DISTRICT OF MUSKOKA RECREATIONAL WATER QUALITY MODEL REVIEW TOWNSHIP OF MUSKOKA LAKES JUNE 16, 2016

Transcription

1 DISTRICT OF MUSKOKA RECREATIONAL WATER QUALITY MODEL REVIEW TOWNSHIP OF MUSKOKA LAKES JUNE 16, 2016

2 RECREATIONAL WATER QUALITY MODEL REVIEW PRESENTATION FLOW INTRODUCTION AND CONTEXT MODEL REVIEW: KEY FINDINGS PLANNING IMPLICATIONS NEXT STEPS DISCUSSION AND QUESTIONS

3 THE PROJECT: RECREATIONAL WATER QUALITY MODEL REVIEW

4 KEY FINDINGS 1. MODEL IS NOT SUPPORTED ON AN INDIVIDUAL LAKE BASIS 2. ALL LAKES WARRANT PROTECTION, WITH MULTIPLE STRESSORS CONSIDERED 3. MOVE TOWARD RELIANCE ON MEASURED CHANGES AND OBSERVED WATER QUALITY - PHOSPHORUS IS STILL A GOOD INDICATOR 4. SEVEN TRANSITIONAL LAKES WARRANT ADDITIONAL STUDY Phosphorus is the window into our lakes

5 RECREATIONAL WATER QUALITY MODEL REVIEW PLANNING IMPLICATIONS 5

6 MUSKOKA WATER QUALITY POLICIES Current Policy Approach Lake Classification Policy Requirement Implementation Over Threshold Lot Level Study High Sensitivity Lot Level Study Moderate Sensitivity Site Plan Required Low Sensitivity Site Plan Recommended SITE PLAN Proposed Policy Direction Categories Standard Protection Transitional Lakes (7) Implementation Site Plan Required Enhanced Protection Lake Level Study Simplified and consistent Leading-edge and defensible Based on observable indicators 20% of lakes impacted BMPs Vegetated Buffers Shoreline Naturalization Soil Protection On Site Stormwater Control Limit Impervious Surfaces Enhanced Septic Setback (30m) Enhanced Lot Size Securities and Compliance Monitoring Standard X X X X X X X X

7 INTERIM COMMENTING EXISTING POLICIES ARE OPEN TO CHALLENGE ALSO CHALLENGES WITH ADOPTING A FORMAL INTERIM APPROACH INTERIM COMMENTS ON DEVELOPMENT APPLICATIONS NEED TO: PROTECT TRANSITIONAL LAKES BE REASONABLE AND DEFENSIBLE REQUIREMENTS FOR ALL OTHER LAKES CONSIDER SITE SPECIFIC CIRCUMSTANCES IMPORTANCE OF PRE-CONSULTATION 7

8 RECREATIONAL WATER QUALITY MODEL REVIEW NEXT STEPS 8

9 PROCESS OVERVIEW We are here Possible appeal Background and Interim Approach Draft Policies Public Consultation Final Policies Adoption by Council and Approval by MMAH Implementation Consultation with: Area Municipalities Lake Associations MOECC Muskoka Watershed Council Community Groups Public Approximately One Year

10 CLOSING THOUGHTS With Change Evolving story, science is advancing All lakes deserve protection Some change but not drastic 20% of lakes may be affected Comes Opportunity Muskoka as a leader Aligns with vision to protect a valued asset Stronger partnerships Improved efficiency and all lakes protected

11 DISCUSSION AND QUESTIONS WHAT DOES THIS MEAN FOR MUSKOKA LAKES? 11

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13 TO: FROM: Chair and Members Planning and Economic Development Committee Christy Doyle Director of Environmental and Watershed Programs and Summer Valentine Director of Planning DATE: April 21, 2016 SUBJECT: District of Muskoka s Recreational Water Quality Model Review REPORT NO: PED RECOMMENDATION THAT Hutchinson Environmental Sciences Limited (HESL) s Revised Water Quality Model and Lake System Health Program Final Report (April 2016), BE ACCEPTED; AND THAT the Official Plan Amendment process to address the recommendations found within HESL s aforementioned Final Report BE INITIATED; AND THAT an Interim Approach as outlined in Report No. PED BE IMPLEMENTED effective immediately; AND THAT Area Municipalities BE REQUESTED to implement this Interim Approach. ORIGIN The District of Muskoka undertakes a comprehensive review of its Recreational Water Quality Model every ten years to update the science, confirm the calibration of the model, review policy implementation, and address emerging water quality issues. In January 2016, Hutchinson Environmental Sciences Limited (HESL) submitted its draft final technical report on the District s Recreational Water Quality Model. Staff reviewed the District s existing water quality policy in consideration of HESL s findings. HESL s Final Report was submitted in April Page 1

14 ANALYSIS Background and Context Water is an essential part of Muskoka s natural environment and a critical resource, especially for people who live, work and play within the District of Muskoka s watershed. In the face of the changing climate, and the pressures of growth and development, continued strong action is needed to protect, maintain and, where possible, enhance the health of Muskoka s watershed for the present and future generations. In Muskoka, water quality is managed across all levels of government and with the assistance of many interested community organizations and individuals. Several departments within the District of Muskoka, together with its partner agencies including the Ministry of Environment and Climate Change (MOECC), the Ministry of Natural Resources and Forestry (MNRF), the Area Municipalities, Muskoka Watershed Council and various community organizations, and individuals, all have a role to play in ensuring that Muskoka s waters remain clean and healthy. The District is widely recognized as a leader in the protection of recreational water quality. For decades, District Council has maintained its lake water quality monitoring program and implemented associated leading edge policy into its Official Plans for the protection of Muskoka s watershed. Through these efforts, District Council has recognized the importance of water quality to Muskoka s residents and visitors, and the importance of water-based recreation to the overall economy. District Council s leadership includes the ongoing use of a Recreational Water Quality Model and the District has a history of generally managing the health of the lakes in Muskoka for nutrient input, particularly phosphorus. In January 2003, Muskoka District Council approved the Muskoka Water Strategy. The Water Strategy is a framework of integrated strategic initiatives to protect Muskoka s water. In 2005, Council adopted the Lake System Health Program, which is intended to guide and minimize the impact of human development on water resources, preserve the environmental health and quality of life in Muskoka and also protect the future of Muskoka as a premier recreational region. The Recreational Water Quality Model, associated planning policies, and monitoring of Muskoka s recreational water quality are key components of the Lake System Health Program. From a planning perspective, the past decade or so has been an important one for managing shoreline development in Muskoka. Many local practices, including Lake of Bays development permitting system and generally strengthened shoreline land-use planning policies in all Area Municipalities, have contributed to stabilized phosphorus levels across Muskoka s lakes, which, in turn, is a testament that planning policy is protecting recreational water quality. However, in recognition of evolving science and changing behaviours, Council has periodically reviewed and strengthened its Recreational Water Quality Model and its associated policies and approaches to include a more holistic approach to watershed and lake management Review: District Recreational Water Quality Model HESL was retained by the District in 2010 to review and update its Recreational Water Quality Model. While the project and HESL s Final Report, Revised Water Quality Model and Lake System Health Program (April 2016) has been completed within the District s approved project budget, Page 2

15 considerable time has been taken to ensure that the best available science, significant analysis, and associated implementation techniques can be addressed through this review. In its Final Report, HESL finds that planning policy based on the Recreational Water Quality Model and with a narrow focus on phosphorus is no longer warranted for several reasons, including: the accuracy of the existing model, with results not effectively predicting measured phosphorus levels on a lake specific basis; the evidence that phosphorus concentrations are not increasing in any of Muskoka s lakes; recent Ontario Municipal Board decisions favouring phosphorus abatement technologies for septic systems; and the emergence of multiple environmental stressors that also significantly impact lake health. Based on these conclusions, HESL recommends that: 1. All lakes be afforded a high degree of protection by a requirement for a minimum set of Standard Best Management Practices (BMPs) for all new development or redevelopment of shoreline lots. 2. The monitoring records for all lakes be reviewed annually and results compared against the following three management flags : a. total Phosphorus (TP) being greater than 20 micrograms/litre; b. an increasing trend in TP; and/or c. documented presence of a blue-green algal bloom. 3. Those lakes flagged by these above-noted factors are referred to as Transitional Lakes and should be subject to: a. enhanced BMPs for new development or redevelopment of shoreline lots as a precaution against phosphorus loading; b. a detailed causation study to determine the role of shoreline development as a precaution against phosphorus loading, which would include targeted use of the District Water Quality Model but with detailed review of input data, land use patterns and hydrology in the immediate watershed and settlement history, implementation of the District s Limits to Growth assessment, assessment of Dissolved Organic Carbon and its role in phosphorus enrichment and remedial actions if warranted; and c. a pause on new lot creation and development of a Remedial Plan if the causation study determined that human phosphorus loading was the likely cause of the management flag(s). Across the District of Muskoka, the HESL Final Report confirms that there are presently seven lakes affected by the proposed management flags, including Ada (ML), Brandy (ML), Bruce (ML), Three Mile (ML), Stewart (GB), Barron s (GB), and Bass (GR). Page 3

16 Existing Policy District planning policy is currently based on the 2005 Recreational Water Quality Model which identifies three lake sensitivity classifications and the overlay status of Over Threshold, which can apply to any sensitivity classification when a lake exceeds the model s defined phosphorus concentration threshold of background levels plus 50%. When development is proposed, each lake classification is subject to a tiered policy approach depending on the sensitivity level and threshold status of the lake: 1. Low Sensitivity lakes are subject to the general water quality policies and the application of site plan control or development permitting is recommended. 2. Moderate Sensitivity lakes are subject to the general water quality policies and application of site plan control or a development permitting is required to address phosphorus management. 3. High Sensitivity lakes are subject to the general water quality policies and lot creation or development of vacant lots can only proceed with a lot-level Water Quality Impact Assessment (WQIA) which demonstrates that development can occur without impairing water quality and identifies a range of phosphorus management techniques to mitigate any potential impacts. 4. Over Threshold lakes are subject to the same requirements as High Sensitivity lakes, but a higher standard of implementation of WQIAs is required before development can proceed, including a site-specific Official Plan Amendment and application of site alteration and tree-cutting by-laws or a development permit by-law. While this policy approach has served the District well in protecting water quality (as is evidenced by the stabilization of phosphorus levels across the watershed and Muskoka s overall excellent water quality), the HESL report indicates that the approach is no longer scientifically defensible. In addition, the past decade has brought significant changes across the watershed. Muskoka s lakes are changing, and so is our understanding of lake dynamics and multiple environmental stressors, including declining concentrations of calcium, invasive species, and climate change. The chemical, physical and biological conditions of lakes are responding to these multiple stressors. Though phosphorus is still the best indicator of lake health, its presence tells only a partial story which should be considered amidst a broader picture of lake health. The District s existing policy approach is also complex and has resulted in implementation challenges. The WQIAs, without exception, have indicated that development is possible without negatively impacting water quality. The recommendations of these studies are also remarkably consistent and indicate that implementation of phosphorus management and mitigation techniques through site plan control or development permitting is the best way to protect water quality (i.e. vegetative buffers, building setbacks, stormwater management, and construction mitigation techniques). Therefore, depending on the policy set being applied, some proponents currently proceed directly to site plan control or development permitting while others spend several months on a study and comprehensive planning process, and at significant cost to reach the same conclusion. Page 4

17 Proposed Policy Direction The development of a consistent, simplified, precautionary, and scientifically defensible approach to planning policy on all lakes appears to be warranted. HESL s Final Report indicates that this approach should be based on observed and measured water quality rather than a model, with phosphorus as an indicator but not the sole focus of the policies, and based on the tenet that all lakes deserve protection against multiple environmental stressors. New Official Plan policies will be required to implement the revised approach and will be drafted based on the following two principles: 1. Application of a standard set of best management practices through site plan control or development permitting on most lakes to implement phosphorus mitigation and management techniques, which are the same actions that best position a lake to be resilient to other stressors; and 2. On the seven transitional lakes where a management flag has been identified, require that a lake-level causation study should be undertaken by the District in order to understand the cause of the management flag(s). Until such a study is completed, enhanced best management practices should be applied. If it is determined through a causation study that shoreline development is a significant contributor to the identified management flag(s), a pause on lot creation and/or additional development may be recommended pending the creation of a remedial action plan. In reviewing this proposed policy direction, it is important to remember that although it may appear quite different from the existing policies, from an implementation perspective, there is essentially no change for the vast majority (80%) of Muskoka s lakes and this approach focuses resources on outcomes through site planning rather than on studies that all drew the same conclusions. In addition, there are many other policies that limit the amount of development permitted around Muskoka s lakes, including those addressing minimum lot sizes, environmental constraints (e.g. wetlands, species at risk habitat, significant wildlife habitat, etc.), avoidance of hazard lands (e.g. steep slopes, floodplains, etc.), and character (e.g. narrow waterbodies, low density development, lake plans, etc.). Interim Policy Approach While staff recommends that HESL s work be adopted and that an Official Plan Amendment process be initiated, given HESL s findings, the District s existing policies may be open to challenge in the meantime. To address this period between the release of HESL s Final Report and the eventual adoption of an Official Plan Amendment, an interim approach is recommended to ensure that all lakes are protected and that time and money are not spent unnecessarily by proponents. Staff evaluated a variety of interim approaches, all of which have benefits and challenges. In order to ensure that lakes are appropriately protected, to implement a fair and consistent approach to development applications, and to minimize the risk to the District, the following approach is recommended to be applied for a period of one year: On Transitional lakes, (7) WQIAs should be required to support lot creation and development of vacant lots, with site plan control or development permits required for redevelopment (i.e. applying the District s existing High sensitivity policy approach). The remainder of Muskoka s lakes should be protected by implementing standard best management practices through site plan control or development permits for lot creation, Page 5

18 development of vacant lots, and redevelopment (i.e. the District s existing policy Moderate sensitivity approach). The HESL Final Report would substitute for the WQIA on lakes where the existing policy set would require such a study. In recognition that lake system health policies are present in all of the Area Municipal Official Plans and to ensure consistency across all municipalities in the District during this interim period, staff are recommending that Area Municipalities be requested to pass similar resolutions to implement the interim approach as well. During the one year period in which the interim approach would be in effect, staff would undertake the Official Plan Amendment process required to develop policies that appropriately address HESL s recommendations, based on consultation with the Province, the Area Municipalities, the Lake Associations, Muskoka Watershed Council, and the community. Conclusion The proposed staff recommendations respond to HESL s Final Report and are intended to simplify, streamline and update Muskoka s planning processes while affording a high level of protection to all lakes in Muskoka. The results of HESL s Final Report indicate that change in our approach to water quality protection through planning policy is warranted by evolving science. While such change is necessary, it does not represent a major departure from existing policy outcomes and only 20% of lakes are potentially impacted. With this change, there is an opportunity for Council to: continue to be a leader in protecting, improving and restoring the elements that contribute to the ecological health and the recreational water quality of Muskoka s lakes; protect what was identified as being valued by all Muskokans through the visioning initiative; consider a wider range of stresses on Muskoka s lakes, including climate change; continue to address water quality in a precautionary manner; ensure that while all lakes are worthy of policy protection, certain lakes may require additional environmental examination and potentially additional protection; use the District s exemplary water quality monitoring program as the scientifically defensible basis for a planning policy approach; and simplify and streamline existing policy. FINANCIAL CONSIDERATIONS The proposed interim approach is not likely to have any cost implications. Costs associated with an Official Plan Amendment are accounted for in the 2016 Tax Supported Operating Budget and Capital Budget and Forecast and are expected to be sufficient to undertake the OPA. Existing funds are within the Official Plan Project Category As part of the Official Plan Amendment, staff will likely be recommending changes to our longterm Water Quality Model and Monitoring Program and a report with any budget implications will be brought for consideration prior to the 2017 budget process. On-going discussions with Area Municipalities will be required to determine any budget implications at the local level. Page 6

