Report. U. S. Geological Survey Professional Paper 1011., 102 p. Available online: http://sofia.usgs.gov/publications/papers/pp1011/index.html. Milroy, S. P., D. A. Dieterle, R. He, G. J. Kirkpatrick, K. M. Lester, K. A. Steidinger, G. A. Vargo, J. J. Walsh and R. H. Weisberg. 2008. A three-dimensional biophysical model of Karenia brevis dynamics on the west Florida shelf: A look at physical transport and potential zooplankton grazing controls. Continental Shelf Research 28:112-136. Monbet, Y. 1992. Control of phytoplankton biomass in estuaries: A comparative analysis of microtidal and macrotidal estuaries. Estuaries 15(4):563-571. National Academy of Sciences. 2001. Assessing the TMDL Approach to Water Quality Management. National Academy Press, Washington, DC. National Council on Air & Stream Improvement (NCASI). 2010. Anaylsis and Comments on Environmental Protection Agency Water Quality Standards for the State of Florida’s Flowing Waters; Proposed Rule. Docket ID No. EPA-HQ-OW-2009-0596, Washington, DC. 102p. Ogburn, R. W., III, and P. L. Brezonik. 1986. Examination of the oligotrophication hypothesis: Phosphorus cycling in an acidic Florida lake. Water, Air, and Soil Pollution 30:1001-1006. Pratt, J. R., J. Cairns, Jr., P. M. Stewart, N. B. Pratt, B. R. Niederlehner. 1985. Measurement of recovery in lakes following phosphate mining . FIPR Publication #03045-039. Prepared for Florida Institute of Phosphate Research, Bartow, FL. Accessed February 5, 2010. Available online: http://www1.fipr.state.fl.us. Reckhow, K. H. 2002. Assessing the TMDL approach to water quality management. Presented at The Environmental Forum on TMDLs in Texas. University of Texas, Austin. Reckhow, K. H., M. E. Borsuk, and . A, Stow. 2002. Adaptive implementation of TMDLs using Bayesian analysis. National TMDL Science and Policy Conference, Proceedings of the Water Environment Federation, November 13-16, 2002, Phoenix, AZ, 12 p. Salas, H. J. and P. Martino. 1991. A simplified phosphorus trophic state index for warm water tropical lakes. Water Research 25:341-350.
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Shannon, E. E. and P. L. Brezonik. 1972a. Eutrophication analysis: A multivariate approach. Journal of the Sanitary Engineering Division, American Society of Civil Engineers 98:37-57. Shannon, E. E. and P. L. Brezonik. 1972b. Relationships between lake trophic state and nitrogen and phosphorus loading rates. Environmental Science and Technology 6:719725. Smith, N. P. 1990.3 Tidal and nontidal flushing of Florida’s Indian River Lagoon. Estuaries 16:739-746. see pg 33 Smith, V. H. 2003. Eutrophication of freshwater and coastal marine ecosystems. Environmental Science and Pollution Research 10(2):126-139. see pg 33 Smith, V. H. 2006. Responses of estuarine and coastal marine phytoplankton to nitrogen and phosphorus enrichment. Limnology and Oceanography 51(1, part 2):377384. see pg 33 State-EPA Nutrient Innovations Task Group. 2009. An urgent call to action: A report of the State-EPA Nutrient Innovations Task Group. Prepared for U. S. Environmental Protection Agency, Washington, DC, 170 p. Steidinger, K. A. 2009. Historical perspective on Karenia brevis red tide research in the Gulf of Mexico. Harmful Algae 8:549-561. Steward, J. S. and E. F. Lowe. 2010. General empirical models for estimating nutrient load limits for Florida’s estuaries and inland waters. Limnology and Oceanography 55(1):433-445. TAI Environmental Sciences. 2001. Periphyton standing crop in the Cahaba River, Alabama during low-flow conditions, 2001. Cahaba River – MOA data: July – September 2001. Prepared by TAI Environmental Sciences, a Division of Strand Associates, Inc., Mobile, AL. Prepared for Jefferson County, Birmingham, AL. TAI Environmental Sciences. 2002. Periphyton standing crop in the Cahaba River, Alabama during low-flow conditions, 2001. Cahaba River – MOA data: July – September 2002. Prepared by TAI Environmental Sciences, a Division of Strand Associates, Inc., Mobile, AL. Prepared for Jefferson County, Birmingham, AL. Tampa Bay Nitrogen Management Consortium. 2010. Tampa Bay nitrogen management consortium comments and request regarding the development of protective loads for the Tampa Bay Estuary as it relates to establishing numeric nutrient criteria for lakes, flowing waters and estuaries in Florida. March 8, 2010. Submitted to Water Docket, U. S. Environmental Protection Agency, Docket ID No. EPA-HQ-OW2009-0596-0606.1, Washington, DC, 24 p.
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The Southwest Florida Water Management District. 2001. Manatee River: Comprehensive Watershed Management Plan. Prepared in cooperation with Manatee, City of Bradenton and City of Palmetto, FL., 175 p. U. S. District Court Northern District of Florida, Tallahassee Division. 2009. Case: 4:08cv-00324-RH-WCS. Document 90-2. Filed 08/25/2009. (consent decree). USEPA. 2010a. Water quality standards for the State of Florida’s lakes and flowing waters. Proposed Rule. 40 CFR Part 131 [EPA-HQ-OW-2009-0596; FRL-9105-1], RIN 2040-AF11. Federal Register Vol. 75, No. 16, January 26, 2010, 54 p. U.S. EPA. 2010b. Technical support document for U.S. EPA’s proposed rule for numeric nutrient criteria for Florida’s inland surface fresh waters. Water Docket, U. S. Environmental Protection Agency, Docket ID No. EPA-HQ-OW-2009-0596-0003 to EPA-HQ-OW-2009-0596-0003.35. Washington, DC. Vollenweider, R. A. 1976. Advances in defining critical loading levels for phosphorus in lake eutrophication. Mem. Ist. Ital. Idrobiol. 33:53-65. Walsh, J. J., J. K. Jollif, B. P. Darrow, J. M. Lenes, S. P. Milroy, A. Remsen, D. A. Dieterle, K. L. Carder, F. R. Chen, G. A. Vargo, R. H. Weisberg, K. A. Fanning, F. E. Muller-Karger, E. Shinn, K. A. Steidinger, C. A. Heil, C. R. Tomas, J. S. Prospero, T. N. Lee, G. J. Kirkpatrick, T. E. Whitledge, D. A. Stockwell, T. A. Villareal, A. E. Jochens and P. S. Bontempi. 2006. Red tides in the Gulf of Mexico: Where, when, and why? Journal of Geophysical Research, Vol. lll, C11003, doi:10.1029/2004JC002813, 46 p. Wang, X., B. Qin, G. Gao and H. W. Paerl. 2010. Nutrient enrichment and selective predation by zooplankton promote Microcystis (Cyanobacteria) bloom formation. Journal of Plankton Research 0(0):1-14. Available online: www.plankt.oxfordjournals.org. Weaver, K. and J. Hendrickson. 2009. Remaining lakes nutrient criteria considerations. Presentation at Technical Advisory Committee Meeting, Aug. 5, 2009. Available online: http://www.dep.state.fl.us/water/wqssp/nutrients/meeting-arch.htm. Weisberg, R. H. 2010. Coastal Ocean and Estuary Water Quality: It’s all about Connectivity. Presentation at FDEP Estuarine Nutrient Criteria Workshop, St. Petersburg, FL, February 4, 2010. Available online: http://www.iswgfla.org/water/wqssp/nutrients/docs/estuarine/stpete/weisberg_presentati on_020410.pdf White, W. H. 1970. The geomorphology of the Florida Peninsula. Geological Bulletin No. 51. Bureau of Geology, Department of Natural Resources, Tallahassee, Florida. Accessed April 7, 2010. Available online: http://ufdcweb1.uflib.ufl.edu/ufdc/?a=fgs&b=UF00000149&v=00001.
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Whitmore, T. 2003. Water-Quality Trends Associated with Algal Coumminity Changes in Florida Lakes: Historic Evidence for Defining Nutrient Criteria. University of Florida, Department of Fisheries and Aquatic Sciences, Gainesville, FL, 106 p. Whitmore, T. and M. Brenner. 2002. Paleologic Characterization of Pre-disturbance Water Quality Conditions in EPA Defined Florida Lake Regions. University of Florida, Department of Fisheries and Aquatic Sciences, Gainesville, FL, 30 p. Wool, T. A., J. L. Martin and S. R. Davie. 2003. Modifications of WASP for simulating periphyton dynamics, sediment oxygen demand and other enhancements. National TMDL Science and Policy Conference, Proceedings of the Water Environment Federation, November 16-19, 2003, Chicago, AZ, 8 p.
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Eldon C. Blancher II, Ph.D. Authors Specific Qualifications related to Proposed Numeric Nutrient Criteria Dr. Blancher received his Ph.D. in Environmental Engineering Science from the University of Florida in 1979; since then a significant proportion of his professional work has involved assessing the effects of nutrients on aquatic systems throughout the southeast and particularly Florida. His dissertation work involved the development of nutrient loading budgets and mechanistic and empirical models for several lakes in central and north-central Florida. He performed post-doctoral work investigating the impact of nutrients, developing nutrient budgets, developing empirical models, and in particular, providing analyses determining which nutrients were limiting production in Lake Okeechobee. In the course of his consulting work he has developed nutrient budgets for Tampa Bay and its sub-basins for the Southwest Florida Water Management District. He also worked on delineating the impact of increased atmospheric deposition on Tampa Bay and its sub-basins and testified in administrative hearings regarding those investigations. In 2005, he was asked to assess the potential for nutrient loading into Apalachicola Bay from a highly controlled residential development proposed for Franklin County, Florida. He also assisted the Southwest Florida Water Management District by finalizing a detailed analytical model of nutrient loading and algal growth impacts in the Evers Reservoir in Manatee County, Florida. He has also reviewed and provided technical comments on a number of administrative rules and guidance documents prepare by scientists at the Florida Department of Environmental Protection including the Department’s Impaired Waters Rule which sets forth the methodology by which the State of Florida identifies waters that do not meet water standards and are therefore in need of restoration under the total maximum daily load (TMDL) program. He served as one of a number of experts consulted when the Department was establishing its biological health assessment tools for streams and needed technical assistance in establishing the States Biological Condition Gradient. Dr. Blancher served as an expert directly related to issues involving nutrients and their water quality and biological impacts upon freshwater and estuarine surface waters in Florida. I have been qualified as an expert in federal court, state court and in administrative proceedings. I have been qualified as an expert in legal proceedings in Florida on a number of occasions. Most recently I was qualified as an expert in environmental impact analysis of estuarine systems in Case No.: 08-2727EPP, In Re: Progress Energy-Florida Levy Nuclear Project, Units 1 and 2, before the Florida Division of Administrative Hearings. He also served as an expert assisting with a permit renewal for the City of Tallahassee and advised the City and its legal counsel as to the impact of nutrient loading upon Wakulla Springs. The matter was resolved prior to hearing. Consolidated Case Nos.: 06-1252, 06-1253, 06-1254, Florida Division of Administrative Hearings. Dr. Blancher also has extensive experience using water quality models and have developed, used and taught modeling throughout his thirty years as an environmental consultant and adjunct professor including models such a AQUATOX, WASP, QUAL II, 4/26/2010
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CE-QUAL-R1, CE-QUAL2-RIV1, CORMIX, DYNTOX, PLUME and UDKHDEN. He also served the Corps of Engineers as an IPA employee in the nutrient – primary producer algorithm development of CE-QUAL-R1. Dr. Blancher also has specific experience in projects involving nutrient loading and the setting of nutrient endpoints such as total maximum daily loads (TMDLs) and has published and presented technical papers on nutrients in aquatic systems. See, e.g., Blancher, E. C., S. A. Sklenar, R. A. Park and J. L. Wood. 1999. Determining the Linkages for a Nutrient TMDL in a Stream Listed as Use-Impaired for Endangered Species. Proceedings of WEFTEC, November 2002. Phoenix, Arizona.
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Attachment 1. Discriminant Analysis Output
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect AmmoniaN Artificial Channelization Score Bank Stability Score (Left Bank) Bank Stability Score (Right Bank) Color Conductivity, field Dissolved Oxygen Habitat Assessment Score Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Substrate Availability Substrate Diversity Water Total_P Turbidity Water Velocity Score Conductivity, field Artificial Channelization Score Bank Stability Score (Left Bank) Bank Stability Score (Right Bank) Color AmmoniaN Dissolved Oxygen Habitat Assessment Score
Summary of stepwise regression; variable: SCI_2007 Category (Streams_Discrim_data) Forward stepwise P to enter: .05, P to remove: .05 Steps Degr. of F to P to F to P to Effect Freedom remove remove enter enter status Step Number 1 2 8.6418 0.000186 Out 2 10.3892 0.000033 Out 2 7.9192 0.000380 Out 2 2.7858 0.062025 Out 2 0.2632 0.768594 Out 2 77.6887 0.000000 Entered 2 14.2197 0.000001 Out 2 44.2901 0.000000 Out 2 6.1427 0.002208 Out 2 8.0561 0.000332 Out 2 8.6070 0.000193 Out 2 4.9060 0.007531 Out 2 21.5374 0.000000 Out 2 6.0704 0.002372 Out 2 5.8325 0.003003 Out 2 4.8561 0.007914 Out 2 5.0051 0.006826 Out 2 28.0228 0.000000 Out 2 1.2153 0.296944 Out 2 7.7722 0.000440 Out 2 8.6533 0.000184 Out 2 2.1697 0.114595 Out 2 10.1007 0.000044 Out Step Number 2 2 77.6887 0.000000 In 2 14.4318 0.000001 Out 2 9.1820 0.000109 Out 2 4.8079 0.008302 Out 2 0.1756 0.839011 Out 2 6.8532 0.001092 Out 2 20.2429 0.000000 Out 2 47.0928 0.000000 Entered
62
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Sustainable Ecosystem Restoration, LLC
Effect Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Substrate Availability Substrate Diversity Water Total_P Turbidity Water Velocity Score Conductivity, field Habitat Assessment Score Bank Stability Score (Left Bank) Bank Stability Score (Right Bank) Color AmmoniaN Dissolved Oxygen Artificial Channelization Score Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank)
Summary of stepwise regression; variable: SCI_2007 Category (Streams_Discrim_data) Forward stepwise P to enter: .05, P to remove: .05 Steps Degr. of F to P to F to P to Effect Freedom remove remove enter enter status 2 9.6631 0.000068 Out 2 6.3661 0.001770 Out 2 6.7538 0.001205 Out 2 22.1654 0.000000 Out 2 28.0008 0.000000 Out 2 8.6717 0.000181 Out 2 8.8998 0.000144 Out 2 7.6370 0.000503 Out 2 7.9088 0.000384 Out 2 29.5306 0.000000 Out 2 3.5931 0.027769 Out 2 12.1759 0.000006 Out 2 6.8436 0.001103 Out 2 1.5624 0.210003 Out 2 14.1904 0.000001 Out Step Number 3 2 80.5958 0.000000 In 2 47.0928 0.000000 In 2 10.7260 0.000024 Out 2 103.3722 0.000000 Entered 2 0.8901 0.410861 Out 2 3.4766 0.031183 Out 2 20.2352 0.000000 Out 2 54.7656 0.000000 Out 2 63.1636 0.000000 Out 2 3.1491 0.043199 Out 2 3.3469 0.035477 Out 2 17.2810 0.000000 Out 2 15.9267 0.000000 Out 2 70.6921 0.000000 Out 2 71.3867 0.000000 Out 2 78.4827 0.000000 Out
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Substrate Availability Substrate Diversity Water Total_P Turbidity Water Velocity Score Conductivity, field Habitat Assessment Score Bank Stability Score (Right Bank) Bank Stability Score (Left Bank) Color AmmoniaN Dissolved Oxygen Artificial Channelization Score Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Substrate Availability Substrate Diversity Water Total_P Turbidity Water Velocity Score Conductivity, field