19 COMMUNICATIONS Lake Associations, Area Municipalities, the Province, Muskoka Watershed Council, and other stakeholders will be consulted in the preparation of the proposed Official Plan Amendment as required by the Planning Act. Where appropriate, additional public meetings or open houses, and attendance at association meetings will be considered in order to provide information and receive input on policy proposals. A press release will be prepared and released to the local media simultaneously with the next presentation to Planning and Economic Development Committee. Staff reports, presentations, and general information will be available through the District of Muskoka Website. STRATEGIC PRIORITIES The District of Muskoka s review and update of its Recreational Water Quality Model and water quality policy will assist in achieving the following Strategic Goals: 1. Manage development and growth in a sustainable manner balancing environmental, economic, social and cultural elements. Recognize that in Muskoka a healthy and vibrant economy depends on wise stewardship of the environment. Build on the cultural heritage of Muskoka and demonstrate municipal leadership in environmentally sustainable policies, programs and practices. 9. Work with all order of government, particularly the Area Municipalities, and the people of Muskoka to achieve these goals. Actively advocate for Muskoka with senior levels of government for programs and policies that will assist Muskoka to reach these goals. Respectfully submitted, Original signed by Christy Doyle, BA, MES (Pl), MCIP, RPP Director of Environmental and Watershed Programs Original signed by Samantha Hastings, MCIP, RPP Commissioner of Planning and Economic Development Original signed by Summer Valentine BSc, MPL, MCIP, RPP Director of Planning S:\WATER\WQMR\DMM Project - OPA 2016\PED Recreational Water Quality Model Review-Final.Doc Page 7

20 Hutchinson Environmental Sciences Ltd. Revised Water Quality Model and Lake System Health Program Prepared for: District Municipality of Muskoka Job #: J April 2016 Final Report

21 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m April 5, 2016 HESL Job #: J Ms. Christy Doyle Director of Watershed Programs District Municipality of Muskoka 70 Pine St. Bracebridge ON P1L 1N3 Dear Ms. Doyle: Re: Revised Water Quality Model and Lake System Health Program Final Report We are pleased to submit this final report of the Revised Water Quality Model and Lake System Health Program for the District Municipality for Muskoka lakes. This has been a most challenging and scientifically interesting project, and we thank The District of Muskoka for their continued support over the course of the last several years while we worked to develop recommendations to revise the program to reflect the results of review in 2013, and again in 2015 to change the program emphasis. We appreciate that there may still be discussions required to move the technical recommendations presented herein into planning policy and look forward to the opportunity to assist with that. Sincerely, per: Neil J. Hutchinson, Ph.D. President Neil.hutchinson@environmentalsciences.ca

22 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m H e a l t h P ro g ra m Signatures Dörte Köster, Ph.D. Senior Aquatic Scientist Tammy Karst-Riddoch, Ph.D. Senior Aquatic Scientist Brent Parsons, M.Sc. Aquatic Scientist

23 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Acronyms 1 m off the lake bottom 1 mob Background Plus 50% BG+50% Dissolved Organic Carbon DOC Dissolved Oxygen DO District Municipality of Muskoka DMM Dorset Environmental Science Centre DESC Geographic Information System GIS Georgian Bay Forever GBF HESL Lake of Bays Association LOBA Lake Partner Program LPP Lake System Health LSH Lakeshore Capacity Model LCM Muskoka Lakes Association MLA Muskoka Water Quality Model MWQM Ontario Base Map OBM Principal Components Analysis PCA Provincial Water Quality Objective PWQO Total Phosphorus TP Wastewater Treatment Plant WWTP Natural Heritage Review NHR

24 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Executive Summary Background The District Municipality of Muskoka (DMM) uses their Water Quality Model (MWQM), a variant of MOE s Lakecap Model (2010), as one component of the Lake System Health program to guide planning policies for recreational lake development in a large and complex watershed of over 500 lakes and lake segments. The MOECC released their Lakecap Model and guidance document in 2010 as their recommended means of Lakeshore Capacity Planning. Prior to 2010, the MOECC encouraged use of this approach, although it had not yet been finaiized as formal Provincial guidance. In 2010, the DMM began a project to review and update the model to address changes in the Provincial approach and scientific background to the model since the last update was completed in The Lakecap approach is based on modelling the current phosphorus concentrations in a lake resulting from natural (or background) sources and human inputs and then calculating the amount of phosphorus from human inputs (generally shoreline development) that the lake can sustain while remaining below a modelled phosphorus concentration of Background + 50% (the lake capacity ). The MOECC approach requires that the model produce accurate estimates of phosphorus concentration that can be verified through a reliable lake monitoring program, such as that of the DMM. Although the MOECC model was developed and calibrated on a set of small headwater lakes in Muskoka and Haliburton, the MOECC advise that the model should be used in a watershed context that is, any lake that is being modelled should incorporate hydrologic and phosphorus loading for all upstream lakes in its watershed (p. 29, MOE 2010). The Muskoka application of the model is thus complex, as it includes over 500 lakes and lake segments in the Muskoka, Black and Severn River watersheds. The Muskoka application also includes lakes and watersheds that exceed the calibration range used to develop the MOE model, as it did in the previous versions. The MOECC recognizes this in their guidance document and caution that modelling lakes that fall outside of the calibration range may be one reason that the model does not perform well. Results of 2005 Review The model was last updated in 2005 by Gartner Lee Ltd 1. At that time, we recognized that not all aspects of Provincial guidance were defensible by the science, especially those aspects which advised that shoreline development could be managed by enforcing lakeshore capacities as a specific number of lots on a given lake. In order to do this, the model would have to provide accurate and defensible results for setting specific lot development capacities 2. We concluded that the model could not set defensible lot development capacities and the DMM implemented the Lake System Health program as a result. Lake 1 The senior author of the 2005 Gartner Lee report was Dr. Neil Hutchinson, and he lead the revision project at 2 The model is implemented by calculating that a lake can sustain, for example, the phosphorus loading from 128 seasonal residences and maintain phosphorus concentrations below the Provincial standard of Background+50%. Thus, the capacity of the lake is 128 lots and the Province advises that any development beyond 128 lots be refused in OP Policy. Our review concluded that modelled phosphorus concentrations often differed from measured values. The Province advises that the modelled phosphorus concentration should be accurate to within 20% of the measured value. The revised model, on average, overestimated phosphorus concentrations by 38%, and underestimated them by 23%. Error exceeded 40% in 81 of the 206 lakes monitored by DMM. This error means that one cannot defend a capacity estimate as fine as 128 lots for use in Policy.

25 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m System Health included planning policies and lake classifications that were based on lakeshore capacity calculations but also considered a) the ability of the model to predict phosphorus concentrations in lakes and b) lake sensitivity to additional development, when classifying lakes. The 2005 Lake System Health Program was therefore implemented as a modification of the previous DMM approach. Primary modifications included: Only one planning category ( Over Threshold ) was based on modelled phosphorus concentrations and capacity calculations. The remaining planning categories ( Low, Moderate and High Sensitivity) used the model to determine lake sensitivity 3 to phosphorus loads but did not set lakeshore capacity limits based on modelled phosphorus concentrations. Instead, the resultant policies addressed the means to manage future development by implementing a series of increasingly stringent study requirements through Water Quality Impact Assessments and Best Management Practices to protect water quality in accordance with lake sensitivity. Results of 2013 Review The most recent review began in HESL revised the 2005 version of the MWQM to incorporate the most recent MOECC guidance (MOE 2010, Paterson et al. 2006). These revisions included: Revised atmospheric loading coefficients for phosphorus, Revised wetland phosphorus export equation for phosphorus, Incorporation of smaller lakes (8ha and greater) in the model, Refined GIS mapping of lake areas, watershed areas and wetland areas by DMM staff, Updated estimates of existing shoreline development (including developed and vacant lots) from DMM records, Removal of the model factors that accounted for attenuation of septic system phosphorus (soil classification and staged attenuation of septic system phosphorus in 100m increments from the lakeshore to 300m) at the request of the MOECC, and Comparison of model output against the most recent 10 year record of total phosphorus measurements made in DMM lakes by the DMM. After extensive testing and analysis of the revised model we once again concluded that the modelled estimates of phosphorus concentrations in lakes were not reliable enough to set and defend specific lakeshore capacities as numbers of cottage or residential lots, as intended by the MOECC. Similar concerns were expressed by MOECC scientists, based on their recent experience, when we presented our findings to them in a meeting with DMM in January of Lake sensitivity was defined for each lake based on its relative change in phosphorus concentration to a standard load of phosphorus (i.e. responsiveness ) and the potential for phosphorus from shoreline development to reach the lake (i.e., mobility ). 4 Although MOECC continues to recommend their 2010 Lakecap process they also recognize some of its weaknesses and are reconsidering their approach to managing shoreline development. In 2014, MOECC awarded HESL a contract to complete a scan of fourteen jurisdictions located in Canada and the USA to identify and describe alternative technical and planning approaches to the management of shoreline development in order to guide future initiatives in the Province of Ontario. The MOECC are currently considering the results of this review.

26 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m In 2013, HESL presented their draft report to the DMM. The report recommended maintaining the 2005 approach to classify lakes according to their threshold and sensitivity to phosphorus loading but with some modifications based on the revised model, an improved understanding of model limitations and discussions with DMM planning staff. That approach, however, still relied on assumptions that may not accurately reflect processes in Muskoka lakes, or that could change over time in response to a new scientific understanding, changing climate or changing MOECC guidance. While changes in science or assumptions could be technically valid, any changes, the known errors in model predictions and implications to planning policy could reduce public confidence in the Lake System Health program. Moreover, the approach was still focussed only on phosphorus and so did not address other threats to the lakes. In addition, the emergence and testing of phosphorus abatement technologies for septic systems since 2010 resulted in OMB decisions favoring development beyond the Lakecap limits in several cases, such that the potential for OMB challenges, and resultant costs for the DMM, warranted reconsideration of those aspects of Lake System Health and District policy that were based on the water quality model. The 2013 draft report was not, therefore finalized, further analysis undertaken and additional discussions held with DMM planning staff. Results of 2015 Review The DMM has developed and implemented an excellent program of water quality monitoring that obtains high quality data on phosphorus concentrations, dissolved oxygen status and water clarity for ~190 lakes or lake segments; and contributes data on major ion and DOC concentrations to the MOECC database. Analysis of the DMM phosphorus record from 190 lakes for the period from showed that phosphorus was not increasing significantly in any lakes but that three lakes showed a statistically significant decline. Muskoka s lakes are changing and are threatened by a variety of stressors in addition to shoreline development (Palmer et al. 2011). The recent Canada Water Network Research Program in the Muskoka watershed, for example, concluded that the multiple stressors included: increasing concentrations of dissolved organic carbon and chloride, declining concentrations of calcium, invading species populating an increasing number of lakes and the changing climate with resultant changes in precipitation, temperature, runoff and evaporation that affect physical, chemical and biological conditions of lakes. 5 Recent research by the MOECC (Winter et al. 2011) showed increasing reports of nuisance algal blooms across Ontario, a possible response to changing climate. It is clear that planning policy that is focussed solely on phosphorus sources is not warranted by the accuracy of the model, the evidence that phosphorus concentrations are not increasing significantly in any lakes, the emerging support of Best Management Practices for control of phosphorus at the OMB and the other stressors acting in Muskoka s lakes. Given the issues with model inaccuracies, changes in scientific understanding and potential effects of multiple stressors, a new, holistic approach is recommended for the Lake System Health program that: 5

27 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m 1. Eliminates the classification of lakes based on modelled estimates of phosphorus concentration in recognition of the uncertainty that the modelling approach adds to the planning process, 2. Provides increasing focus in District planning policies on the excellent water quality monitoring program that has been in place for 15 years, and 3. Recognizes Best Management Practices and development standards that can effectively mitigate the impacts of shoreline development and which may address a host of other environmental concerns. Recommended Approach We therefore recommend that the Lake System Health program be based on: 1. A higher minimum standard of protection and Best Management Practices for new development and redevelopment on all lakes, 2. Use of the District monitoring program to track phosphorus on DMM lakes and classify them according to measured changes and observed quality, and 3. Implementation of enhanced planning requirements and Best Management Practices for individual lakes based on observed water quality concerns or triggers based on the District s monitoring program. These would include implementation of causation studies on individual lakes and focussed use of the existing model 6 in response to the monitoring triggers. Lake Planning and Management Triggers The intent of the Lake System Health Program is to manage human activities that contribute phosphorus to DMM lakes. The DMM water quality monitoring program collects data that can be used to assess lake status and there is high confidence in these data. The data that are routinely collected on Muskoka s lakes can be used to inform the following triggers of lake sensitivity: Phosphorus concentrations exceeding 20 µg/l based on the most recent 10-yr average phosphorus concentrations measured in the DMM monitoring program. (The 20 µg/l trigger is MOECC s interim PWQO for total phosphorus to protect against algal blooms and the maximum allowable concentration allowed under Lakecap ), A statistically significant increasing trend in phosphorus concentration, based on evaluation of the phosphorus concentration record measured in the DMM monitoring program since 2001, and Occurrence of bluegreen algal (cyanobacterial) blooms as documented by public complaints to the MOECC or the Simcoe-Muskoka District Health Unit. 6 The model has been implemented as a screening tool in which a consistent approach is applied to all 500+ lakes that are modelled. This approach does not allow detailed examination of lake specific factors that might affect model accuracysuch as confirmation of the numbers of residences and their usage factors, confirmation of soil types and depths that may alter phosphorus dynamics in the watershed or hydrologic alterations induced by road building or beaver dams that could alter phosphorus dynamics. A causation study would include detailed and lake specific evaluations of these factors to see if any changes in water quality (or triggers ) could have been related to shoreline development. This is explained further in Section 8.2 of this report.

28 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Each of these triggers merit investigation to determine if they indicate a response to human shoreline development and any associated phosphorus loads, to natural factors or to other factors such as climate change. The outcome of the investigation will determine the need for enhanced protection through management action or policy intervention. We therefore recommend an approach based on the use of reliable lake monitoring data as triggers for additional study and, if required, a management and planning response. We recommend that water quality results be subject to a quality control check each year, added to the DMM s long term record of water quality and reviewed each year against the proposed triggers. For triggered lakes: Management recommendations such as Enhanced Best Management Practices (BMPs) would be implemented to protect water quality. These would be elaborated in, and drawn from, a schedule in the District Official Plan. Implementation would be a) encouraged for all existing development through a stewardship program and b) required for any development or redevelopment. A Causation Study would be required to examine possible reasons why the water quality was triggered and the role of shoreline development or other human factors. If the causation study concluded that shoreline development was responsible for, or a significant contributor to, the observed trigger then DMM Policy could limit further development or require a formal Remedial Action Plan. Best Management Practices Best Management Practices (BMPs) recognize that the manner in which a shoreline is developed can have as great an effect on water quality as the amount of shoreline development. They include a variety of stewardship and engineering practices designed to reduce the surface runoff of storm water and associated erosion and contaminant transport, maintain natural vegetation to reduce runoff, stabilize soils, provide habitat, intercept nutrients and provide a social screen between adjacent land owners or to retain phosphorus from septic systems through incorporation of mineral rich soils or implementation of engineered nutrient abatement technology. The 2005 Lake System Health Program promoted the use of increasingly strict BMPs with increasing lake sensitivity and required the completion of Water Quality Impact Assessments to maximize the use of BMPs for development on sensitive lakes. The promotion of BMPs through stewardship and educational programs has been a focus of Muskoka s water quality program since before the Lake System Health Program was formalized in 2005 and remains an important component of lake management in Muskoka ( We recommend that a basic set of BMPs be adopted and enforced for development and redevelopment of all lakes in Muskoka as a precautionary approach. This recognizes that the importance of BMPs extends beyond mitigation of septic system phosphorus and provides benefits to all lakes, not just those which are nutrient sensitive. We recommend that a suite of stricter Enhanced BMPs be adopted and enforced on any lake in which a water quality trigger has been met in recognition that lakes in which total phosphorus concentrations exceed 20 µg/l or are increasing, or in which a cyanobacterial bloom has been documented may be particularly sensitive to development. A potential list of BMPs is provided below. Some (such as

29 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m enhanced lot size or septic system setback) are already in place in DMM policy. Others would need to be fully described prior to implementation. Proposed BMPs for Standard and Enhanced Lake Classifications. Standard Enhanced Vegetated Buffers X X Shoreline Naturalization X X Soil Protection X X On-Site Stormwater Control X X Limit Impervious Surfaces X X Enhanced Septic Setback (30m) X X Enhanced Lot Size X X Securities and Compliance Monitoring X X Increased Monitoring Intensity X Site-Specific Soils Investigation X Septic Abatement Technologies OR// Full Servicing Slope Dependent Setback X X Enhanced Building Setback X Limit Lot Creation X Remedial Action Plan X Causation Studies Previous versions of the Lake System Health Program worked on the premise that increases in phosphorus concentration beyond the modelled estimate of Background 50% were related only to shoreline development and that lakes which the model showed to be sensitive to phosphorus loading should be managed to prevent increased phosphorus loading from shoreline development. The proposed changes acknowledge the problems with model accuracy, the potential for other causes of changed water quality and recognize the merits of a high quality record of water quality as determined through the DMM monitoring program as a more reliable trigger for management or planning action. Planning and management responses must, however, be based on an understanding of the factors that caused a) phosphorus concentrations to exceed 20 µg/l, b) phosphorus concentrations to increase in a trend or c) a cyanobacterial bloom. Causation studies are therefore recommended for triggered lakes to a) examine the cause of the trigger, b) examine the role of shoreline development in the observed trigger and c) develop the appropriate management response. These could include any or all of the following investigations: Detailed review of water quality monitoring data (e.g. Secchi depth, DO and DOC measurements),

30 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Collection of additional water quality data through the DMM monitoring program (e.g. hypolimnetic samples to assess internal load), Detailed and lake specific application of the Muskoka Water Quality Model to consider detailed counts of shoreline development and usage (seasonal vs permanent), land use in the watershed and catchment soil types and depth to assess phosphorus attenuation in the soil, Site specific investigations of hydrology and inflows to assess any flooding in the catchment from road construction or beaver dams that may alter phosphorus dynamics, A septic system inspection program, A survey of shoreline disturbance (i.e. presence of lawns and budgets) A Limits to Growth assessment based on the present shoreline characteristics (see to determine any factors limiting shoreline development and the feasibility of additional shoreline development or redevelopment, which would help determine the need for and nature of a planning response or implementation of Enhanced BMPs. Causation Studies will be developed on any lakes triggered by the three criteria to evaluate the reasons they were triggered and determine the need for, and type of, any lake-specific management responses. Causation Studies can include many of the investigations listed above but need not include all of them. This report describes examples of the potential scope of work for three different Causation Studies to provide an idea of what type of information would be required to inform appropriate lake management in response to the various triggers, and associated costs with collecting and interpreting the required information. Scopes of work were developed for lakes that would be triggered under each of the proposed trigger criteria (i.e. TP > 20 µg/l, increasing trend in TP, or documented blue-green algal blooms. Many of the analyses required for the Causation Studies could be done using existing monitoring data, reviewing some aspects of the Muskoka Water Quality Model or by collecting additional samples through the DMM lake monitoring program. Others would require more detailed investigations. Estimated costs range from $1000 to $10,000 depending on the required complexity. District of Muskoka Planning Implications Under the existing Lake System Health Program, proponents of development or redevelopment are responsible for the costs associated with the required Water Quality Impact Assessments, as these are triggered by applications for development or redevelopment. The revisions proposed herein would see Causation Studies that were triggered by the DMM water quality monitoring data. The DMM would therefore undertake the Causation Studies and post the results along with the resultant requirements for development or redevelopment. We anticipate that only one Causation Study would be required for each lake - there would be no need to repeat the study if the lake remained triggered in subsequent years unless there was clear evidence that conditions had changed. One could anticipate the need for additional study, however, if a lake that had TP > 20 µg/l or an increasing trend in TP were to develop an algal bloom as well. The proposed revisions would also increase the need for enforcement of development and redevelopment conditions and standards and resultant costs. One cannot assume that water quality will be protected under the proposed planning controls and BMPs unless they are implemented and maintained as intended. We

31 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m would propose that a position of Environmental Compliance Inspector at either the District or the local government level would be required for enforcement, and that fees for non-compliance, or breach of conditions be sufficient to assure encourage compliance. Proponents of development or redevelopment would be responsible for the costs associated with implementation of standard or enhanced BMPs. Development and redevelopment on lakes which were not triggered would proceed under standard planning requirements using the Standard BMPs listed above to protect water quality, Development and redevelopment on lakes which were triggered would proceed using the Enhanced BMPs listed below to protect water quality, Lakes which were triggered would also undergo a Causation Study to determine the need for additional development controls or management. The proposed process is summarized in the following flow chart. Proposed Revised Lake System Health Planning Approach. All DMM Lakes Standard BMPs for New Development and ReDevelopment Sample Lakes and Review Data Annually TP > 20 µg/l Increasing TP Trend Documented Blue-Green Algal Bloom No Yes Enhanced BMPs Causation Study Detailed Water Quality Sampling and Review Review Land Use, Lake History and Development Detailed Lake Model Limits to Growth Assessment Development Related TP as Cause? No Yes Limit Lot Creation Remedial Action Plan

32 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Summary In summary, we recommend that: 1. All lakes are afforded a high degree of protection by a requirement for a minimum set of Standard BMPs for all new development or redevelopment. These would be further elaborated in Schedules to the Official Plan. a. This would require assurance in the form of formal inspections and incentives or penalties for compliance or non-compliance with BMP implementation. 2. That the monitoring records for all lakes be reviewed annually and results compared against the three triggers of: Total Phosphorus > 20 µg/l, an increasing trend in total phosphorus or documented presence of a blue-green algal bloom. 3. That triggered lakes be subject to: a. Enhanced BMPs for new development or redevelopment as a precaution against phosphorus loading, b. A detailed Causation Study to determine the role of shoreline development on water quality. i. This would include use of the District Water Quality Model but with detailed review of input data, review of land use patterns in the immediate watershed, review of settlement history, implementation of the DMM Limits to Growth assessment, assessment of Dissolved Organic Carbon and its role in phosphorus enrichment. The resuts could lead to remedial actions if warranted. c. A freeze on new lot creation and development of a Remedial Plan if the causation study determined that human phosphorus loading is likely the cause of increased phosphorus concentrations and/or the occurrence of cyanobacteria blooms. This approach would simplify policy implementation, provide a consistent and verifiable public and planning framework of lake status, provide protection for all lakes and enhanced protection for sensitive lakes and be based on the DMM s excellent record of lake water quality.

33 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table of Contents Transmittal Letter Signatures Acronyms Preamble 1. Introduction Review of Watershed Characteristics and Export Coefficients Addition of New Watersheds Confirmation of Watershed, Lake and Wetland Areas Loadings from Wastewater Treatment Plants Loadings from Golf Courses and Urban Runoff Review of Lake Water Quality Dissolved Organic Carbon Total Phosphorus Data Quality Outlier Detection and Removal Ten-Year Mean Spring Turnover TP Total Phosphorus Trends Relationship between Total Phosphorus and Dissolved Organic Carbon Review of Oxygen and Phosphorus Dynamics Selection of Lakes for Identification of Internal Phosphorus Loading Selection process Scheduling Late Summer Hypolimnetic Phosphorus Results Relationships of Bathymetry with Oxygen Status Relationships of TP and DOC with Oxygen Status Ordination Analysis Multiple Regression Analysis Literature Review on Phosphorus Retention and Internal Load Summary Embayments Lakes Joseph, Rosseau, Muskoka and Lake of Bays Embayments Approach to Embayment Criteria Data Sources Quality Control of Data Main Basin vs. Embayment Comparisons Recommendations for Modeling Summary Georgian Bay Embayments Current Monitoring and Modeling District of Muskoka Monitoring Data Data Gaps and Recommendations... 49

34 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Other Monitoring Programs Conclusions and Recommendations Muskoka Water Quality Model Review and Update Model Results and Validation Potential Sources of Error Summary Development of a Planning Approach Rationale for a Revised Approach Management Triggers Integration, Conclusions and Recommendations Lake Planning and Management Triggers Causation Studies Examples of Causation Studies District of Muskoka Planning Implications Recommendations References List of Figures Figure 1. Distribution of the previous and updated watershed areas Figure 2. Distribution of differences between the previous and updated wetland areas Figure 3. Distribution of mean dissolved organic carbon in Muskoka lakes (n=195) Figure 4. Dissolved Organic Carbon in Muskoka Lakes from Figure 5. Changes in Dissolved Organic Carbon in DESC lakes from 1980s to Figure 6. Distribution of ten-year mean spring TP ( ) in Muskoka lakes (n=196) Figure 7. Relationship of total phosphorus and dissolved organic carbon in Muskoka lakes Figure 8. Lake area and depth relationship for Muskoka lakes. (n=188) Figure 9. Lake area and depth relationship for all lakes except three large lakes (Lakes Muskoka, Rosseau and Joseph) (n=185) Figure 10. Lake area as a function of depth for lakes with oxic and anoxic hypolimnia Figure 11. Lake area as a function of depth for small (<100 ha) lakes with oxic and anoxic hypolimnia Figure 12. Principal Component Analysis plot of morphometric and chemical characteristics for lakes that are more than 3 m deep Figure 13. Number of total phosphorus measurements at MLA open-water stations used for embayment analysis Figure 14. Relationship between MLA annual average TP ( ) and DMM average spring TP in main basins and embayments of lakes Joseph, Muskoka, and Rosseau. The dashed line is the 1:1 line (y=x) Figure 15. Relationship between average MLA and DMM spring TP concentrations ( ) in main basin and embayments of Lakes Joseph, Muskoka, and Rosseau Figure 16. Map of Median Total Phosphorus Concentrations at MLA Open Water Sampling Stations in Lakes Muskoka, Rosseau and Joseph ( ) Figure 17. TP concentrations in Lake Joseph and its embayments ( ; MLA data) Figure 18. TP concentrations in Lake Muskoka and its embayments ( ; MLA data)

35 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 19. TP concentrations in Lake Rosseau and its embayments ( ; MLA data) Figure 20. TP concentrations in Lake of Bays and its embayments ( ; LOBA data) Figure 21. Changes in Total Phosphorus from Spring to Summer at MLA Sampling Stations Figure 22. Accuracy of the MWQM model to predict phosphorus concentration (n=206 lakes). Dotted lines enclose +/-20% about the 1:1 line Figure 23. Model error compared to potential phosphorus load from development, all lakes Figure 24. Model error for lakes with <10% potential development phosphorus load (D.I <1.1; n=36) Figure 25. Comparison of 2005 and 2012 estimates of wetland areas for individual lakes in the Dwight subwatershed Figure 26. Model error as a function of wetland area Figure 27. Relationship of model error to DOC in Muskoka lakes Figure 28. Relationship of model error to areal water load Figure 29. Relationship of model error to ratio of watershed area/lake area Figure 30. Relationship of model error to lake maximum (top) and mean (bottom) depth Figure 31. Human phosphorus loading and relationship between TP and DOC in Muskoka Lakes Figure 32. Proposed Lake System Health Planning Approach List of Tables Table 1. Revised Subwatershed Delineations for Model Table 2. Annual Flows and Total Phosphorus Loads from Wastewater Treatment Plants in Muskoka ( )... 6 Table 3. List of Outlier TP Measurements Detected in DMM Dataset Table 4. Lakes With Significant Decreasing Trends (p<0.10) in Total Phosphorus: Table 5. Screening Level 1 Candidate Anoxic Lakes (n=95) Table 6. List of Lakes Removed From the Anoxia Sampling List and Rationale Table 7. Forty Lakes Recommended for 2011 Late Summer Bottom TP Sampling Table 8. Priority 1 Lakes, No Anoxia Table 9. Priority 2 and 3 Lakes, Substantial and Weak Anoxia Table 10. Hypolimnetic TP Measurements in Late Summer Table 11. Multiple Regression Statistics of Anoxia predicted by Depth, Area, DOC and TP Table 12. Comparison of Average TP Concentrations Collected by Both the DMM and MLA Table 13. Average Spring TP Concentrations (May-early June) Collected by the DMM and MLA Table 14. Comparison of Main Basin and Embayment TP and Physical Characteristics. Bold values are significant at p < Table 15. District of Muskoka TP Monitoring Data for Georgian Bay Embayments ( ) Table 16. Recommendations for Monitoring Inner and Outer Bay Sampling Locations Table 17. Georgian Bay Embayment Sites and Inland Lakes monitored by Georgian Bay Forever Table 18. Predictive Error of the MWQM (n=206 lakes) Table 19. Percentage Error of Phosphorus Concentrations in Lakes with Little Development Table 20. Relationship of model error to watershed position of lake Table 21. Relationship of Model Error to Hypolimnetic Oxygen Status Table 22. Model Components and Evaluation of Confidence Table 23. Proposed BMPs for Standard and Enhanced Lake Classifications

36 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Appendices Appendix A. Methodology for GIS exercise providing new watershed, lake and wetland areas Appendix B. List of Updated Lake, Watershed, and Wetland Areas Appendix C. Phosphorus Data used for Calculation of the Year Mean and 15 year ( ) Trend Assessments (Excl. Outliers) Appendix D. Correspondence and Meeting Minutes Appendix E Total Phosphorus Update Appendix F. List of MNR Lake Trout Lakes Within the District Municipality of Muskoka.