Summary of stepwise regression; variable: SCI_2007 Category (Streams_Discrim_data) Forward stepwise P to enter: .05, P to remove: .05 Steps Degr. of F to P to F to P to Effect Freedom remove remove enter enter status 2 80.2314 0.000000 Out 2 34.9162 0.000000 Out 2 93.5367 0.000000 Out 2 49.0850 0.000000 Out 2 3.4203 0.032979 Out 2 0.8097 0.445217 Out 2 36.9561 0.000000 Out Step Number 4 2 56.9441 0.000000 In 2 151.6081 0.000000 In 2 103.3722 0.000000 In 2 4.5341 0.010897 Out 2 0.0206 0.979565 Out 2 4.8594 0.007888 Out 2 38.2313 0.000000 Entered 2 8.9135 0.000142 Out 2 6.5132 0.001530 Out 2 4.6193 0.010012 Out 2 4.6252 0.009954 Out 2 24.7513 0.000000 Out 2 6.2067 0.002072 Out 2 17.1019 0.000000 Out 2 20.4091 0.000000 Out 2 10.1150 0.000044 Out 2 12.2082 0.000006 Out 2 1.8652 0.155262 Out 2 10.3782 0.000034 Out 2 24.6741 0.000000 Out 2 4.8314 0.008110 Out 2 1.5816 0.206015 Out 2 32.3702 0.000000 Out Step Number 5 2 63.0059 0.000000 In
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect Habitat Assessment Score Bank Stability Score (Right Bank) Dissolved Oxygen Color AmmoniaN Bank Stability Score (Left Bank) Artificial Channelization Score Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Substrate Availability Substrate Diversity Water Total_P Turbidity Water Velocity Score Conductivity, field Habitat Assessment Score Bank Stability Score (Right Bank) Dissolved Oxygen Water Velocity Score AmmoniaN Bank Stability Score (Left Bank) Artificial Channelization Score Habitat Smothering Score
Summary of stepwise regression; variable: SCI_2007 Category (Streams_Discrim_data) Forward stepwise P to enter: .05, P to remove: .05 Steps Degr. of F to P to F to P to Effect Freedom remove remove enter enter status 2 173.9455 0.000000 In 2 123.4121 0.000000 In 2 38.2313 0.000000 In 2 1.6001 0.202259 Out 2 3.0517 0.047597 Out 2 2.0005 0.135653 Out 2 9.5502 0.000076 Out 2 2.0489 0.129272 Out 2 2.8166 0.060153 Out 2 2.9045 0.055111 Out 2 6.7148 0.001253 Out 2 2.8583 0.057708 Out 2 18.3763 0.000000 Out 2 19.8861 0.000000 Out 2 11.2843 0.000014 Out 2 12.1685 0.000006 Out 2 0.4260 0.653208 Out 2 11.1339 0.000016 Out 2 20.7162 0.000000 Out 2 3.0010 0.050060 Out 2 0.6076 0.544781 Out 2 30.5357 0.000000 Entered Step Number 6 2 61.8019 0.000000 In 2 133.6688 0.000000 In 2 105.4603 0.000000 In 2 36.3779 0.000000 In 2 30.5357 0.000000 In 2 2.4057 0.090578 Out 2 1.3713 0.254126 Out 2 3.4451 0.032175 Out 2 0.3323 0.717341 Out
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Substrate Availability Substrate Diversity Water Total_P Turbidity Color Conductivity, field Habitat Assessment Score Bank Stability Score (Right Bank) Dissolved Oxygen Water Velocity Score Substrate Diversity Bank Stability Score (Left Bank) Artificial Channelization Score Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank)
Summary of stepwise regression; variable: SCI_2007 Category (Streams_Discrim_data) Forward stepwise P to enter: .05, P to remove: .05 Steps Degr. of F to P to F to P to Effect Freedom remove remove enter enter status 2 2.2419 0.106648 Out 2 2.2439 0.106426 Out 2 6.2006 0.002085 Out 2 1.0361 0.355096 Out 2 7.9614 0.000365 Out 2 8.5891 0.000196 Out 2 2.9160 0.054486 Out 2 2.8527 0.058031 Out 2 1.4406 0.237132 Out 2 9.2036 0.000107 Out 2 13.4357 0.000002 Entered 2 2.3262 0.098052 Out 2 0.6186 0.538863 Out 2 0.2934 0.745751 Out Step Number 7 2 65.1569 0.000000 In 2 96.1161 0.000000 In 2 101.6114 0.000000 In 2 33.3348 0.000000 In 2 23.1479 0.000000 In 2 13.4357 0.000002 In 2 1.0928 0.335579 Out 2 3.2166 0.040392 Out 2 0.3766 0.686230 Out 2 2.9093 0.054846 Out 2 2.9760 0.051323 Out 2 6.4631 0.001608 Out 2 11.3526 0.000013 Out 2 5.4666 0.004318 Out 2 7.0582 0.000892 Out 2 0.8373 0.433113 Out 2 1.2815 0.277958 Out
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect Habitat Assmt Secondary_sum Substrate Availability AmmoniaN Water Total_P Turbidity Color Conductivity, field Habitat Assessment Score Bank Stability Score (Right Bank) Dissolved Oxygen Water Velocity Score Substrate Diversity Substrate Availability Artificial Channelization Score Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Bank Stability Score (Left Bank) AmmoniaN Water Total_P Turbidity Color Conductivity, field Habitat Assessment Score
Summary of stepwise regression; variable: SCI_2007 Category (Streams_Discrim_data) Forward stepwise P to enter: .05, P to remove: .05 Steps Degr. of F to P to F to P to Effect Freedom remove remove enter enter status 2 10.3707 0.000034 Out 2 17.4671 0.000000 Entered 2 3.1089 0.044962 Out 2 3.0346 0.048416 Out 2 0.7142 0.489783 Out 2 0.3905 0.676806 Out Step Number 8 2 58.6850 0.000000 In 2 103.3420 0.000000 In 2 60.4742 0.000000 In 2 33.7318 0.000000 In 2 17.9535 0.000000 In 2 21.7462 0.000000 In 2 17.4671 0.000000 In 2 3.0362 0.048337 Out 2 0.6739 0.509906 Out 2 3.4501 0.032017 Out 2 3.4637 0.031587 Out 2 5.1337 0.006008 Out 2 2.6010 0.074560 Out 2 4.3941 0.012523 Out 2 6.2702 0.001946 Entered 2 0.7376 0.478455 Out 2 0.7076 0.493004 Out 2 2.8923 0.055783 Out 2 1.0672 0.344263 Out 2 3.6237 0.026939 Out 2 3.5463 0.029095 Out 2 1.0486 0.350723 Out 2 0.2013 0.817706 Out Step Number 9 2 57.1042 0.000000 In 2 86.8422 0.000000 In
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect Bank Stability Score (Right Bank) Dissolved Oxygen Water Velocity Score Substrate Diversity Substrate Availability Riparian Buffer Zone Width (Right Bank) Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Artificial Channelization Score Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Bank Stability Score (Left Bank) AmmoniaN Water Total_P Turbidity Color Conductivity, field Habitat Assessment Score Bank Stability Score (Right Bank) Dissolved Oxygen Water Velocity Score Substrate Diversity Substrate Availability Riparian Buffer Zone Width (Right Bank) pH Water Kjeldahl Nitrogen
Summary of stepwise regression; variable: SCI_2007 Category (Streams_Discrim_data) Forward stepwise P to enter: .05, P to remove: .05 Steps Degr. of F to P to F to P to Effect Freedom remove remove enter enter status 2 53.0167 0.000000 In 2 33.8156 0.000000 In 2 11.3182 0.000013 In 2 19.5520 0.000000 In 2 16.6598 0.000000 In 2 6.2702 0.001946 In 2 0.6669 0.513445 Out 2 3.2059 0.040823 Out 2 3.2484 0.039136 Out 2 4.7558 0.008743 Entered 2 2.1222 0.120164 Out 2 0.9995 0.368332 Out 2 2.6174 0.073353 Out 2 1.5006 0.223361 Out 2 3.7761 0.023149 Out 2 2.4151 0.089737 Out 2 0.9243 0.397045 Out 2 3.3963 0.033777 Out 2 3.3148 0.036632 Out 2 1.0076 0.365371 Out 2 0.4921 0.611438 Out Step Number 10 2 62.1464 0.000000 In 2 85.5529 0.000000 In 2 52.9783 0.000000 In 2 18.3002 0.000000 In 2 11.3081 0.000013 In 2 19.3753 0.000000 In 2 15.4146 0.000000 In 2 5.8908 0.002835 In 2 4.7558 0.008743 In 2 2.0711 0.126438 Out
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect "NO2NO3N" Habitat Smothering Score Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Artificial Channelization Score Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Bank Stability Score (Left Bank) AmmoniaN Water Total_P Turbidity Color Conductivity, field Habitat Assessment Score Bank Stability Score (Right Bank) Dissolved Oxygen Water Velocity Score Substrate Diversity Substrate Availability Riparian Buffer Zone Width (Right Bank) pH Riparian Zone Vegetation Quality (Right Bank) "NO2NO3N" Habitat Smothering Score Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Artificial Channelization Score Riparian Zone Vegetation Quality (Left Bank) Water Kjeldahl Nitrogen Habitat Assmt Secondary_sum
Summary of stepwise regression; variable: SCI_2007 Category (Streams_Discrim_data) Forward stepwise P to enter: .05, P to remove: .05 Steps Degr. of F to P to F to P to Effect Freedom remove remove enter enter status 2 2.0616 0.127639 Out 2 0.6845 0.504526 Out 2 2.1909 0.112202 Out 2 0.8637 0.421846 Out 2 2.5890 0.075459 Out 2 1.4729 0.229611 Out 2 3.7134 0.024640 Entered 2 2.4746 0.084568 Out 2 1.0165 0.362134 Out 2 2.2124 0.109822 Out 2 2.1311 0.119098 Out 2 0.7476 0.473715 Out 2 0.4764 0.621091 Out Step Number 11 2 61.5794 0.000000 In 2 87.3893 0.000000 In 2 36.3450 0.000000 In 2 17.9308 0.000000 In 2 13.0430 0.000002 In 2 21.8912 0.000000 In 2 16.2154 0.000000 In 2 9.0018 0.000131 In 2 4.6922 0.009313 In 2 3.7134 0.024640 In 2 1.9164 0.147522 Out 2 0.7845 0.456572 Out 2 2.1966 0.111575 Out 2 1.9671 0.140250 Out 2 3.1500 0.043164 Entered 2 0.9957 0.369728 Out 2 1.9287 0.145726 Out 2 2.6268 0.072676 Out
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect Bank Stability Score (Left Bank) AmmoniaN Water Total_P Turbidity Color Conductivity, field Habitat Assessment Score Bank Stability Score (Right Bank) Dissolved Oxygen Water Velocity Score Substrate Diversity Substrate Availability Riparian Buffer Zone Width (Right Bank) pH Riparian Zone Vegetation Quality (Right Bank) Artificial Channelization Score Habitat Smothering Score Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) "NO2NO3N" Riparian Zone Vegetation Quality (Left Bank) Water Kjeldahl Nitrogen Habitat Assmt Secondary_sum Bank Stability Score (Left Bank) AmmoniaN Water Total_P Turbidity Color
Summary of stepwise regression; variable: SCI_2007 Category (Streams_Discrim_data) Forward stepwise P to enter: .05, P to remove: .05 Steps Degr. of F to P to F to P to Effect Freedom remove remove enter enter status 2 1.0810 0.339559 Out 2 2.0658 0.127111 Out 2 1.9778 0.138761 Out 2 0.7159 0.488951 Out 2 0.3499 0.704834 Out Step Number 12 2 61.8034 0.000000 In 2 54.6550 0.000000 In 2 36.9208 0.000000 In 2 17.8953 0.000000 In 2 12.3796 0.000005 In 2 21.8373 0.000000 In 2 16.4307 0.000000 In 2 8.9251 0.000141 In 2 4.7585 0.008720 In 2 4.2744 0.014106 In 2 3.1500 0.043164 In 2 0.7141 0.489806 Out 2 0.7667 0.464751 Out 2 1.3623 0.256415 Out 2 1.9073 0.148881 Out 2 0.3558 0.700665 Out 2 1.9142 0.147847 Out 2 0.7198 0.487021 Out 2 0.8261 0.437972 Out 2 2.0520 0.128866 Out 2 1.9685 0.140056 Out 2 0.7662 0.464982 Out 2 0.3998 0.670512 Out
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect Intercept AmmoniaN Artificial Channelization Score Bank Stability Score (Left Bank) Bank Stability Score (Right Bank) Color Conductivity, field Dissolved Oxygen Habitat Assessment Score Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Substrate Availability Substrate Diversity Water Total_P Turbidity Water Velocity Score
Classification Functions for SCI_2007 Category (Strea Sigma-restricted parameterization Category 1 Category 2 Category 3 p=.1859 p=.4568 p=.3573 -18.9664 -15.7208 -12.5970 0.0000 0.0000 0.0000 -0.3017 -0.2644 -0.3070 0.0000 0.0000 0.0000 0.0599 0.1594 0.4135 0.0000 0.0000 0.0000 0.0011 0.0022 0.0035 0.0212 0.0172 0.0071 0.3302 0.3087 0.2729 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0005 -0.0058 -0.0128 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -0.0386 -0.1454 -0.3077 0.0000 0.0000 0.0000 -0.3787 -0.3285 -0.1747 0.0000 0.0000 0.0000 -0.2695 -0.1816 -0.1610 0.0312 -0.0501 -0.1434 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2531 0.2008 0.1513
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Effect Intercept AmmoniaN Artificial Channelization Score Bank Stability Score (Left Bank) Bank Stability Score (Right Bank) Color Conductivity, field Dissolved Oxygen Habitat Assessment Score Habitat Smothering Score Water Kjeldahl Nitrogen "NO2NO3N" pH Habitat Assmt Primary_sum Riparian Buffer Zone Width (Left Bank) Riparian Buffer Zone Width (Right Bank) Riparian Zone Vegetation Quality (Left Bank) Riparian Zone Vegetation Quality (Right Bank) Habitat Assmt Secondary_sum Substrate Availability Substrate Diversity Water Total_P Turbidity Water Velocity Score Eigenvalue Cum. Prop.
Standardized Canonical Discriminant Function Coeffic Sigma-restricted parameterization Function Function 1 2 0.00000 0.00000 0.00000 0.00000 0.27479 2.89067 0.00000 0.00000 -3.79867 -2.43436 0.00000 0.00000 -0.49880 0.17692 0.29567 0.18843 1.32645 0.08636 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.15819 -0.09841 0.00000 0.00000 0.00000 0.00000 2.74856 -0.21730 0.00000 0.00000 -2.23336 -1.88676 0.00000 0.00000 -0.92193 3.20246 1.78716 -0.97685 0.00000 0.00000 0.00000 0.00000 1.04487 -0.93351 0.58083 0.02108 0.96498 1.00000
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Attachment 1 to comments for EPA-HQ-OW-2009-0596
Sustainable Ecosystem Restoration, LLC
Classification Matrix (Streams_Discrim_data) Rows: Observed classifications Columns: Predicted classifications Percent Category 1 Category 2 Category 3 Correct p=.1859 p=.4568 p=.3573 Class Category 1 20.93023 54.0000 198.0000 6.0000 Category 2 79.17981 22.0000 502.0000 110.0000 Category 3 60.08065 2.0000 196.0000 298.0000 Total 61.52738 78.0000 896.0000 414.0000
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Attachment 2 Resume of Dr. Eldon C. Blancher II, Ph.D.
4/26/2010
Page
SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
ELDON C. (Don) BLANCHER, II., Ph.D. CEO & Chief Scientist SUSTAINABLE ECOSYSTEM RESTORATION, LLC 775 N. University Blvd, Suite 260A Mobile, Alabama 36608 +1.251.243.0376
[email protected] EDUCATION B.A. Biology – University of New Orleans, Louisiana, 1972 M.S. Zoology and Physiology – Louisiana State University, 1974 Ph.D. Environmental Engineering Science – University of Florida, 1979 Post Doctoral Fellowship, University of Florida, 1979 PROFESSIONAL AFFILIATIONS AND MEMBERSHIPS Water Environment Federation: Ecology and Water Resources Committee, chair 2005 -2009 Co-Chair – 2007 and 2009 TMDL Specialty Conferences Marine Water Quality Committee, Chairman, 1993-1996 Member, Ecology Committee, Vice-Chair 2003-2005 Member, Watershed Committee Member, TMDL/Pollution Trading Task Force Member, Program Committee 1991-96; 1998 - present Member, Surface water & Ecology Symposia, Program Committee Alabama's Water Environment Association Florida’s Water Environment Association Louisiana’s Water Environment Association Society of Environmental Toxicology and Chemistry Sediment Quality Work Group; Life-Cycle Assessment Workgroup International Society for the Advancement of Emergy Research National Association of Environmental Professionals (inactive) National Lead Abatement Council (inactive) National Society of Professional Engineers (Inactive) CERTIFICATIONS Hazardous Materials Certification (40 hour + 8 hour refreshers) Lead Hazards Risk Assessor (Inactive)
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SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
FIELDS OF SPECIALIZATION - Ecosystem Analysis, Assessment, Modeling, Planning and Restoration - Plankton/W etland/Seagrass/Sediment Systems - Aquatic Toxicology – Impacts to Aquatic and Marine Systems - Systems Ecology - Marine and Aquatic Systems - Scientific Data Management - Water Quality & Ecosystem Modeling - Statistics and Scientific Visualization - Numerical Taxonomy - Bioinformatics - Expert Witness EXPERIENCE SUMMARY Dr. Blancher founded Sustainable E cosystem Restorat ion, LLC in 2009, and brings with him ex tensive experience in assessm ent, analysis and planning of restoration projects. He is responsible for corporate performance for government and industrial client s; wit h emphasis on the north central gulf coast southeastern United States (Alabam a; Florida; Mississippi; Louisiana) but with extensiv e national experience. Dr. Blancher is a senior consultant performing general project managem ent, restoration planning and implementation, ecologi cal ass essment and modeli ng; EMERGY analys is; surface water quality modeling (aquatic & marine); expert witness; benthic macroinvertebrate assessm ent and analys is (aquatic & marine) ; wetland & SAV assessment and analysis; Clean Water Act permitting, data interpretation and reporting. other experience includes: Director of Southeastern Operations, Toxicological and Environmental Associates, Inc. (2005-2009). University of South Alabama (2004 – present) Adjunct Associate Professor, Environmental Toxicology Program. Graduate Faculty Member. Strand Ass ociates, Inc. – TAI Division (2000 – 2005) Director of Operations, Mobile, AL. Toxicology laboratory director 2000-to-2003. TAI Envir onmental Science, Inc. laboratory director 1986-to-2000.