37 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m H e a l t h P ro g ra m 1. Introduction In the 1980s, The District Municipality of Muskoka (DMM) incorporated policies for the protection of recreational water quality into its Official Plan in recognition of the importance of water quality to Muskoka residents and visitors and the importance of water-based recreation to its economy. The protection of water quality remains important and, over the years, the DMM has periodically reviewed its policies and approaches. The DMM s recreational water quality program is based on three key and interlinked components: A watershed-based model of recreational water quality in over 500 lakes in the DMM that is based on the original model and approach of Dillon and Rigler (1975), as modified by the Ontario Ministry of the Environment s Lakeshore Capacity Study (Dillon et al 1986) and subsequent variants (Gartner Lee Ltd. 2005, Hutchinson 2001), A program of monitoring water quality in over 190 DMM lakes to track changes in water quality over time, to calibrate the water quality model and to inform residents, Official Plan policies that use the model and the monitoring results to guide the amount and nature of development on individual lakes. The approach is focussed on the management of lakes so that water quality is not degraded by the enrichment of phosphorus, an algal nutrient that can move to lakes from shoreline septic systems and urban runoff. This approach limits the amount of shoreline development to maintain phosphorus concentrations at acceptable levels in lakes and has hence been termed Lakeshore Capacity. A complete description and explanation of the approach, as applied in Muskoka, is provided in Gartner Lee Ltd. (2005), Paterson et al (2006) and Ontario (2010) and will not be repeated here. The recreational water quality model was first formulated in 1980 and was based on the scientific understanding of nutrient dynamics in local lakes advanced by the pioneering study and model of Dillon and Rigler (1975). Substantial review, modification and adaptation of the model to personal computers was undertaken in 1992 by LGL Limited and minor corrections adopted between then and In 1996, the need for a comprehensive reworking of the model was identified, to incorporate recent advances in scientific understanding, provide a full linkage between all lakes in the watershed and to move from chlorophyll a to total phosphorus as the capacity determinant in policy. The 1996 review process also recognized the need to calibrate the revised model using a set of current phosphorus measurements from lakes, as the original model was formulated in the absence of reliable phosphorus measurements for the lakes. The first revisions to the water quality model were completed by Freshwater Research and submitted to the DMM in May of 1998 (Freshwater Research, 1998). Significant improvements were made to the model at this point. These included the dynamic linking of lakes to allow a better watershed approach to management, updating of various coefficients and the gradation of phosphorus impact based on distance from the lake. Unfortunately, it was not possible at the time to improve the predictive accuracy of the model to a level acceptable for implementation. In 1999, the DMM retained Gartner Lee Ltd. to update their Lakeshore Capacity approach. Recent scientific advances were incorporated into the model and the results of recent monitoring used to develop a model of R _150074_MWQMLSH_final.docx 1

38 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m over 500 lakes within the DMM. Significant changes were recommended and adopted by the DMM as part of their Lake System Health Program (LSH) in 2005 (Gartner Lee Ltd. 2005). In 2010, the DMM retained (HESL) to undertake a review of the LSH program in response to changes in the science (Paterson et al. 2006), the finalization of MOE guidance as their Lakecap program (Ontario 2010) and as input to periodic updates to their Official Plan. The 2010 review was also intended to take advantage of a ten-year record of high resolution phosphorus measurements made in over 190 of the LSH lakes that was made possible by the DMM s ongoing commitment to the monitoring program, cooperation with scientists at the Dorset Environmental Science Centre (DESC) of the Ontario Ministry of the Environment and Climate Change (MOECC) and Trent University and improvements to field collection and analytical techniques (Clark et al. 2010). The review was also informed by the water quality monitoring program and questions raised by the Muskoka Lakes Association (MLA). It therefore included examination of larger lakes to determine if they were accurately represented when modelled as one water body, or if specific embayments needed to be managed separately. Finally, the model was revised to include lakes of 8 ha in size and larger (versus the former cutoff of 10 ha) to reflect the original Official Plan definition of waterfront development. A series of technical memos were provided to the DMM between 2010 and 2013 as part of the review process and a final draft of the results and recommendations submitted to the DMM in December of 2013 (HESL 2013). Between 2013 and 2015 discussions between HESL, the DMM and the MOECC concluded that further revisions to the approach were warranted to reflect the findings presented in the 2013 report. The 2013 report was therefore revised and a new approach to lake management was recommended, as described herein. This report provides the results of the review process but focusses on the changes made between 2013 and 2015 to derive its recommendations. It incorporates water quality monitoring data for total phosphorus and dissolved oxygen that were made between 2000 and 2014 with a qualifier that the results and conclusions should be updated with results from the 2015 monitoring program. Throughout the process, HESL scientists have worked closely with the DMM staff, and we are indebted to Judi Brouse, and later Christy Doyle, for their leadership and championing of the program, the Department of Economic Development and Long Range Planning for their support and to Rebecca Willison and Stuart Paul for their technical assistance. 2. Review of Watershed Characteristics and Export Coefficients 2.1 Addition of New Watersheds The Muskoka Water Quality Model (MWQM) was originally cast to include lakes of 10 ha or greater in surface area. While the model did include 46 lakes that are smaller than 10 ha in size, many other smaller lakes were not modeled individually but were included as part of the watershed of a larger lake (a parent watershed ). The revised model is intended to accommodate lakes of 8 ha or greater in size, which is the minimum lake size that results in a Waterfront designation for adjacent land in the 2010 version of the Official Plan of the DMM (DMM 2010). Recent GIS mapping of each watershed was reviewed so that lakes of 8 ha surface area or greater could be added to the model and watershed areas and linkages revised accordingly. Although additional lakes were added to improve the estimates of hydrologic function in the model, the resolution of the mapping did not allow complete review for the entire District. The exercise of identifying lakes >8 ha in size will continue and the complete set included in the next revision of the model. R _150074_MWQMLSH_final.docx 2

39 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Five additional lake catchments were subdivided from larger watersheds by the review, including four lake catchments and one river catchment. The areas of the revised catchments were determined and the size of their larger parent watersheds were reduced accordingly (Table 1). The watershed area for the Buck River parent watershed was greatly reduced in the new delineation, which reflects the large wetland complexes in the area leading to inconsistent water course definitions on previous maps and the fact that this watershed is at the District boundary. The exact watershed boundaries of this watershed are still to be confirmed, but the areas shown in Table 1 were used for the revised model. Table 1. Revised Subwatershed Delineations for Model. New Watershed Name New Waterbody Size (ha) New Catchment Size (ha) Subwatershed Parent watershed Old Parent Watershed size (ha) New Parent watershed size (ha) Ivy Lake Sparrow Lake Prospect Lake Island Lake Sparrow Lake Prospect Lake Haller Lake 13 1,462 Lake Vernon Buck River 7, Samlet Lake Lake Vernon Big East River n/a* 44,253 Kawpakwagog River 8 4,770 Muskoka River South Muskoka River 14,686 9,547 *Note: Samlet Lake watershed was previously included in the Big East River watershed that was modeled as a point source input to Lake Vernon, therefore no old parent watershed size is available. 2.2 Confirmation of Watershed, Lake and Wetland Areas Staff of the DMM completed a Geographic Information System (GIS) mapping exercise in order to review the areas of lakes, watersheds and wetlands used in the model. The details of the GIS methodology for this exercise are provided in Appendix A. In summary, watershed boundaries for the Water Quality Model were digitized from the archived Ontario Base Map (OBM) tiles, which had hand-sketched watershed lines that were drawn in the early 1990s. These OBM tiles only covered the area of the DMM and therefore updated areas were not available for the entire Muskoka River or Black/Severn subwatersheds, which extend outside the boundaries of Muskoka. In addition, updated watershed areas for lakes from the Lake Joseph and Lake Rosseau subwatersheds were available from the lakeshore capacity modeling that was completed for the Township of Seguin (AECOM 2009). A wetland GIS layer developed through the recent DMM Natural Heritage Review (Glenside Ecological Services Limited, 2009) was used to update the wetland areas in the watershed. The DMM acquired an updated wetland layer for the Natural Heritage Review (NHR) project in 2009 (Glenside Ecological Services Limited 2009). It was determined that this was the most appropriate layer to use for this process. The DMM administrative boundary represents the extent of this layer which was a problem as many of the watersheds extended outside of this boundary. The new NHR wetland layer ( NHR_Wetlands_Model ) had to be modified by adding wetland polygons from the MNR wetland layer to the areas that fell outside of the district boundary. Also, certain wetlands in the NHR layer had to be either deleted or clipped if a waterbody in the water quality model had been re-designated in the new NHR layer as a wetland. For the purposes of the water quality model it had to stay as a waterbody. R _150074_MWQMLSH_final.docx 3

40 Frequency J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m The revised watershed areas and wetland areas are provided, along with updated development statistics (from DMM records) in Appendix B. Updated data were available for 437 lakes, including 24 lakes in the Lake Joseph and Lake Rosseau subwatersheds. The median values of the updated watershed areas differed from the previous dataset by ~4 ha or 1% of the original, and the median lake areas differed by 1 ha, or 2.5%. We investigated all differences in watershed and lake sizes that were larger than 30% and generally concluded that the new areas were more accurate based on visual assessment of hard copy maps. Some exceptions were lakes with two distinct basins (e.g., Allen Lake, Boleau Lake), where lake area was calculated based on one basin in the new exercise, while both basins were counted in the previous version, which is more appropriate for the purpose of water quality modeling. The overall distribution of watershed sizes has not changed as revisions were generally minor ( Figure 1). Figure 1. Distribution of the previous and updated watershed areas Previous Model Updated Model Watershed Area (km 2 ) The new wetland GIS layer resulted in large changes in wetland areas, reflecting changes in wetland classifications: from those based on 1:50,000 NTS topographic maps to the ecological definitions that informed the mapping done for the DMM in The difference between previous and updated areas for individual wetlands ranged from 0 to 1423 ha, with a median of 16 ha. The most frequent change was the one from smaller to larger wetland areas (Figure 2). Wetlands play an important role in the dynamics of nutrients and dissolved organic carbon in the Muskoka watersheds and the water quality model is sensitive to wetland area to predict natural or background phosphorus concentrations in lakes (Paterson et al. 2006, Dillon and Molot 1994). The considerable change in wetland areas, therefore, could have a significant effect on the output of the water quality model. R _150074_MWQMLSH_final.docx 4

41 Frequency J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m The wetlands used by the MOE to derive the phosphorus export coefficients in the model (Paterson et al. 2006, Ontario 2010) were delineated on the ground by MOE staff in the 1980s and have been monitored since that time to determine annual phosphorus export and dynamics. The scale of the DMM program does not allow for delineation by the same methods and the NTS maps (which were generated from aerial photography) and the GIS mapping (which was generated through digital aerial photography and satellite imagery) would not classify wetlands the same as ground surveys. The differences in wetland areas between the 2013 LSH model and previous models are therefore unavoidable artifacts of changes in the classification and mapping methods over time. Such changes should be minimal for any future model revisions. Figure 2. Distribution of differences between the previous and updated wetland areas More Difference in Wetland Cover (ha) Note: Negative values represent a decrease in wetland area from the previous model to the updated model and positive values represent an increase in wetland area in the updated model. 2.3 Loadings from Wastewater Treatment Plants The DMM provided the most recent ( ) figures for annual total phosphorus loading from the municipal wastewater treatment plants (WWTPs) that discharge to surface waters in Muskoka (Table 2). These data were used in two ways for the model: The average annual loads for the past five years ( ) were added as point-source phosphorus loads to the respective receiving water bodies in the model to estimate existing phosphorus concentrations, and The total allowable annual phosphorus loads were used to model the response of the receiving water bodies at full plant build out, as an estimate of the maximum future phosphorus concentrations that would result from WWTP discharges. R _150074_MWQMLSH_final.docx 5

42 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 2. Annual Flows and Total Phosphorus Loads from Wastewater Treatment Plants in Muskoka ( ) Municipality Receiver Total Allowable P Loading Total Flow Total TP Load kg/yr m 3 /yr kg/yr Min Max Ave. Min Max Ave. Bala Baysville Moon River South Muskoka River , , , n/a 7,571 58,367 35, Bracebridge - Mech. 949,530 1,184,628 1,070, Bracebridge - Lagoons 9, , , Bracebridge Total Gravenhurst Huntsville -MV & GP Mactier Port Carling Port Severn Muskoka River Lake Muskoka Fairy Lake Conger Marsh Indian River Severn River 500 1,078,691 1,527,021 1,270, ,933 1,180,938 1,077, ,150,105 2,244,067 1,843, n/a 23,554 51,594 40, n/a 120, , , ,400 75,124 65, Loadings from Golf Courses and Urban Runoff No information was available on phosphorus export from golf courses on the Canadian Shield or in Muskoka for the previous model (Gartner Lee Limited 2005) and therefore phosphorus export from cleared cottage lots (22.5 mg/m 2 /yr) developed by Freshwater Research (1998) was used to estimate loads from golf courses. Winter and Dillon (2006) have since developed phosphorus export coefficients from intensive run-off sampling on two different-sized golf courses located on the Precambrian Shield in Muskoka. The resulting export coefficient was 14 mg/m 2 /yr and was adopted for use in the LCM model (Paterson et al. 2006). This value is similar to the golf course export coefficient of 0.19 kg/ha/yr derived in the U.S. by Reckhow et al. (1980). Golf course export was calculated based on the number of holes with direct drainage to a lake (golf courses are designed to maximize interior drainage to storm water management ponds to allow for reuse of irrigation water) and an average of 20,000 m 2 /hole (2 ha) of cleared area assigned the export coefficient of 14 mg/m 2 /yr. The export from cleared cottage lots was estimated on an assumed average cleared area of 2000 m 2 for each cottage lot. A phosphorus export coefficient of 45 mg/m 2 /yr from urban areas was used in the previous model and was derived for Lake Wilcox in southern Ontario by Nürnberg (1997). Dillon et al. (1986) reported that R _150074_MWQMLSH_final.docx 6