(1986 – 2000) President
76
; Toxicology
SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
Taxonomic Associates, Inc. (1977 – 1986) President University of South Alabama (1988 – 1990; 1997) Adjunct Assistant Professor, Civil Engineering University of South Alabama (1983 – 1984) Assistant Professor, Department of Computer Sciences, Coordinator, Interactive Computing Laboratory, College of Medicine University of Alabama, Marine Sciences Program (1979 – 19 83) Research Associate (Dauphin Island Sea Lab), Assistant Professor, Biology Department PATENTS Jones, B.G., R.E. Gr eene and E. C. Blan cher II, 1990. Artifi cial Reef Module. US Patent # 4913094 Jones, B.G., R.E. Gr eene and E. C. Blan cher II, 1992. Artifi cial Reef Module. US Patent # 5113792 Jones, B.G., R .E. Greene and E. C . Blancher II, 1993. Artificial Reef Module. EP Patent # EP19920925008 – WIPO Publication Number WO/1993/003229 HONORS AND AWARDS 2000. Alabama Water Environment Association. Laboratory Analyst Award. Water Environment Federat ion. Comm ittee Ser vice Award. M arine W ater Quality Committee - Committee Chairman. 1993-1996 Water E nvironment Fe deration. C ommittee Ser vice Award. Ecology and Aquatic Resources Committee Chairman. 2005-2009 SELECTED PROJECTS
Expert – New Jersey Attorney G eneral’s Office; NJ Depart ment of Conservation and Natural Res ources. Coastal W etlands As sessment, Restoration and Natural Res ource Dam age at Hercules Corporation Sit e Parlin, N.J. Ongoing litigation, Kanner and Whiteley, LLC. (2009-2010).
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SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
Project Manager-Chief Scientist. Se minole Electric Cooperative- Medora Facility- Tox icity Permitting Issues – St ate of Florida. Seminole Electric Cooperative Inc. Tampa Fl. (2008-2010)
Consulting Expert Scientist - Coordi nating Group, Florida Energy Council Environmental Committee - Expert representation for Nutrient Rule Making Process and Biocriteria Rule discussi on – State of Flor ida - Hopp ing Green and Sams (2009).
Expert - Cross Bayou, Destrehan Louisiana, Restoration Planning and Contaminant Assessment – Litigation – Smith & Fawer, LLC. 2008-2009
Expert – Water Quality and Ecology. Progress Energy Corporation’s Levy County Nuclear Plant Permit – Admini strative Hearing. Hopping, Green and Sams. ( 2008-2009)
Expert – New Jersey Attorney G eneral’s Office; NJ Depart ment of Conservation and Natural Resources. Coastal Wetlands Restor ation a nd Natural Resource Damage at Bayway and Bayonne Refinery. Ongoing litigation, Kanner and Whiteley, LLC. (2006-2010).
Managing Scientist. Negotiation and preparation of mitigation banking documents including Miti gation Prospec tus for tw o Priv ate Clients , Terrebonne and LaFourche Parishes, Louisiana. 2008-2009.
Project Scientific Advisor. Na tional Resource Damage Ass essment (NRDA) vo luntary settlement ne gotiations f or contaminated sediments in Bayou Trepagnier, Louisiana. Representing Coa lition to Restore Coastal Louisiana and Lake Pontchartrain Basin Foundation. (2001– present)
Expert Tulane Law Clinic- Interveners for State of Louisiana Department of Environmental Qualit y. Administrati ve Hearing on El Dorado Pipeline Permit, Arkansas Department of Environmental Quality. (2007)
Expert to State of Florida. Biol Florida Systems. (2006).
Expert- City of Tallahassee. Nutr ient Impacts to Submerged Aquatic Vegetation in F lorida Springs. Consult ant to City of T allahassee, litigation and permit negotiations. Hopping Green and Sams, Tallahassee. (2006)
ogical Condition Gradient Workgroup for
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SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
Expert – F LUM Administrative Heari ng, Bay County F lorida. Wetland an d Water Quality impacts from Propos ed Marina on Gulf Intercoastal Waterway, Bay County Florida. Hopping Green and Sams, Tallahassee (2006)
Solutia, Inc. Pensacola Florida. Permit renewal assistance including producing an expert analysis of permit variance for copper. (2006)
Testifying Expert - Hopping Green & Sams. Expert witness. St. Joe Corporation, Intervener on Franklin County, Flori da, FLUM litigation. Estuarine wetlands (including s eagrasses) – nutrient issues and impacts. (2005)
Mobile Bay National Estuary Program . E MERGY analysis of two Mobile Bay sub-watersheds. Project result ed in a full EMERGY accounting and development of a Landscape Developm ent Index and spatial model of the Dog River and Fis h River sub-watershed which drain into Mobile Bay. (2003-2004)
Terrebonne Basin Dissolved Oxygen St udy – Cons ulting Sc ientist 2005. Sampling and analys is of reference ar eas in Terrebonne Bas in for the USEPA under a contract to Arcadis. Cons ultation to USEPA and LaDEQ (2005).
University of South Alabama USAID Birth Defects Project- Ukraine. Helped establish the Web-based systems for the USAID BD program. Also provided c onsultation on environment al is sues and recommendation of various software platforms, including collaborative softw are platform for web-medicine initiative (1996 – Present).
Mobile Bay National Estuarine Pr ogram- WQ CoChair. Preliminary Characterization of Water Quality fo r the Mobile Bay Natur al Estuary Program. Involved extensive review of water quality literature and previous data collec ted on Mobile Bay. Included preliminar y Nutrient budget for Mobile Bay. Mobile Bay National Estuary Program (1997 – 1998, 2002 – 2003).
Seagrass Survey for Mixing Zone St udy, City of Palmetto, Florida. Performed detailed seagrass asse ssment and impact assessment of wastewater discharge in Terra Ceia Bay (Outst anding Florida Water) for permit renewal. Jones Edmunds & Assoc iates, Gainesville FL. (20012002).
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SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
Project Scientist. Stormwater Wa ter Quality iss ues in Manatee and Sarasota Counties, Florida. AQ UATOX modeling of Evers reservoir. With Singhofen Associates for Southwest Florida Water Management District. (2003 – 2008)
Project Manager. St ormwater, wetl ands and various water quality and environmental iss ues under Tas k Order Contract with Jones Edmunds & Associates for Sarasota County Florida (2003 – 2005, ongoing)
Project Manager, Toxicity Identificat ion Evaluation. for ion- i mbalance issues with Mysidopsis bahia in discharge t o St. Johns River. Seminole Electric Corporation. Palatka, Florida. (2001 – 2005)
Project Consultant for stormwater impact planning and Expert on Nutrient Loading Issues from wetlands/devel opment near Appalachic ola Bay, Florida. ARVIDA Corporation and Hopping Greene Sams & Smith, Tallahassee Florida. (2002-2004).
Project Manager. USEPA Coast al EMAP Macroinvertebrate Identification, Enumeration and Analyses. Coastal Maine to Texas. USEPA Gulf Breeze Laboratory. (2002-2005).
Project Manager. Habitat Asses sment and Biological studies for SRCER Study, Banklick Creek, Kentucky. Sanitation District 1. Covington Kentucky. (2001 – 2003)
Expert Witness. Repr esented City of Fairhope, Al abama in effluent metal-sediment contamination issues in Yardarm Resturant vs Booth Case. Baldwin County Circuit Court. (2000 – 2001)
Expert. Coordinating Group, Flor ida Energy Counci l Environmental Committee and Florida Paper and Pulp Association . Consultation regarding the Florida Impaired Waters Rule (2000 –2001)
Project Scientist. Kokomo Indiana, Stream Reach Characteriz ation of Biological and Chemical impacts to the Kokomo and Wildcat Creek , City of Kokomo, Indiana. Brownfield grant to establis h municipal park areas in former superfund site. Included Habita t Assessments, Biological Sampling and Sediment Profile Analyses of st ream through area potentially impacted by combined sewer overflows and the superfund site (CSO’s) an d (2000 – 2002)
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SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
Project Scientist. Peru Indiana, Stream Reach Characte rization of Biological and Chemical impacts to t he Wabash River, Indiana. Habitat Assessment and Rapid Bioassessment Protocol (2000 – 2002)
Project Manager. Jefferson County, Alabama. Directed Nutrient Utilization Study (1999) and Periphyton-Nut rient relationship Study (2000) for data collection efforts related to TMDL development for the Cahaba Riv er, Alabama. Included pr eparation of a third-party nut rient target study (for Phosphorus) in support of the State developed TMDL . Included stressor identification for periphyton growth using AQUATOX (1999 – 2004)
Project Manager. Charlton Count y/DuPont Collaborative Process (adjacent to Okefenokee Swamp, Georgia) to resolve permitting i ssues (including mitigation considerations) regarding proposed titanium mine for 39,000 acres. Selected as Independent Techni cal Entity (ITE) to advise all stakeholders (mining, environmental and other interested parties) on issues surrounding mine development. Deve loped an independent Technica l Advisory G roup (TAG) of top-caliber scientists, and prepared white paper on Okefenokee Research and Education Center and Land Trust involved in the no-mining scenario. RESOLVE, Inc. (1998 – 1999)
Project Manager. Wetlands Determinat ion at Louisiana National Guard, Camps Beauregard and Camp Livingston, Alexandria, Louisiana consisting of over 18,000 acres of land area. (1996)
Project Manager. Wetlands Det ermination on 9,000 plus acres of land at Louisiana National Guard, Camp Villere, Slidell Louisiana. (1996)
Systems Analys is Director. Direct ed and directly participated in all modeling projects for both the firm and prior entities si nce 1978 including eutrophication modeling and wasteload allocation st udies for numerous watershed/lake and estuarine systems in Florida, Geor gia, Alabama, Mississippi and Louisiana. (1978 – present)
Laboratory Director. Direc ted all laboratory operati ons in TAI Toxic ology and Taxonomy Laboratory Mobile Alabam a (1986 – 2003) with c ertification in the State of Florida since 1988 and NELAP Certif ication. Als o directed toxicology laboratory oper ations in St. Rose, Louisiana (1989-1992) and Greenville, South Carolina (19 96 – 199 9), with certifications in Sout h Carolina and North Carolina. Approx imately 600 bioa ssay tests annually under various NPDES and OPPTS protoc ols. Taxonomic analysis of plankton, macrobenthos (including E PA EMAP samples) and fis h samples from Maine to the Florida Keys (Atlant ic), Florida to Texas (Gulf Coast), and the Bahamas, Puerto Rico and Venezuela. 81
SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
Senior Aquatic Toxico logy Pr oject Manager. Senior manager on all toxicology and Toxic ity Reduction Ev aluations/Toxicity Identification Evaluations (TRE/TIE) for TAI Environmental Scienc es Inc. R eviews all large scale work pr oducts and direct s all staff s cientists and project managers. (1986 – 2005).
Project Manager. Taxonomic Asse ssment of Several Estuaries in Panhandle Florida. Ecological assessm ent of receiving waters receiving cooling wa ter discharges from power generation facilities in s upport of regulatory requirements. Gulf Power Corporation. (1998 – 1999)
Project Scientist. Workplan develop ment for offsite ecological ris k assessment near Anniston Alabama. Re mediation project involving PC B contamination of stre am and lake bed fate and transport and potential source of aquatic food-chain uptake . Blas land, Bouc k & Lee. (1998 – 1999)
Project Scientist and Environmental C onsultant to Birth Defects Project: Developing environmental linkages to birt h defects, Ukraine. University of South Alabama and USAID. (1998 – 1999)
Project Manager. Ecological Survey and Taxonomic Analy sis for the Caloosahatchee River -Orange River Florid a. Florida Power and Lig ht. (1998)
Project Scientist. Preliminary Char acterization of Water Quality for the Mobile Bay Natural Estuary Program. Involved e xtensive revie w of water quality literature and previous data collect ed on Mobile Bay. Included preliminary Nutrient budget for Mobile Bay . Mob ile Bay Nationa l Estuary Program. (1998)
Expert Witness. Biological Impacts from Improperly clos ed was te oil pits, Barataria Bay, Louis iana. Repres ented parish gov ernment on impact assessment, ecological risk asses sment and remediation options including creation of new marsh. Martzell an d Bick ford, New Orleans. (1998 – 1999)
Discharge Plume Modeling, Lower P earl River - Main Stem. Senior scientist on instream wast e determination study for a new disc harge in the Lower Pearl River, MS. Conducted CORMIX modeling and sensitivity study and oversaw toxicity testing for dete rmining IWC for new disch arge J.M. Montgomery. (1997) 82
SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
Expert Witness, Sediment Contami nation ( Lead & PAH). Key witness on Impact on biology and ecology of exce ssive lead levels in sediments and soils in a Louis iana Bayou sys tem. Te stified on fate and transport and toxicity of sediment metals and mob ilization and fate of Lead and PAHs in a swamp environment and commented on restoration options. A. Kanner & Associates. (1997)
Project Manager. Wetlands Determinat ion at Louisiana National Guard, Camps Beauregard and Camp Living ston, Alexandria, Louisiana. A determination and mapping of over 18, 000 acres of base areas. (1996 – 1997)
Project Manager. Wetlands Determinat ion at Louisiana National Guard, Camp Villere, Slidell Louis iana. We tlands mapping pr oject for over 7,000 acre military base. (1996)
Expert Witness. Orimulsion Conver sion Project, Florida Power and Light , Manatee Plant. Developed compar ative Nitrogen loading budgets for determining water quality and biological im pacts associated with increase N inputs from Atmospheric deposit ion to Tampa Bay and to several Outstanding Florida Waters (OFW). Reviewed WASP and other modelin g efforts by South Wes t Florida W MD. Also testified about effects of water withdrawals from the Little Manatee River. (1995 – 1996)
Project Manager. Historic Lake Level Changes for B anks Lake Georgia, Reviewed historical information and designed and implemented basin investigation including sediment and vegetation analysis to determine Historic Lake Levels. U.S. Fish & Wildlife. (1994)
Project Manager. Former Super Fund Site, Pascagoula MS. Investigated fate of creosote residue in a c losed superfund site. Conducted erosion , sedimentation rate and macroinvertebrate studies, and also prepared study using sediment profile techniques to assess creosote migration along t he Pascagoula River. CSX Railroad through Geraghty-Miller (1994)
Project Manager. Tidal Survey of the Jourdan Tidal Chann el, Mississippi for determining tidal prism and fres hwater flushing. Performed an extensive diel salinity and hydrographic survey to determine the tidal status of several river areas. (1993)
Project Manager. Water Effect Ratio Study, and Site specific Analysis, City of Fort Wayne Indiana. Directed ex tensive site specific analyses for determining the impact of the metals Cadmium, Lead and Zinc to the White River, Indiana. (1992 – 1993) 83
SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
Project Manager. Tampa Bay Nutrient Study. Responsible for determining nutrient budgets (nitrogen, phosphorus and Silica) and limiting nutrients for Tampa Bay. Includes data review, nonpoint and point source loading estimates and internal nutrient regeneration. (1990 – 1991)
Principal Scientist, Project Manager , ICI Americas Inc. Performed Wetlands delineation, Mixing Zone anal ysis and plume fate modeling for proposed outfall project, Axis, Alabama. Included all aspects of permitting process, including mitigation considerations. (1991 – 1992)
Project Manager. Toxic ity Reduction Evaluation for industrial wa stewater system, sp ecialty chemical manufacturer for Tributyl Tin (TBT). Included treatability studies on wast ewater, including extens ive toxicologica l testin g from two North American fa cilities, and extensive aquatic toxicity studies t o extend fate and transport database fo r the development of water quality criteria for TBT. Also performed CO RMIX modelin g s tudies to d etermine Instream Waste Concent ration and DYNTOX M onte Carlo probabilistic modeling for determining downstream fate and effects of the compound. Market basket study coordinator for t he northern Gulf Coast area . (1989 – 1994)
Project Scientist. Sit e evaluation, biological community analysis and dat a collection risk assessment of clos ed Pan American refinery on Mississippi River, Louisiana. Helped develop portions of RI/FS for remediation options at this Amoco site. Montgomery-Watson Engineers. (1990 – 1991)
Principal Scientist, Project Manager , ICI Americas Inc. Performed Wetlands delineation, Mixing Zone analysis (including dy e studies, UDKHDEN and CORMIX modeling) and do wnstream plume fate modeling for proposed outfall project, Axis, Alabama. (1991 – 1992)
Project Manager. Mixing Zone Analysis, Atochem. Responsible for project management, modeling, data collection, data processing and reporting of isopleth data using Rhodam ine WT dye to determine outfall mixing zone, concentration. This was combined with further environmental assessments to give a comprehensive mixing zone analysis. (1988 – 1990)
Principal Scientist. Produced water plume modeling. Res ponsible for model dev elopment, modeling and data an alysis in predicting the fate of various constituents in an offshore produced water discharge. Resulted in development of the Ocean Discharge Criteria Document for a large offshore mining client. (1988 – 1991) 84
SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
Project Manager. Mixing Zone Anal ysis. Responsible for project management, modeling, data collection, data processing and reporting of isopleth data using Rhodam ine WT dye to determine outfall mixing zone, concentration. This was combined with further environmental assessments to give a comprehensive mixing zone analysis. (1988 – 1990)
Project Manager. Water Resource Study. Analysis and review of municipal and industrial effluent di scharges into the streams of the Tombigbee-Black Warrior River Basins. U.S. COE, Mobile District, Mobile, Alabama. (1987 – 1988)
Principal Scientist. Diffuser Design Modeling. Responsible for all modeling and sensitivity analysis of diffuser plume models using PLUME, UDKHDEN and CORMIX for several c orporate clients in Alabama, Kentucky, Tennessee and Arkansas. Included re -coding of F ORTRAN model for inclusion of ambient effects not in cluded in existing model and changes in model geometric representations. (1987 – 1998)
Project Manager. Environmental Im pact Assessment. Respons ible for subcontractor negotiations and overall management of extens ive study for Monitoring Environmental Impacts Asso ciated with Open-Water Thin-Layer Disposal of New Work Dredged Material at Gulfport Harbor, Mississippi. Study inc luded automated precision bat hymetric surveys, extens ive biological sampling and analy sis (benthic and demersal organisms), sediment profiling and water quality studies. (1986 – 1988)
Project Manager. Environmental Im pact Assessment. Respons ible for overall management, budget, scheduling and reporting for Monitoring Environmental Impacts Associat ed wit h Thin-Layer Open-Water Dispos al of Dredge Material at Fowl River, Alabama. Included comprehensive water quality, and biological sampling of pre- during and postdredging operations. (1986 – 1988)
Project Manager. Water quality (WQ) survey of Coast Guard Stations, Destin and Pensacola, Florida and Port Isabel, Texas. Performed overall WQ surveys and diurnal oxygen metabolism studies to determine sediment oxygen contribution from these docking facilities. (1985 – 1986)
University of South Alabama. Southeast Regional Genetic Group (SERGG). Advisor to Department of Medica l Genetics in charge of data management and graphical descriptions for establishing a reporting system for Genetics and Birth Defect Screening program. This effort impacted the national approach to assess the status of Birth Defects in several southern states. (1983 –1984). 85
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Eldon C. Blancher, II., Ph.D.