43 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m phosphorus export in Shield areas was 50% of that in off Shield areas, for forested and cleared areas, and so the Lake Wilcox value was reduced to 22.5 mg/m 2 /yr for cleared (urban) areas on each cottage lot. Natural phosphorus would make up part of this load and is already accounted for in the natural loading component of the model. The average natural load for forested Shield areas of 5.5 mg/m 2 /yr (Dillon et al. 1986) was therefore subtracted from 22.5 to produce a final export figure of 17 mg/m 2 /yr for cleared areas of cottage lots. The total export assumed for each cottage lot was therefore 2000m 2 * 17 mg/m 2 /yr = mg/yr. This is similar to the value of 0.04 used by Paterson et al. (2006) but differs through use of a) a smaller assumed cleared area for each cottage lot and b) a higher export coefficient (17 mg/m 2 /yr versus 9.8 mg/m 2 /yr). Runoff from urban areas of Muskoka was assigned an export coefficient of 39.5 mg/m 2 /yr, which was calculated as the value derived from the Lake Wilcox study minus the 5.5 mg/m 2 /yr for natural export. Published urban or high density residential phosphorus export coefficients range from 38 to 410 mg/m 2 /yr for a number of studies that we reviewed, but none of these are specific to Muskoka or the Precambrian Shield. Paterson et al. (2006) suggest a value of 50 mg/m 2 /yr, but this was derived by Winter and Duthie (2000) for off Shield urban areas which have higher phosphorus export than on-shield areas (Dillon et al. 1986). The Lake Wilcox export coefficient is at the lower end of published values. Its catchment is dominated by high- to medium-density residential development with some commercial property (Google Maps; Gartner Lee Ltd. 1998). Most Muskoka towns and villages are dominated by residential areas as well, and therefore the current urban export coefficient appears to be appropriate. Commercial and industrial areas have a higher phosphorus export in comparison to residential areas due to larger impervious areas that increase runoff. The towns of Huntsville, Bracebridge and Gravenhurst contain large commercial developments, such as department stores and adjacent large parking lots. For lakes in these areas, for example Lake Vernon in Huntsville, the current urban export coefficient may therefore underestimate urban phosphorus load to lakes. There are large uncertainties in the export coefficients for urban runoff in general, no estimates have been made for Shield areas, much parking lot export is in particulate form and hence easily intercepted by stormwater management practices or not-bioavailable. As a result, we did not attempt to derive specific coefficients for these sources. Phosphorus export from commercial and industrial areas was therefore estimated using the general coefficient for urban runoff described above. 3. Review of Lake Water Quality 3.1 Dissolved Organic Carbon Dissolved organic carbon (DOC) affects water clarity, is closely related to total phosphorus concentrations in Muskoka lakes (Gartner Lee Ltd. 2005) and has been declining over the past 25 years (Palmer et al. 2011). It is therefore important to monitor DOC in order to better understand the reasons for patterns in lake water clarity and nutrient concentrations. The DMM water quality model does not directly use DOC concentrations, but the main source of DOC, wetlands, are included in the model as a phosphorus source if they represent 3.5% or more of the watershed area. Measured DOC concentrations assist in interpreting potential differences between modeled and measured phosphorus concentrations and any trends in phosphorus concentration and in assessing if phosphorus contributions from wetlands are correctly estimated by the model. R _150074_MWQMLSH_final.docx 7

44 Frequency J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m The DMM measured DOC at 195 sites on two to four occasions from 2004 to 2011, since the last revision of the Muskoka Water Quality Model. This is more than double the lakes sampled for DOC prior to 1998, when DOC data for 85 lakes were available. Average DOC for the 195 sites ranged from 1.4 to 11.8 mg/l, with an overall average of 5 mg/l. About half of the lakes have DOC concentrations >5 mg/l (Figure 3). These tea coloured lakes appear orange/yellow brown to the eye and have low water clarity (as described by Secchi depth). Figure 3. Distribution of mean dissolved organic carbon in Muskoka lakes (n=195) Distribution of Mean DOC in Muskoka Lakes More Dissolved Organic Carbon (mg/l) Figure 4 shows that, on the basis of average values of all lakes, there was no change in DOC in the monitored lakes between 2004 and Eimers (2008), however, reported that DOC increased in PreCambrian Shield lakes between 1980 and 2001 and Palmer et al. (2011) reported the same for Muskoka area lakes between the 1980s and 2004/2005. (Figure 5). These changes likely reflect changes in hydrology, the effets of warming temperature on soils dynamics and the cycle of drought and flooding in wetlands in a changing climate. The changes have implications for water clarity, the relationship between phosphorus and water clarity and for the predictive relationship between wetland area and natural phosphorus export in Paterson et al. (2006) that was used in the provincial and DMM models. R _150074_MWQMLSH_final.docx 8

45 DOC (mg/l) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 4. Dissolved Organic Carbon in Muskoka Lakes from Mean DOC of Muskoka Lakes Year Figure 5. Changes in Dissolved Organic Carbon in DESC lakes from 1980s to R _150074_MWQMLSH_final.docx 9

46 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m 3.2 Total Phosphorus Total phosphorus (TP) concentrations have been monitored by the DMM since the mid 1980s, but reliable low detection limits were only achieved in The current ten-year mean value of measured phosphorus concentrations is compared against the modeled threshold value as part of the Lake System Health program and therefore represents a major input to policy decisions. Measured TP also serves to validate the water quality model. This section reviews the quality of DMM TP data, identifies outliers, presents the most recent ten-year mean TP concentrations, and describes the TP distribution and recent trends in the 195 monitored lakes Data Quality High quality phosphorus data are required to provide a representative ten-year mean value that can be used to validate the water quality model and ultimately to support policy decisions. Quality of the DMM monitoring data is assured by a strict sampling protocol, precise laboratory analysis and a number of in-laboratory QA/QC measures. The sampling techniques and analyses have been continuously refined over the course of the LSH program, with implementation of the following main improvements: Since 2000, the samples have been taken during spring turnover only and collected directly into the borosilicate glass tubes that are used for analysis. This eliminates the container effects that often add bias to the results when samples are stored and then transferred from a sampling container to the analytical tube (Clark and Hutchinson, 1992, Clark et al 2010). Since 2002, duplicate phosphorus samples have been collected and analyzed at the Dorset Environmental Science Centre (DESC) laboratory. Since 2003, samples have been field filtered through an 80- m coarse sieve to remove large particulate matter such as zooplankton that can contaminate the samples and result in high phosphorus concentrations and increased variability in mean values (Clark et al. 2010) Outlier Detection and Removal An outlier is a data point that is distinctly different from other values in a dataset. Outliers can result from analysis errors, from contamination or from naturally occurring variability or change. If an outlier is due to natural variability or change, it can provide useful information about emerging trends. Analysis errors include the accidental exchange or mix-up of samples in the laboratory, mistakes in data transfer and recording and failure of analytic equipment. Contamination can occur if the water comes in contact with phosphorus-enriched surfaces before entering the sample container, such as sunscreen lotion covered hands, or if drops of a phosphorus-containing liquid, such as liquid soap, spill into the container. Another important source of contamination is the presence of large zooplankton in the sample. As the samples taken by the DMM were not filtered for zooplankton before 2003, this is a potential source of outliers for samples taken during that time (Gartner Lee Ltd. 2008). In 2008, a statistical approach was developed and used to identify outliers in the DMM TP data set (Gartner Lee Ltd. 2008). The outlier analysis consisted of a tiered approach that included up to three different tests on the data: Run the non-parametric Dixon s test, which is appropriate for small sample sizes, R _150074_MWQMLSH_final.docx 10

47 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Run the parametric Grubb s test to confirm outliers identified by the Dixon s test, If the two tests are in disagreement, include duplicate sample data to increase the sample size and thereby power of the statistical tests and re-run both tests (recognizing that this approach may result in pseudoduplication), If both tests still disagree, then the data point in question will be retained as a conservative approach to outlier removal, i.e., if there is no clear statistical reason to remove the value it will be retained. The identification of outliers in the 2008 study resulted in changes to the average TP concentration of several lakes and the re-classification of four lakes in the LSH program, which was adopted by the DMM. The current review again applied the approach developed by Gartner Lee Limited (2008) to identify outliers. All data from 2000 to 2014 were included in the analysis, which increased the number of data points for each site, thereby improving confidence in the statistical tests. A total of 20 outliers was identified in the dataset and 54 outliers were detected in the data set, including 18 outliers from sites in Lake Joseph (Table 3). The increase reflects increased monitoring effort for Lake Joseph and Little Lake Joseph which were monitored at approximately biweekly intervals from spring to the end of August since The remaining lakes were sampled once each year, during spring overturn. R _150074_MWQMLSH_final.docx 11

48 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 3. List of Outlier TP Measurements Detected in DMM Dataset Outliers (Lake Joseph) Outliers Site_Name TP ( g/l) Date Site_Name TP ( g/l) Year Joseph River Aug-10 Bonnie Lake Lake Joseph-Cox Bay Aug-10 Camp Lake Lake Joseph-Cox Bay May-09 Clear Lake BB Lake Joseph-Cox Bay Aug-09 Clearwater Lake HT Lake Joseph-Hamer Bay Aug-09 Cornall Lake Lake Joseph-Main Aug-09 Fawn Lake Lake Joseph-Main Aug-09 Galla Lake Lake Joseph-Main May-09 Go Home Lake Lake Joseph-Main 7 23-Jul-09 Gull Lake Lake Joseph-North May-09 Lake Huron-Little Go-Home Bay Lake Joseph-North Jun-08 Lake of Bays - Haystack Bay Lake Joseph-North Aug-09 Lake of Bays - Rat Bay Lake Joseph-South Aug-09 Lake Vernon - Main Lake Joseph-South 16 4-Aug-09 Lake Vernon - North Bay Lake Joseph-South Jul-09 Lake Waseosa Lake Joseph-South May-09 Little Lake Joseph Lake Joseph-South Jul-10 Little Long Lake Little Lake Joseph 61 6-Aug-14 Longline Lake Longs Lake Loon Lake McKay Lake Morrison Lake Oudaze Lake Ril Lake Riley Lake Silver Lake GR Six Mile Lake - Cedar Nook Bay Skeleton Lake Spence Lake - North Stewart Lake Sunny Lake Tackaberry Lake Three Mile Lake - Hammels Bay Tooke Lake Toronto Lake Tucker Lake R _150074_MWQMLSH_final.docx 12

49 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Ten-Year Mean Spring Turnover TP The ten-year mean spring turnover TP concentrations were calculated after removing outliers and bad splits. Values for each lake and calculated 10 and 15 year mean values are presented in Appendix C. Mean total phosphorus concentrations in the 196 monitored Muskoka lakes and basins ( ) ranged from 2.7 to 28.2 µg/l with an average of 9.1 µg/l and a standard deviation of 3.7 µg/l. More than half (68%) of the lakes met the MOE s Interim Provincial Water Quality Objective (PWQO; MOE 1994) of 10 µg/l for total phosphorus for lakes that are naturally below this value ( Figure 6). Only five lakes had a TP concentration greater than the PWQO of 20 g/l for the protection against nuisance growth of aquatic plants and algae. This demonstrates the excellent recreational water quality in Muskoka lakes in general. The average TP was 10 µg/l and 68% of the lakes had a TP concentration <10 g/l for the dataset (GLL 2005) indicating that the overall status of Muskoka lakes has not changed since the 1990s. Some individual lakes, however, show trends over time, as discussed in Section TP concentrations in the DMM lakes varied by an average of 19.9% between years, which corresponds to the interannual variance described for other Ontario lakes with long-term monitoring data. Clark et al. (2010) reported a coefficient of variation of 21.4% for a series of measurements (n=1,994) made in 8 DESC lakes over 27 years, and 23% for 243 locations sampled over 4 years in the MOE s Lake Partner program. Interannual variance for the period is largely reduced from the value of ~40% for data collected from (GLL 2005) likely due to improved data quality (Section 3.2.1). The current monitoring program therefore has an increased capacity to detect changes in TP concentrations when compared with the 1990s. R _150074_MWQMLSH_final.docx 13

50 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 6. Distribution of ten-year mean spring TP ( ) in Muskoka lakes (n=196) Total Phosphorus Trends Statistical testing was conducted using the annual means of measured phosphorus concentrations in all lakes or lake basins in the Muskoka data set for the years There are 196 lakes for which measured concentrations exist but for 6 of these 8, the data record was 2 years or less and so they were excluded from the analysis. Trends in annual measured total phosphorus concentrations were computed for each site with at least three years of data (n=190 sites) for the period from Data were tested for normal distribution of residuals using the Wilks-Shapiro Test. Normally distributed data were tested for trends using simple linear regression while non-normal data were tested using the non-parametric Mann-Kendall test at a significance of p<0.10. Trends were evaluated for significance at a probability level of p<0.10. The probability level sets an error rate that the investigator has chosen as an acceptable level of confidence in their conclusion; in this case the probability of determining a trend when in fact there is no trend, called a Type 1 statistical error. A Type 1 error would protect water quality because it would increase the likelihood that management actions would be triggered under the revised Lake System Health classification. A conclusion that there is no trend when there is, could threaten water quality because management would not be triggered. This error increases the risk to water quality. 7 Mean TP values were calculated in 2012 using the ten-year data set for comparison against the model. The trend assessment was completed later on in the project using all available data since Barrons Lake, Rogers Cove (Fairy Lake), Go Home Bay, Little Go Home Bay, South Basin of Lake Muskoka,and McLeans Bay of Sparrow Lake R _150074_MWQMLSH_final.docx 14

51 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m It is customary to use a significance level of p<0.05 for statistical testing, accepting a risk that one in 20 comparisons may result in a Type 1 error. A significance a level of p<0.10 was chosen for the trend testing for Muskoka lakes by accepting a higher Type 1 error rate to increase the degree of water quality protection. There was a statistically significant (p<0.10) decreasing trend in total phosphorus concentrations in 18 of the 190 lakes tested (Table 4). None of the lakes displayed a significant increasing trend (p<0.10). These results contrast those reported in HESL (2013) where phosphorus concentrations ( ) increased significantly in four lakes over the period of record (Gull Lake, South Nelson Lake, Nine Mile Lake and Solitaire Lake) and decreased significantly in 24 of the lakes. The difference in the number of trends occurred due to the increase in sampling points. The lakes that had significant increasing trends in the assessment had only few data years (4 years for Gull, South Nelson and Solitaire lakes, and 6 years for Nine Mile Lake). The increase in data years improves the identification of trends, but also provides a more robust data set to identify outliers that can have an undue influence on trend analysis based on such few data points. Table 4. Lakes With Significant Decreasing Trends (p<0.10) in Total Phosphorus: Site n slope R2 SLR p- value residuals normal (if >= 0.05) Kendall's Tau Kendall's p-value Mirror.Lake Tackaberry.Lake Clark.Lake Medora.Lake Moot.Lake Kahshe.Lake...Main Lake.of.Bays...SMRB Longline.Lake Three.Mile.Lake...Hammels.Bay Clearwater.Lake.GR Six.Mile.Lake...Provincial.Park.Bay Prospect.Lake High.Lake Lake.Joseph.Main Gartersnake.Lake Lake.Muskoka...Muskoka.Bay Mary.Lake Kahshe.Lake...Grants.Bay Note: Shaded cells indicate the p value for the significance assessment. 3.3 Relationship between Total Phosphorus and Dissolved Organic Carbon Gartner Lee Limited (2005) showed a strong and statistically significant relationship between DOC and TP in DMM lakes. The expanded and updated dataset for average DOC concentrations ( ) and average TP concentrations (2002 to 2011) improved this relationship (r 2 = 0.45, p<0.001, d.f. = 192) (Figure 7). This confirms the importance of wetlands in the phosphorus budget of DMM lakes. The scatter about the R _150074_MWQMLSH_final.docx 15

52 Total Phosphorus ( g/l) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m relationship would be influenced by changes in TP and DOC concentrations over the period of record that were not captured by use of mean values, changes in the export of DOC and TP to DMM lakes over the period of record and other sources of phosphorus to the lakes. Figure 7. Relationship of total phosphorus and dissolved organic carbon in Muskoka lakes. TP-DOC Relationship in 193 Muskoka Lakes y = x R² (adj) = 0.45; p< Dissolved Organic Carbon (mg/l) 4. Review of Oxygen and Phosphorus Dynamics Dissolved oxygen is an important water quality variable for several reasons. Fish and other aquatic animals need it to survive, and a lack of oxygen (anoxia) in bottom waters can cause the mobilization of phosphorus from sediments to the water column in a process termed internal phosphorus loading. The decomposition of organic matter uses oxygen, but surface waters that are mixed by wind are regularly replenished with oxygen by exchange with the atmosphere. The layer of cool dense water at the bottom of lakes that thermally stratify (called the hypolimnion), however, is cut off from surface exchange, and is only replenished with oxygen during the spring and fall turnover periods. In some cases, high rates of decomposition and/or a small volume of water in the hypolimnion can result in anoxia near the sediments and trigger internal phosphorus loading. Internal phosphorus loading from anoxia can be a significant component of the total phosphorus load to lakes, and is considered in the water quality model. Internal phosphorus loads are not explicitly calculated in the water quality model. Instead, a lower settling velocity for phosphorus is assumed in the model for lakes that have an anoxic hypolimnion as a surrogate to account for internal phosphorus loading (Dillon et al. 1986). Not all phosphorus contained in a lake is passed on to downstream lakes because a portion of the phosphorus is lost from the water column to the sediments. This portion is estimated in the model by a retention coefficient (R) that describes the proportion of the phosphorus load to a lake that is expressed as concentration. Retention is based on the relationship between the areal water load (qs) to a lake and the settling velocity (v) of phosphorus where R = v/(v+qs). The settling velocity of phosphorus is 12.4 m/yr for stratified oligotrophic lakes on the Precambrian Shield and 7.2 m/yr for those lakes with anoxic hypolimnia (Dillon et al., 1986). R _150074_MWQMLSH_final.docx 16

53 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m A weakness in the previous modelling was that oxygen status, and therefore the potential for internal phosphorus loading, was not known for a large number of lakes (~340 lakes) in the DMM model. In addition, the settling velocity used for anoxic lakes was developed from only one lake (east basin of Red Chalk Lake, Dillon et al. 1986). The entire hypolimnion of that lake basin goes anoxic for several weeks during the open water season and so the settling velocity derived from Red Chalk East to approximate internal phosphorus loads may not be representative of lakes that differ in the extent and duration of anoxia. Freshwater Research (1998) attempted to improve the DMM model through use of the anoxic factor (Nürnberg 1995) to quantify the duration of anoxia. The anoxic factors were estimated via empirical means, however, and showed a poor fit with the measured data on oxygen status and so this approach was not used in the last version of the model (Gartner Lee Ltd. 2005). The following sections explore a number of opportunities to improve the ability of the DMM water quality model to predict internal phosphorus loads in the lakes, including: Development of a priority list of lakes for which data on oxygen status and phosphorus concentration in the hypolimnion should be collected to identify the occurrence of internal phosphorus loading in DMM lakes (Section 4.1), Assessment of relationships between lake characteristics and measured oxygen data to see if a predictive relationship could be developed and used to estimate oxygen for the unmonitored lakes (Sections 4.2 and 4.3), and A literature review to assess if any recent scientific knowledge can be used to improve estimates of oxygen status and phosphorus retention in anoxic lakes (Section 4.4). 4.1 Selection of Lakes for Identification of Internal Phosphorus Loading Elevated phosphorus concentration in the hypolimnion relative to surface water provides a direct indication of internal phosphorus loading in lakes. Lakes in which anoxia may occur were therefore identified and sampled for phosphorus at 1m off the lake bottom (1 mob) by the DMM during the end-of-summer field program in 2011 to confirm the occurrence of internal phosphorus loading for input to the water quality model Selection process The selection process was carried out in the spring of 2011 using data from the previous 10 years ( ) Stage 1 Screening Dissolved oxygen (DO) profiles collected from 2000 to 2010 were reviewed to identify DMM lakes that potentially go anoxic. All lakes were first identified for which low oxygen levels (hypoxia; defined as DO <2 mg/l at 1 mob) were recorded at least once within two weeks of September 1 in the hypolimnion, and therefore are considered to have potential for continued oxygen loss and therefore to become anoxic. This initial screening provided a list of 95 potentially anoxic lakes (Table 5). Lakes were then screened out if hypoxia was recorded infrequently ( rarely anoxic), if DO was only slightly below 2 mg/l ( barely anoxic), or if hypoxic conditions were recorded at only the deepest 1-m depth interval (suggesting that the probe of the DO meter may have been in lake sediments instead of overlying waters). Lakes were also screened out if they are being monitored by another program (e.g. MOECC) and therefore R _150074_MWQMLSH_final.docx 17

54 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m additional sampling for hypolimnetic TP was not required. Twenty-five lakes were removed from the list following this screening (Table 6). The remaining 70 lakes were those in which hypoxia was substantial (<< 2 mg/l) and occurred in more than 50% of the years for which measured data exist, and that were not presently monitored by another program. Table 5. Screening Level 1 Candidate Anoxic Lakes (n=95) Lake_# Lake_Name WardName Lake_# Lake_Name WardName ATKINS LAKE Macaulay LITTLE LONG LAKE Cardwell BASS LAKE Ryde LONG LAKE Bala BAXTER LAKE Baxter LONGLINE LAKE Ridout BEARPAW LAKE Wood MAINHOOD LAKE Stephenson BEN LAKE Ryde McDONALD LAKE Gibson BLACK LAKE Medora & Wood McKAY LAKE Draper BONNIE LAKE Macaulay McREY LAKE Macaulay BRUCE LAKE Medora (North) MEDORA LAKE Medora & Wood BUCK LAKE Sinclair & Finlayson MENOMINEE LAKE McLean BUTTERFLY LAKE Medora & Wood MOOT LAKE McLean CAMEL LAKE Watt MORRISON LAKE Wood CARDWELL LAKE Cardwell NEILSON LAKE Wood (South) CASSIDY LAKE Medora (North) NINE MILE LAKE Wood (South) CHUB LAKE Brunel NORTH BAY Baxter CLEAR LAKE Medora & Wood NORTH MULDREW LAKE Muskoka COOPER LAKE Franklin NUTT LAKE Watt CORNALL LAKE Morrison OTTER LAKE Brunel DARK LAKE Bala OUDAZE LAKE Chaffey DEVINE LAKE Stephenson PAINT LAKE Ridout DICKIE LAKE McLean PENINSULA LAKE - EAST Sinclair & Finlayson DOTTY LAKE Sinclair & Finlayson PIGEON LAKE Muskoka ECHO LAKE McLean PINE LAKE Wood (South) FAIRY LAKE - NMRB Chaffey PORCUPINE LAKE Ridout FAWN LAKE Stephenson PROSPECT LAKE Draper FLATROCK LAKE Gibson REBECCA LAKE Sinclair & Finlayson FOOTE LAKE Sinclair & Finlayson RICKETTS LAKE Medora (North) FOX LAKE Stisted RIL LAKE Ridout GARTERSNAKE LAKE Draper RILEY LAKE Ryde GIBSON LAKE - NORTH Gibson ROSE LAKE Stephenson GIBSON LAKE - SOUTH Gibson RYDE LAKE Ryde GILLEACH LAKE Macaulay SILVER LAKE Muskoka GOLDEN CITY LAKE Stisted SILVER LAKE Port Carling GRINDSTONE LAKE Ridout SILVERSANDS LAKE Freeman GULLWING LAKE Wood (South) SIX MILE LAKE - CEDAR NOOK Gibson HALFWAY LAKE Macaulay SIXTEEN MILE LAKE Franklin HARDUP LAKE Sinclair & Finlayson SOUTH BAY Baxter HEALEY LAKE Macaulay SPARROW LAKE Morrison HESNER'S LAKE Bala SPENCE LAKE - NORTH Draper HIGH LAKE Watt STEWART LAKE Medora (North) JESSOP LAKE Chaffey STONELEIGH LAKE Macaulay JOSEPH RIVER Medora (North) TASSO LAKE Sinclair & Finlayson KAHSHE LAKE - Grants Bay Ryde THINN LAKE Draper LAKE MUSKOKA - WHITESIDE BAY Wood (South) THREE MILE LAKE Morrison LAKE MUSKOKA - MUSKOKA BAY Muskoka THREE MILE LAKE - HAMMEL'S Watt LAKE VERNON - HUNTERS BAY Chaffey TUCKER LAKE Brunel LAKE VERNON - NORTH BAY Chaffey TWELVE MILE BAY - WEST Freeman LEECH LAKE Oakley WALKER LAKE Sinclair & Finlayson Lake_# Lake_Name WardName WEISMULLER LAKE Draper LITTLE LONG LAKE Cardwell WOOD LAKE Oakley R _150074_MWQMLSH_final.docx 18

55 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Site Name Black Lake Table 6. List of Lakes Removed From the Anoxia Sampling List and Rationale Reason for Removal from Anoxia Sampling List hypoxic at 4m but not at 3m, where DO of up to 8.5 mg/l was measured. Minimal hypoxic volume. Bonnie Lake hypoxic at 20m only meets criterion 2 years out of 4 Bruce Lake Dotty Lake Dickie Lake Fairy Lake, North Muskoka River Flatrock Lake Hammells Bay of Three Mile Lake High Lake Jessop Kahshe Lake hypoxic 2 out of 8 years at 6 m from bottom, always >6.7 at 5m, but has a history of algal blooms hypoxic 1 year of 4, at 28 m but not 26 m sampled by MOE Dorset, only stratifies weakly and hypoxia occurs occasionally hypoxic 3 of 4 years but marginally so marginally hypoxic in 1 of 4 years, 1 other year was 1.6 mg/l at bottom hypoxic but well sampled by MOE work in the past hypoxic 2 of 4 years but only at bottom (14m) DO is very high at 12m hypoxic in 2 of 4 years at 3m, but DO very high at 2m Grants Bay is hypoxic, but DO in the Main Basin only drops to 2-2.5mg/L North Bay (GB) Joseph River Muskoka Bay North Bay of Lake Vernon Morrison Lake Oudaze Lake Paint Lake Prospect Lake Rebecca Silver Lake GR Six Mile Lake South Bay (GB) Stoneleigh Lake Tasso Lake Tucker Lake hypoxic but is included in GBF/SDEA sampling program hypoxic in 2 years out of 4 at 7m-8m hypoxic in 1 of 4 years (14m) but has been in past; kept in to document its recovery over time hypoxic in 1 of 6 years hypoxic in 3 of 5 years but degree of anoxia is not strong hypoxic in 1 of 4 years at 32m depth, For 3 years max. measured depth was only 18-20m. hypoxic in 1 of 6 years and marginal hypoxic in 1 of 4 years hypoxic in 2 of 5 years but marginal hypoxic in 1 of 5 years covered by TGB program hypoxic but is included in GBF/SDEA sampling program hypoxic in 1 of 3 years - stratifies at 3m hypoxic in 1 of 5 years inconsistent data: 3 profiles to 16m (1 is <1 mg/l, 1 has bottom DO max of 13 mg/l), 1 profile to 6 m R _150074_MWQMLSH_final.docx 19

56 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Stage 2 Screening A technical meeting between DMM, HESL and the Ontario Ministry of the Environment (MOE) was held in October 2010, where the parties agreed that lakes with a maximum depth of 9-12 m were good candidates for internal loading. Lakes in this depth range are deep enough to stratify, but their limited depth is likely to result in a small hypolimnion and hence limited volume and a small mass of oxygen for assimilation of organic matter making them susceptible to development of anoxia. The second stage screening process therefore focussed on lakes within this depth range. Our review of the DMM dataset found that many of the lakes that were less than 9 m deep were hypoxic but that lakes less than 6 m in depth were rarely or barely anoxic. We therefore included only those lakes that were 6-9 m in depth in the list as candidates that were likely to be anoxic. Of these, 4 lakes were rarely or barely anoxic or had MOE data. This reduced the candidate lakes to 32. The final screening process considered lakes that were deeper than 12 m and anoxic. Eight lakes were identified including five lakes that were 13 m deep and three 14-m deep lakes, including Muskoka Bay. Summary There were 32 lakes between 6 and 12 m deep, and 8 lakes between 13 and 14 m deep that were hypoxic, for a total of 40 lakes that were included in the list for sampling total phosphorus at 1 mob in 2011 (Table 7). Table 7. Forty Lakes Recommended for 2011 Late Summer Bottom TP Sampling DMM ID Lake Name Township Area Depth PIGEON LAKE Muskoka RICKETTS LAKE Medora (North) FAWN LAKE Stephenson HESNER'S LAKE Bala MENOMINEE LAKE McLean MOOT LAKE McLean BASS LAKE Ryde BEARPAW LAKE Wood CASSIDY LAKE Medora (North) HEALEY LAKE Macaulay NUTT LAKE Watt SPENCE LAKE - NORTH Draper THREE MILE LAKE Morrison BEN LAKE Ryde DEVINE LAKE Stephenson GULLWING LAKE Wood (South) LAKE VERNON - HUNTERS BAY Chaffey MEDORA LAKE Medora & Wood OTTER LAKE Brunel CORNALL LAKE Morrison McREY LAKE Macaulay NEILSON LAKE Wood (South) RIL LAKE Ridout SILVERSANDS LAKE Freeman SIXTEEN MILE LAKE Franklin DARK LAKE Bala FOOTE LAKE Sinclair & Finlayson ROSE LAKE Stephenson CHUB LAKE Brunel COOPER LAKE Franklin ECHO LAKE McLean RYDE LAKE Ryde ATKINS LAKE Macaulay CAMEL LAKE Watt FOX LAKE Stisted LEECH LAKE Oakley WOOD LAKE Oakley GIBSON LAKE - NORTH Gibson GIBSON LAKE - SOUTH Gibson LAKE MUSKOKA - MUSKOKA BAY Muskoka R _150074_MWQMLSH_final.docx 20