Project Manager. Collection and reporting for metal corrosion and bioaccumulation of radioactive boiler plate study on samples placed in near shore shallow-water areas (Card Sou nd) in the Flor ida Keys. Knolls Atomic, (Navy Contract to DOE). (1983 – 1987)
Project Manager. Assessment of St ormwater Lake Systems, Kings Bay Naval Base, St. Mary's, Georgia. Deter mined stormwater nutrient and sediment loadings and modeled various water quality parameters. Jones, Edmunds & Associates. (1985)
Project Manager. Coastal Land Use Co mpilation for the Baldwin County, Alabama Coast. Included a s ewer and water survey and design of the permit process for county administrators. Baldwin County. (1984)
Principal Investigator. Compilation of biologic al and chemical data for Mississippi and Alabama Coastal Ar ea (Mississippi Sound and Adjacent Areas Study), analy sis of tidal and water quality parameters inclu ding nutrient inputs from major str eams and designed a data management system fo r use by environmental planner s at the USCOE Mobile District. (1983 – 1984)
Project Scientist. Assessment of im pacts to aquatic biological systems resulting from the construction of the Tennessee-Tombigbee Waterway. Baseline characterization, data management and analys is (SAS), assessment of impacts to fishery res ources. Mississippi and Alabama. (1979 – 1983)
Consultant to Sea Grant Advisor y Service. Biological Advisory Committee for Mobile District COE. (1981 – 1983)
Project Manager. Assessment of impacts associated with the dredging and operation of the Th eodore Ship Channel. Data management, statistic al analyses, impact assessment, Mobile, Bay, Alabama. (1979 – 1982)
Project Scientist Analysis of zooplankton community structure in a series of hydropower reservoirs. Coosa River , Georgia and Alab ama. (1980 – 1981)
Project Scientist. Evaluation of biological strategies for macrophyte control. Ecological modeling, development of a hydrologic-nutrient systems models using FORTRAN and CSMP, data m anagement and analys is us ing SAS. Included development of nutrient-macrophyte algorithm for CE-QUAL-R2 at 86
SUSTAINABLE ECOSYSTEM RESTORATION, LLC
Eldon C. Blancher, II., Ph.D.
USCOE W aterways experiment station. Lake Conway, Florida. (1978 – 1982)
Project Manager. Comprehensive environmental monitoring and assessment of environmental monitoring and assessment of environmental sensitivity. Broad-based char acterization of estuarine water quality, plankton, benthos, sediments, and fisher y resources. Biological analysis, data management, evaluat ion, management recommendations. Mobile Bay-Mississippi Sound.
Principal Investigator. Developm ent of a plan of study for the utilization of a salt marsh system for the treatment of seafood wast es. Performed Field studies and nutrient modeling of nitrogen transformations in s alt marsh and developed ultimate loading rates fo r wastes. Bayou LaBatre, Alabama. USEPA. (1979 – 1981)
Quality Control Manager fo r the taxonomic analys is of biota collected from the St. John's River, Florida. Ebasco Environmental Services. (1980)
Project Scientist. Characterization of the z ooplankton community of Lake Lanier and Lake Seminole, Georgia. COE. Mobile District (1978 – 1979)
Project Scientist. Environmental Assessment. Evaluation of the impacts of agricultural runoff of Lake Okeechobee, Florida. Included development of nutrient budgets, analysis of wasteload allocations from various land-us e types and development of empirical and deterministic models of nutrient utilization in Lake Okechobee. (1978 – 1979)
Project Scientist. Characterization estuaries in support of area wide Southwest Florida. (1977 –1978)
Project Scientist. Characterization and sensitivity analys is of aquatic communities at a proposed source of power plant cooling water . Lake Rousseau, Florida. (1977)
Project Manager. Es tuarine fish surv eys. Coastal Louisiana. (1973 – 1974)
Cruise Sc ientist. Survey of the fish and epibenthic fauna of Hudson Canyon, North Atlantic Ocean. (1973)
INVITED PAPERS 87
of the plankton communities of six 208 water management planning.
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Eldon C. Blancher, II., Ph.D.
Hird, J. and Blancher E.C. Challenges of Socio-economic research in Watersheds. The 7th Randall L. Gibson Tulane University Conference on the Mississippi River. 18 November 2004 New Orleans, LA.
PUBLICATIONS AND REPORTS Blancher, E.C. 2009. Restoration Recommendation for Bayou Trepagnier. Report to the Louisiana Department of Environmental Quality. Prepared for the Lake Pontchartrain Basin Foundation and the Coalition to Restore Coastal Louisiana. 32 pp. Hunter, A., R. C. Brassieur, B. Vivas and E.C. Blancher. 2009. “Valuing the Subsistence Use of Goods and Services in Louisiana Coastal Communities: Using Emergy to Analyze Non-Market Activities.” In: Emergy Synthesis 5: Theory and Applications of the Emergy Methodology, (Ed: M. T. Brown), (2009) in Press. Hunter, A. E., B. Vivas, and E.C. Bl ancher. 2008. Habitat Evaluation and Assessment Tools (HEAT) Planning Model Certification Report. Prepared For: U.S. Army Corps of Engineers Under Contract No.: W912HQ-04-D-0008. S. Janga, E. C. Blancher II, and M. Misra, "Emergy Analysis for Correlating Environmental Pollution with Birth Defects", Environ. Res., (submitted, 2008) Blancher, E.C. and A. Cincovich. 2008. Braden river watershed management plan Surface water resource assessment. Final report (Draft). Volume II. Evers reservoir water quality report. Southwest Florida Water Management District. Brooksville, Florida. 59 pp. Hunter, A., B. Vivas, E. Blancher and R. El Farhan. 2008. Wetland Value Assessment Model Application in the Sabine-Neches Waterway Channel Improvement Project - Model Assessment Report. Report to USCOE Institute for Water Resources. 76 pp. Blancher, E.C. and Y.J. Etheredge. 2007. Emergy Analysis of two watersheds in the Mobile Bay Estuary area, Alabama, USA. Ecological Questions 8/2007:87-97. W. Thompson, E. C. Blancher II and M. Misra. 2007. “Emergy Analysis and the Eigenvalue Method for Solving Transformities using Microsoft Excel”, In:
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Eldon C. Blancher, II., Ph.D.