57 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Scheduling The DMM field crews were scheduled to sample 89 lakes in Of these, 14 lakes were on the list of lakes recommended for phosphorus sampling at 1 mob, leaving an additional 26 lakes to be added to the summer schedule. The scheduling and available time for the summer sampling crew meant that sampling had to begin in the last week of July. Lakes sampled between then and mid August (the temporal criterion for sampling of anoxia), however, may not yet be anoxic, risking false negatives in the sampling of phosphorus at 1 mob. Prioritized lists of lakes for sampling were developed to reduce the probability of false negatives, so that: lakes that showed no history of anoxia in the Stage 1 screening were scheduled for sampling in the early summer, as there is a low probability of them developing anoxia and therefore little implication for early sampling. These Priority 1 lakes are shown in Table 8. of the 40 hypoxic lakes, those with the greatest magnitude of hypoxia (very little DO throughout much of the hypolimnion) were more likely to show anoxia if sampled before mid-august. These Priority 2 lakes are shown in Table 9. lakes that did not exhibit strong hypoxia were sampled at the end of the summer, closest to the end of August as they will take longer to develop anoxia. These Priority 3 lakes are shown in Table Late Summer Hypolimnetic Phosphorus Results In mid to late summer 2011, the priority 2 and 3 lakes were sampled for hypolimnetic TP in order to test whether the measured low oxygen concentrations also lead to internal phosphorus loading from the sediments. Out of the 39 samples, only 12 samples showed elevated TP in the bottom waters compared to the surface sample (Table 10), representing 30% of the sampled lakes. This is likely an underestimation of phosphorus recycling, as 14 of the lakes that did not show elevated bottom TP concentrations were sampled at depths more than 3 m above the bottom as a result of field crew error and therefore any elevated TP closer to the bottom may have been missed. In addition, some of the priority 2 lakes (substantial anoxia) that were measured in mid August 2011 and did not show elevated TP in the hypolimnion may show an internal phosphorus loading response later in summer. Given that oxygen dynamics can vary over the years and given the uncertainties related to the sampling method in 2011, it is recommended to repeat the hypolimnetic sampling strategy. For many priority 2 and 3 lakes, the sampling procedure can stay the same, but some lakes should be sampled deeper or shallower and some should be sampled later (Table 10). The results of a repeat sample for these lakes will help to assess whether the lack of evidence for internal phosphorus loading in the lakes is due to inter-annual variation, sampling procedure or if it is actually representative of the average conditions. R _150074_MWQMLSH_final.docx 21

58 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 8. Priority 1 Lakes, No Anoxia Priority 1 Lakes Ada LOB - Trading Bay Axle Long s (Utterson) Barron s Longline Bigwind Mary Brandy McDonald Brooks Morrison Bruce Myers (Butterfly) Buck Nine Mile Buck North Muldrew Butterfly Oudaze Chub Oxbow Cognashene Bay Paint (St. Mary) Crosson Pell Deer Penfold Flatrock Pine Go Home Bay Prospect Grandview Roderick Grindstone Rosseau - Brackenrig Bay Gull Rosseau - Main Gullfeather (Gull) Rosseau - North Hardup (Poverty) Rosseau - East Portage Bay Joseph - Cox Bay Rosseau - Skeleton Bay Joseph - Hamer Bay Silver Joseph - Little Lake Silver Joseph Main Six Mile - Cedar Nook Bay Joseph North Six Mile - Main Joseph River Six Mile - Provincial Park Bay Joseph South Solitaire Kahshe - Grant s Bay South Muldrew Kahshe Main South Nelson Leonard Stoneleigh Little Go-Home Bay Tackaberry LOB - 10 Mile Bay Tasso LOB - Dwight Bay Tucker LOB - Haystack Bay Turtle (Long Turtle) LOB - South Portage Bay Wah Wah Taysee LOB - Rat Bay Waseosa LOB - South Muskoka River Bay R _150074_MWQMLSH_final.docx 22

59 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 9. Priority 2 and 3 Lakes, Substantial and Weak Anoxia Priority 2 Lakes (Substantial Anoxia) Atkins Bearpaw Ben Gibson-North Gibson-South Gullwing Hesner's Lake Muskoka-Muskoka Bay McRey Menominee Moot Neilson Otter Ril Rose Ryde Lake Vernon-Hunters Wood Bass Priority 3 Lakes (Weak Anoxia) Camel Cassidy Chub Cooper Cornall Dark Devine Echo Fawn (Deer) Foote Fox Healey Leech Medora Nutt (Mud) Pigeon Ricketts Silversands Sixteen Mile (Long) Spence-North Three Mile R _150074_MWQMLSH_final.docx 23

60 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 10. Hypolimnetic TP Measurements in Late Summer Lake Surface TP (ug/l) Bottom TP 2011 (ug/l) P Recycling confirmed? Lake Depth (m) TP sampling depth (m) Sample above bottom (m) Oxygen Status for model Recommendations for monitoring Atkins Lake No anoxic deeper Bass Lake GR Yes anoxic deeper Bearpaw Lake No not anoxic later Ben Lake Maybe anoxic deeper, later Camel Lake No not anoxic same Cassidy Lake No not anoxic same Chub Lake HT No anoxic deeper Cooper Lake No anoxic deeper Cornall Lake No not anoxic same Dark Lake No not anoxic same Devine Lake No anoxic deeper Echo Lake No anoxic deeper Fawn Lake Yes anoxic less deep Foote Lake No not anoxic deeper Fox Lake No not anoxic deeper Gibson Lake - North yes anoxic same Gibson Lake - South yes anoxic same Gullwing Lake yes anoxic same Healey Lake yes anoxic same Hesners Lake Maybe 7 7 anoxic later Lake Muskoka - Muskoka B No not anoxic deeper, later Lake Vernon - Hunters B Yes anoxic less deep Leech Lake No anoxic deeper McRey Lake No anoxic deeper, later Medora Lake No not anoxic same Menominee Lake No not anoxic later Moot Lake Maybe anoxic later Neilson Lake Yes anoxic less deep Nutt Lake No not anoxic same Otter Lake Yes anoxic same Ricketts Lake No not anoxic same Ril Lake No not anoxic later Rose Lake Maybe anoxic deeper Ryde Lake No anoxic deeper, later Silversands Lake No not anoxic same Sixteen Mile Lake No not anoxic later Spence Lake - North No anoxic less deep Three Mile Lake GR No not anoxic same Wood Lake Yes anoxic same R _150074_MWQMLSH_final.docx 24

61 Area (ha) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m 4.2 Relationships of Bathymetry with Oxygen Status There are ~340 lakes in the water quality model that are not monitored and for which oxygen status must therefore be estimated. We examined the set of monitored lakes to see if there are aspects of hypolimnetic DO that can be inferred by either lake size (surface area), lake depth, or a combination of the two, and which could then be used to estimate oxygen status. There was a statistically significant relationship between the size of the lake and its depth for the entire dataset. The relationship between size and depth was still significant (adjusted r 2 = 0.35, d.f. 184, p <0.001, Figure 11) following exclusion of the three large Muskoka Lakes (Lakes Muskoka, Rosseau and Joseph, top right on Figure 10), but of poor explanatory power (adjusted r 2 = 0.35). Figure 8. Lake area and depth relationship for Muskoka lakes. (n=188) Figure 9. Lake area and depth relationship for all lakes except three large lakes (Lakes Muskoka, Rosseau and Joseph) (n=185) R² = Depth (m) R _150074_MWQMLSH_final.docx 25

62 Area (ha) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m There was an almost even mix of oxic and anoxic hypolimnia in lakes over the range of lake size and depth (Figure 12). Very generally, the deepest lakes (>30 m) tended to have less chance of being anoxic and larger lakes (>400 ha) tended to be anoxic if they were less than 20m deep and oxic if they were deeper than 20m. These generalizations are based on a small number of lakes and these physical properties are often unique to lakes in a way that can only become clear once the lake is examined more closely. The large shallow anoxic lake on the graph with a depth of 16 m and an area of 1000 ha, for example, is Sparrow Lake. It is unique in being the only large shallow mesotrophic off-shield lake in the dataset. Figure 10. Lake area as a function of depth for lakes with oxic and anoxic hypolimnia Oxic anoxic Depth (m) The oxygen status of smaller lakes is of most interest for modeling because the majority of lakes in the DMM dataset without measured hypolimnetic oxygen data are small. We set the size cut-off at 100 ha to examine these same relationships for smaller lakes. In this dataset there was an almost equal mix of anoxic and oxic lakes over a 1- to 40-m depth range ( Figure 11). All lakes shallower than 3 m were oxic although there were only three of these. In lakes >3 m deep there was no clear pattern based on size or depth that would indicate whether or not the lake would have an anoxic hypolimnion. Lakes between ~8-10 m deep tend to be anoxic and lakes <5 m deep tend to be oxic. The mix in between may delineate differences in mixing regimes as 6 m represents the approximate depth at which stratification begins in Muskoka Lakes. It was not, however, possible to predict hypolimnetic oxygen status reliably from lake morphometry. R _150074_MWQMLSH_final.docx 26

63 Area (ha) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 11. Lake area as a function of depth for small (<100 ha) lakes with oxic and anoxic hypolimnia Oxic Anoxic Depth (m) 4.3 Relationships of TP and DOC with Oxygen Status Two analyses were completed to assess if the occurrence of anoxia could be predicted from one or several morphometric and chemical lake characteristics: ordination and multiple linear regression. DOC and TP are related (see section 3.3) and higher TP concentrations generally indicate lower hypolimnetic DO. Measured DOC or TP concentrations were therefore tested to determine if they could indicate whether or not lakes are anoxic. Shallow lakes (depth 3 m) were removed from the analysis because these lakes are usually wellmixed and would not respond to TP or DOC concentrations resulting from deepwater anoxia Ordination Analysis A Principal Components Analysis (PCA) was carried out in order to display the general distribution of lake characteristics in the data set. PCA is a descriptive technique that shows the statistical similarities and differences between lakes for a number of variables in one figure: Lakes are displayed as points on the plot. Lakes that plot close together are similar based on the data provided; lakes that plot far from each other are most different. Lake characteristics (or variables) are displayed as arrows; o The length of the arrows is proportional to the strength of that variable in describing variance in the lakes. o Lakes that plot at the tip of the variable arrow have a high value for that variable and lakes that plot on the opposite side of the figure (back of arrow) have a low value for that variable. o Variables for which the arrows point in the same or opposite direction are correlated, variables for which the arrows point in directions perpendicular to each other are not correlated. R _150074_MWQMLSH_final.docx 27

64 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m The PCA plot for the lake data (Figure 12 Figure 12) shows that: The lakes on the left are the oxic lakes; the lakes on the right are the anoxic lakes. There is a large variety of depths, areas and DOC concentrations throughout both the anoxic and oxic lake data sets; therefore DOC, depth and area are not correlated with anoxia. Lakes with higher TP have the tendency to be anoxic (TP is correlated with anoxia), but there are many lakes that do not follow this generalization. There are high TP lakes that are oxic: e.g., Clark, Haggart, Axle and there are low TP lakes that are anoxic: Flatrock, Bonnie, High Lake. There is no clear separation between anoxic and oxic lakes based on one or a combination of other known lake characteristics. Figure 12. Principal Component Analysis plot of morphometric and chemical characteristics for lakes that are more than 3 m deep Multiple Regression Analysis A multiple regression analysis was also completed in order to assess the relationship between lake variables and anoxia in quantitative terms. Not all assumptions regarding data distributions for regression were met as R _150074_MWQMLSH_final.docx 28

65 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m the variable anoxia is a binary variable, that can only have two values (oxic/anoxic; here set as 0/1). Regression analysis requires a normal, or random distribution of continuous data that can, in theory, have any value, such as the DOC or TP data. The results of the Multiple Regression are displayed in Error! Reference source not found.. The only variable hat explained a significant portion of variability in the anoxia data was TP (p< ). All other variables were not significantly related to anoxia. These data confirm results presented in the previous section that known variables of unmonitored lakes, such as area, cannot be used to predict oxygen status. The only variable with predictive potential is TP, but lakes with TP data are also being monitored for oxygen and therefore do not require the use of TP data to predict oxygen status. Table 11. Multiple Regression Statistics of Anoxia predicted by Depth, Area, DOC and TP Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 74 Coefficients Standard Error t Stat P-value Intercept Depth Area DOC TP If a predictive equation was sought to assess oxic status based on TP, the coefficients resulting from a bivariate regression would have to be used. The relationship of TP to oxic status is less strong (p = 0.02) and the resultant regression equation to predict anoxia is: Anoxia = * TP If TP is in the range of the lakes used to develop this regression, Y will be a value between 0 and 1 or somewhat higher, with values close to 1 indicating anoxia and values close to 0 indicating oxic conditions. For values between 0.3 and 0.8, the oxygen status of the lake is much more uncertain. Clearly, there is a lot of uncertainty associated with this equation and the statistical assumptions underlying the regressions are not met; therefore this equation should not be used to calculate if a lake is anoxic or not. It may, however, serve as an initial screening tool in a large dataset. Using the relationship between measured DO concentrations in the bottom R _150074_MWQMLSH_final.docx 29

66 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m waters of all lakes and the measured TP concentrations may improve the predictive capabilities of the regression. 4.4 Literature Review on Phosphorus Retention and Internal Load Underestimation of the ice-free total phosphorus concentration (TPlake) in small lakes by the LCM may be due to overestimating phosphorus retention (RP, the loss of phosphorus from the water column to the sediments 9 ), which in turn is strongly dependent on the settling velocity (v) of phosphorus. Alternatively, the phosphorus export coefficients used by the model may represent the catchment loadings to smaller lakes less accurately than for larger ones. It is more likely that the model underestimates TPlake in small lakes for either of two distinct reasons: 1) small lakes are more susceptible to developing anoxic hypolimnia, in which internal loading of TP occurs (i.e., a flux of phosphorus from the sediments into the water column), or 2) some lakes are polymictic, that is, they can experience complete mixing of the water column during the ice-free period. This mixing can result in sediment resuspension, which temporarily increases TPlake until the resuspended particles have time to settle back to the lake bottom. The retention coefficient for phosphorus in the water quality model, RP, is calculated as: RP = v / (v + qs), where qs is the areal water load (outflow discharge / lake area) and ѵ is the settling velocity for phosphorus. Lakes with long water residence times (Ƭw), and therefore relatively low qs, will retain a greater proportion of the phosphorus that enters the lake (higher RP) than lakes that flush quickly (high qs, lower RP). The higher the settling velocity, v, the greater RP will be for a given value of qs. The process can be visualized by imagining a particle making its way to the outflow of a lake and settling towards the bottom at the same time. If the particle settles slowly (low v) then it may exit through the outflow before it settles to the bottom and not be retained. If the particle settles quickly (high v), it may contact the sediments before it reaches the outflow, and therefore be retained. Accurate determination of v for a particular lake is a complex exercise, given its dependence on the particle size and density distributions, presence/absence of phytoplankton motility or buoyancy control, the density (i.e. temperature) of the water, and vertical water column mixing dynamics and other hydrologic factors. A meta-analysis of 664 lakes, mostly from northern, temperate regions, found that phosphorus settling velocities increased with increasing TPlake, up to ~30 g/l TP, above which ѵ declines (W.D. Taylor, Univ. Of Waterloo, pers. comm.). This trend of an increasing v with increasing TPlake below 30 g/l likely reflects the increasing phytoplankton cell size (picoplankton to nanoplankton to microplankton) with increasing TPlake. However, as TPlake increases beyond 30 μg/l, larger numbers of cyanobacteria act to decrease v, as many of these taxa exhibit positive buoyancy control that would slow the settling of phosphorus to the lake bottom. In summary, accurate determination of ѵ on a lake-specific basis would be a complex undertaking, especially since v is partly a function of TPlake, which is the variable that is ultimately being predicted by the model. A constant is used for v in the LCM due to the complexity of estimating it on a lake-specific basis. The model is currently calibrated with v = 12.4 m/yr for stratified lakes with oxic hypolimnia (Paterson et al. 2006). Other 9 Phosphorus can also be lost from the main basin by settling and uptake into the littoral zone, or to higher trophic levels, such as fish. Dillon et al (1986), however, found that loss of phosphorus by removal of fish by angling was not a significant factor in the lake phosphorus budget. R _150074_MWQMLSH_final.docx 30