Emergy Synthesis 4: Theory and Applications of the Emergy Methodology, (Ed: M. T. Brown), (2007). Blancher, E.C., B. F. Droy and A. M. Cincovich. 2007. Expert Environmental Assessment for a Color Additive. FDA Petition for Bismuth Citrate. Report to Combe, Inc. White Plains, NY. 41 pp. Blancher, E.C. and A. Hunter. 2006. Ex pert Assessment of Impacts of Total Recoverable Copper Variance f or the E scambia Riv er, Florida. Report to Solutia, Inc. Pensacola, Florida. NPDES Permit # FL 0002488. Blancher, E.C. and Etheredge, Y. J. 2006. Emergy Analysis of Two Watersheds in the Mobile Bay Estuary Area, Alabama USA. In: Brown & Bardi, Proceedings of the Fourth Biennial Conference on Emergy Analysis Research Conference. January 24, 2006, Gainesville Florida. S. Janga, E. C. Blancher II, and M. Misra, “Emergy and Transformity Matrix Analysis for Correlating Environmental Pollution with Birth Defects”, Annual AIChE Meeting, Nov 12 – 17, San Francisco, CA, (2006). Blancher, E.C., S. A. Sklenar and B. G. Jones. 2003. Fi nal Report on Stream Reac h Characterization for Banklick Creek, Kentucky . Report to Kentucky Sanitation District 1, Covington Kentucky. Park, R. A., E. C. Blancher, S. A. Sklenar, and J. L. Wood. 2002. Modeling the Effects of Multiple Stressors on a Use‐Impaired River. in Society of Environmental Toxicology and Chemistry, Salt Lake City. Blancher, E.C., S. Sklenar, R Park and J.L. Wood. 2002. Determining the Linkages for a Nutrient TMDL in a Stream Listed as Use-Impaired for Endangered Species. National T otal Maxim um Daily Load Conf erence. Proceeding of Water Environment Federation Conf erence held in Phoenix, Arizo na November 2002. Blancher, E.C., S. Sklenar and J. L. Wood. 1999. Nutr ient Utilization During Lowflow Conditions along the Cahaba Riv er Alabama. Proceedings of WEFTEC, October 1999. New Orleans, Louisiana. Blancher, E. C., W. Ondler, J. Gnecco and C. Po llman. 1998. Trading Nitrogen Loading in Tampa Bay. Maintaining th e Nitrogen Loading Goals of the Tampa Bay National Estuar y Program. A ccepted: Proceeding of W atershed 98. Watershed Managem ent: Moving from Theory to Implementation. May 3-6, 1998. Denver, Colorado.
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Blancher, E. C. 1996. Wetlands Determi nation at Louisiana Na tional Guard, Camp Villere, Slidell, Louisiana. April 1996. Report and Maps prepared for Louisiana National Guard, New Orleans. Blancher E. C. and W. F. Geers. 1994. Historic Lake Changes for Banks Lake , Georgia. Prepared for U.S. Fis h and Wild life Servic e. Atlanta, Georgia. 153 pp. Blancher, E. C. and J. R. Stewart. 1993. Tidal Pris m and Freshwater Exchange of The Jourdan Tidal C hannel, Bay St. Louis , Mississipp i. Final Report to First Mississippi Investment Corporation. Blancher, E. C. 1993. Be nthic Invertebrate Community Analyses Along the Centra l Coast of the Gulf of Mexico. Prec eedings of a Confer ence on Marine Water Quality Monitoring. Water Environment Federation. October 21, 1993 . Anaheim, California. Blancher, E. C., B. G. Jones and R. E. Greene. 1991. Reef Structure and Reef Function: Engineering and Mat erial Consi derations for Artificial Reef Design. Proceedings of Fifth Inter national Conference on Arti ficial Habitats. Long Beach, California. 3-7 November 1991. Bull. of Mar. Science. (abstract). Blancher, E. C. and J. R. Stewart. 1991. Interim Nu trient Loading Bu dgets for the Tampa Bay System. Report to Foth and Van Dyke Engineers. Green Bay Wisconsin, prime contractors to S outhwest Florida Water Management District. Blancher, E. C. and J. R. Stewart. 1991. Nitrogen and Phosphorus Limitation of the Tampa Bay, Florida System. Report to Foth and Van Dyke Engineers. Green Bay Wisconsin, prime contractors to Southwest Florida Water Management District. Blancher, E. C. and R. Austin. 1988. (princi pal autho rs). Monitoring Env ironmental Impacts Associated with Open-Water Thin Lay er Dispos al at Fowl Riv er, Alabama. Final Report, Contract DACW01-86-C-0099. U.S. Army Corps of Engineers, Mobile District. Blancher, E. C. and R. Austin. 1988. (princi pal autho rs). Monitoring Env ironmental Impacts A ssociated with O pen-Water Thin Layer Disposal of New Work Dredged Material at Gulfport Harbor, Mi ssissippi. Final Report, Contrac t DACW01-87-C-010. U.S. Army Corps of Engineers, Mobile District. Blancher, E. C. and K. Shaw. 1988. (princi pal authors). Analysis of Municipal an d Industrial Effluent Discharges into t he Streams of the Tombigbee-Black 90
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Eldon C. Blancher, II., Ph.D.
Warrior Basins. Final Report, Contract DACW01-87-C-0144 to TAI Environmental Sciences Inc.. U.S. Army Corps of Engineers, Mobile District. Stout, J.D., E.C. Blan cher and M. Dardea u. 1986. Utiliz ation of a Saltwater Marsh Ecosystem for the Management of Seafood Processing Wastewaters. 197 pp. EPA 904-9-86 142 Blancher, E. C. 1985. Util ization of a Nat ural S altmarsh as an Efficient, Low Cost Management System for Screened Seaf ood Wastewaters: Engineerin g considerations. Final Report to the Marine Environmental Scienc es Consortium, EPA Contract. Blancher, E. C. 1984. Zoopl ankton Trophic State Relationships in Some North and Central Florida Lakes. Hydrobiologia 109:251-263. Blancher, E. C. 1983. An Inf ormation Management System for the MississippiAlabama Coastal Zone: Design and Im plementation. Proceedings of the ASCE Coastal Zone 83 Conference. June 1983, San Diego, California. Blancher, E. C. 1982. Application of Existing Data M anagement Sys tems to compute and document Various Data Elem ents for the Mississippi Soun d and Adjacent Areas Study. Final Report to U.S. Army Corps of Engineers, Mobile District. Contract DACQ-82-M-9911. Blancher, E. C. 1982. Establishing a Biological Benchmar k for Mobile Bay: Rationale Methods and Progres s. Final Report to the Alabama Coastal Area Board. Blancher, E. C. and C. G. Buglewicz. 1982. Large Sc ale Operations Management Test of use of the whit e Amur for Control of Aquatic Plants; Report 1, Baseline Studies; Vol. VII Summary of Baseli ne Studies and Data. U.S. Army Engineer Waterways Experiment Station. Tech. Report A-78-2 Vol. VII. Blancher E. C. 1982. A Description of the Marine Envir onmental Sciences Consortium Data Management System. Dauphin Island Sea Lab Technical Report 82-002. Blancher, E. C. 1981. Data Managem ent Systems for the Theodore Chann Project. Report to U.S. Army Corps of Engineers, Mobile District.
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Blancher, E. C. 1980. Data M anagement Systems for the Large Scale O perations Management Test at Lake Conway. Final Report. U.S. Corps of Engine ers Waterways Experiment Station. Miscellaneous Paper.
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Blancher, E. C. 1980. Impact of Stormw ater Runoff on a Florida Lake Ecosystem: Effects on Water Quality and Biota. U.S. EPA Conference Proceeding s. Municipal Environmental Research Laboratory, Cincinnati EPA 600/9-80- 056. Blancher, E. C. and C. R. Fellows. 1980. Nitrogen and Phosphorus Dynamics of the Lake Conway Ec osystem: Loading Budgets and a Dynamic Hydrolo gicPhosphorus Model. Final Report to U.S. Army Corps of Engineers Waterways Experiment Station. Brezonik, P. L., E. C. Blanc her and V. B. Myers. 1979. Factors Affecting Primary Productivity in Lake Okeechobee, Florida. Final Report to the Florida Sug ar Cane League. Department of Environmental Engineering, Univ. of Florida, Gainesville, Florida 07-70-01. Conley, R. A., E. C. Blancher and F. Kooi jman. 1978. Biologic al Baseline Studies of the Lake Conway, Florida, System. Second Annual Report to the Waterways Experimental Station. U.S. Army Corps of Engi neers. Tech. Report A-78-2 Vol. III. Blancher, E. C. and C. R. Fellows. 1978. Nitrogen and Phosphorus Loading Characteristics of the Lake Conway, Flor ida, Ecosystem. Preliminary Repor t to the Waterways Experiment Station, U. S. Army Corps of Engineers. Tech. Report A-78-2 Vol. IV. Fox, J.L., E. C. Blanc her and R. A. Conley . 1976. Biological Ba seline Studies of the Lake Conway F lorida System . Inte rim Report to Wate rways Experiment Station. U. S. Army Corps of Engineers, Vicksburg, MS. Misc. Paper 1-77-33. Blancher, E. C. 1974. Abundance, Diversity and Distribution of Fishes in a Louisiana Estuary. ASB Bulletin 21(2):209 (Abstract).
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