67 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m settling velocities have been used in the literature, ranging from 5 to 73 m/yr (e.g., Chapra & Tarapchak 1976, Thomann & Mueller 1987, Chapra 1997, Reinhard et al. 2005). For lakes that are either polymictic (prone to sediment resuspension) or have anoxic hypolimnia (internal P loading), a lower value of v can be used to decrease RP, and increase TPlake, improving the predictive accuracy of the model. The model uses v = 7.2 m/yr for anoxic lakes (Paterson et al. 2006). Regrettably, data on lake depths and surface areas are needed to assess whether a lake is prone to sediment resuspension, and data on hypolimnetic oxygen concentrations or some proxy are needed to determine whether a lake experiences internal loading. Rather than using a lower value of v to compensate for sediment resuspension or internal loading, a more mechanistic approach is to leave v as a constant and explicitly model the internal loading or sediment resuspension processes in a way that acts to decrease RP. According to Nϋrnberg et al. (2009), the internal phosphorus loading rate can be quantified: by in situ determination of hypolimnetic TP increases (i.e. direct measurement), from net estimates from complete P budgets (i.e., a mass balance approach), or from gross estimates calculated as the product of the anoxic areal TP release rate and the number of days of hypolimnetic anoxia. None of these approaches, however, are useful in the current context due to their data requirements. The first method requires information on the timing of anoxia and associated values of TPlake, the second method requires that an accurate value for ѵ is known, and the third method requires data on areal sediment TP release rates and oxygen concentrations. Brett and Benjamin (2008) conducted a statistical reassessment of mass balance TP models and found that the best fit to observed data was obtained by estimating the coefficient for TP loss from the lake per year (σ) as an inverse function of the lake s water residence time (Ƭw), where Ƭw = V/Q. This cannot be applied here, as the database for the Muskoka lakes does not include Ƭw data, nor does it allow us to calculate Ƭw because there are no lake volume data for most lakes (or mean depths, which could be used to calculate volumes with the available surface area data). At present, MOE guidance (A. Paterson, pers. comm.) is to assess all lakes (in cases where anoxia cannot be confirmed) initially with the oxic settling coefficient of v = 12.4 m/yr. If the model underpredicts TPlake for a subset of lakes, then a lower settling coefficient for anoxic lakes (v = 7.2 m/yr) (Dillon et al. 1994) should be used to see if the model fit improves for those lakes. There is one additional consideration with respect to phosphorus retention. Lakes in which the water retention time (Ƭ w ) is extremely short would not retain phosphorus as water would be replaced faster than phosphorus could settle. Brett and Benjamin s (2008) assessment of a large North American dataset indicate that this would begin to occur in lakes with Ƭ w <1 month although there is considerable scatter between the results observed for individual lakes. There may, however, be merit in cases where retention times are extremely short to use a retention factor of 0 if the model is still underestimating with the use of the anoxic coefficient. If model fit is improved to within 20% of measured values then this would be a preferred approach. In summary, two distinct processes (sediment resuspension and internal loading) can act to increase TPlake. The effects are of particular importance in small, relatively shallow lakes for which the model generally R _150074_MWQMLSH_final.docx 31

68 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m underestimates TPlake. These processes could be accounted for in the model to increase predictive accuracy, but the necessary data (dissolved oxygen concentrations or lake depths and volumes) are not presently available for a large number of lakes and cannot be predicted based on known lake characteristics. 4.5 Summary The recycling of phosphorus from sediments (internal load) in anoxic lake bottom waters (hypolimnia) and other factors that affect phosphorus retention in a lake represent one of the largest uncertainties in the current DMM water quality model and lakeshore capacity models in use elsewhere. In this chapter we explored a few options to improve the DMM model s capacity to predict phosphorus retention for monitored and unmonitored lakes. We recommended collection of hypolimnetic TP data for a group of lakes that are most likely to display anoxic conditions by late summer. These data were intended to confirm whether observed anoxia actually results in significant phosphorus recycling from the sediments and therefore needs to be considered in the model. Many anoxic lakes (60%) sampled in mid- to late-summer 2011 did not show elevated phosphorus in the hypolimnion. Although these results were in part due to too shallow sampling depths, they did demonstrate that some of the anoxic lakes should be modeled as if they were oxic, as there was no evidence of significant internal phosphorus loading. A repeat late-summer sampling of hypolimnetic phosphorus in the same lakes is recommended with improved sampling procedure for a number of lakes. We analyzed available bathymetry and lake area data to explore any relationships between oxygen status and lake morphometry that could be used to predict oxygen status in un-monitored lakes. Some general statements can be made, with shallow (<3 m) and most very deep (>30 m) lakes being oxic. There was, however, a lot of variation in the relationships, especially among the small lakes that represent the majority of un-monitored lakes in the DMM model. We concluded that hypolimnetic oxygen status is not related to depth or area in Muskoka Lakes in any reproducible way that could currently be used for modeling lake phosphorus concentrations. We found expected relationships of oxygen status with TP concentrations, but not to DOC. This confirms the general scientific understanding that high TP lakes are more likely to have anoxic hypolimnia, given that they display higher productivity of algae, which when they settle to the bottom waters, cause oxygen depletion through decomposition. While DOC is correlated with TP, it does not show the same relationship with oxygen. This is likely in part due to light inhibition of algal productivity by the coloured substances found in DOC and the fact that the phosphorus associated with DOC is not readily available for algal uptake. A literature review of factors affecting phosphorus retention in lakes in general and phosphorus recycling from sediments under anoxic conditions in particular demonstrated that the DMM water quality model incorporates the most current state of knowledge for recreational water quality modeling. There is a more thorough understanding of processes governing phosphorus retention in lakes than is included in the model, but in order to incorporate these processes, data are required that are currently not available and not easy to obtain. We also note that use of the LCM in a whole watershed context is best used as a screening tool. Ideally, each of the 500+ lakes in the DMM would be the subject of a focussed monitoring effort and a lake-specific model and management plan developed, but this would require a large investment of resources to assess if the effort resulted in improved model prediction. Recreational water quality modeling is an evolving science and the R _150074_MWQMLSH_final.docx 32

69 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m dynamics of Muskoka s lakes are changing in response to a warming climate. The means to address this uncertainty in the LSH program are presented in Section Embayments 5.1 Lakes Joseph, Rosseau, Muskoka and Lake of Bays Embayments The four large lakes Joseph, Rosseau, Muskoka and Lake of Bays are the oldest and most well known destinations of Muskoka and therefore have a long history of development. Their shapes are highly irregular with a large number of bays, peninsulas and islands, which result in a number of separate embayments that often differ in the type and degree of shoreline development. In some cases, the water exchange between these embayments and the main basins is limited and therefore water quality in the embayments can be different due to local watershed and bay characteristics. For these reasons, some embayments are modeled separately in the DMM water quality model, i.e., they are treated as if they were separate lakes. The Muskoka Lakes Association and Lake of Bays Association have managed their own monitoring programs since 2000 and requested that the DMM review the status of several embayments based on their results, to determine if they should be modelled separately or not. The degree of separation and difference of an embayment from the main lake basin varies considerably among embayments, and no clear rationale has been developed to decide if an embayment warrants separate modeling. In addition, changes in land use since the initial model setup may have lead to more or less pronounced differences between certain embayments and the respective main basins. The following assessment therefore: Developed criteria for determining which embayments should be modeled separately from the main basins, Determined if those embayments that are currently being modeled separately meet the above criteria, and Proposed additional embayments that should be modeled Approach to Embayment Criteria Freshwater Research (1997) proposed criteria that would justify modeling embayments as separate distinct basins, including: Size: only large basins should be investigated separately, Morphometric differences (e.g., difference in depth or the presence of an obvious ridge or sill separating an embayment from the main basin). Different water quality (e.g., total phosphorus (TP), chlorophyll a or Secchi depth long-term averages), and Differences in development. These criteria are relevant for the large Muskoka lakes, and were adopted in the approach to identify embayments that should be modeled separately, but with some modifications and refinements as described below: R _150074_MWQMLSH_final.docx 33

70 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Size has to be considered for embayments but the qualifier large basins is insufficient as a criterion as small embayments may also be very different from the main basin. It is difficult to justify why small embayments should receive less priority than a small lake in the model. We therefore used the the same minimum size criterion for embayment as that used for lakes in the model (8 ha). Depth can determine if an embayment behaves differently than the main basin. A shallow embayment, for example, is likely to have different water quality than the deep main basin, as it may have different mixing patterns (conitinuously mixed as opposed to stratified in summer), or a smaller volume to assimilate watershed inputs. Morphometric separation is a major factor influencing the degree of water exchange that occurs between an embayment and the main basin of a lake. The embayment can be separated by a shallow area at the embayment mouth, by a narrow mouth or by a long distance from the centre of the embayment to the main basin. Land use differences can explain why an embayment has different water quality than the main basin. The purpose of modeling embayments is to have a planning tool in place that allows managing development at the embayment level to protect the local water quality in the embayment. Differences in land use, however, cannot alone justify modeling an embayment; they will only influence local water quality if water exchange with the main lake is limited. Water quality differences are the most direct indication that an embayment is significantly different from the main basin. We focussed on TP, as it is the variable used by the DMM to measure, model and manage water quality and also offers the most comprehensive long-term data set for embayments in large Muskoka lakes. We used statistical comparisons of all available data pairs for each year to compare long-term means. This allowed detecting differences despite inter-annual variations or temporal trends in the data. River influence can result in different water quality in an embayment if the river provides large volumes of water with different water quality. Embayments receiving river water usually have a very high water renewal rate and therefore the effect of shoreline development may be overwhelmed by the input of large volumes of water of differing water quality. Although these embayments may display differing water quality from the main basin the inflows are the cause and not shoreline development. The intent of the DMM program is to provde guidance for management of shoreline development. The influence of shoreline development on a riverine embayment is minimal, however, and so management would not be improved, and the additional complexity in modelling a riverine embayment not warranted. We did not, therefore, consider riverine inputs as a criterion for modelling embayments separately. Differences in the measured water quality between sites was therefore considered the most important criterion for selecting embayments for modeling, in agreement with the approach by Nürnberg (1998). TP is precisely measurable and can help determine if land use or morphometric differences have significantly affected water quality in an embayment differently than in the main basin. Morphometric and land use criteria can provide supporting information in cases where water quality differences are subtle, and help to decide if separate modeling is justified. R _150074_MWQMLSH_final.docx 34

71 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Data Sources Available datasets collected by the DMM, the Lake of Bays Association (LOBA) and the Muskoka Lakes Association (MLA) were used to assess statistical differences in TP concentrations between main basin stations and embayments in the four large Muskoka lakes. Only open-water embayment TP data were used from the MLA dataset for analysis; data from nearshore stations were excluded. This selection resulted in a total number of 32 embayments and three main basin sites. The number of measurements available per site ranged from 3 at Arundel Lodge and Stephens Bay to over 70 at Muskoka Bay, Beaumaris and Hamer Bay ( Figure 13). Nautical charts (Fisheries and Oceans Canada 1995) were used to assess depth differences, morphometric separation and river influence, and embayment-specific landuse information for lakes Muskoka, Joseph and Rosseau was obtained from the MLA 2011 annual report (Riverstone 2012) Quality Control of Data The MLA dataset was screened using a number of quality control procedures to ensure that the data were comparable to DMM data and representative of the overall water quality conditions at the sites. Quality control procedures included the detection and removal of outliers, including bad split measurements in duplicate samples, as well as comparison of data between the DMM and MLA datasets, where data were available from the same sites and years Outliers and Bad Splits Contamination with particles, such as large zooplankton, can lead to unusually high TP values ( outliers ) that are not representative for the overall water quality of a lake. This is particularly true for samples that have not been filtered prior to analysis, such as the samples collected by the MLA prior to Screening through a 80 µm mesh is the standard method in DMM monitoring and was adopted by the MLA in Spurious values can also be the result of contamination during sample collection in the field, laboratory analytical errors, or data transcription mistakes. In order to avoid using such data in our analysis, we applied procedures to detect and remove outliers. First, disagreement between duplicate samples, or bad splits was identified if both of the following conditions were met: 1) the absolute difference between duplicates was >4 μg/l, and 2) this difference was >40% of the lower TP value. If these conditions were met for a pair of duplicate measurements, the higher of the two values was assumed to be contaminated and discarded from the analysis. Second, the entire data set from each site ( ) was evaluated to detect outliers. Common outlier tests that were previously recommended for the District of Muskoka dataset, such as the Dixon s Q and Grubbs tests (Gartner Lee Limited 2009), are designed to detect a single outlier in a dataset or can be modified to detect multiple outliers in datasets with more than 24 data points. In the case of the MLA dataset, however, multiple outliers were suspected in relatively small datasets which required use of an alternative method. Outliers were identified based on differences from the inter-quartile range (IQR; the difference between the 25 th and 75 th percentiles) of the dataset (Tukey 1977). TP values that were more than 150% above or below the IQR were considered to be outliers. A total of 75 outliers were detected in the MLA dataset including 17 R _150074_MWQMLSH_final.docx 35

72 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m in Lake Joseph, 23 in Lake Rosseau, and 35 in Lake Muskoka, and were excluded from the analysis (Appendix D). Figure 13. Number of total phosphorus measurements at MLA open-water stations used for embayment analysis. R _150074_MWQMLSH_final.docx 36

73 MLA TP ( g/l) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Data Comparison Muskoka Lakes Association vs. District Municipality of Muskoka The following sites were monitored by both the MLA and DMM during : Little Lake Joseph, Lake Joseph (main basin, Cox Bay, Hamer Bay), Lake Muskoka (main basin, Bala Bay, Dudley Bay, Muskoka Bay, Whiteside Bay), and Lake Rosseau (main basin, Brackenrig Bay, East Portage Bay, Skeleton Bay). Annual average values were calculated for comparison as the sampling dates were not consistent among the MLA and DMM sampling programs, and were then compared for each site using paired t-tests (for normally distributed data) and the Wilcoxon Signed Rank Test (for non-normally distributed data). The entire MLA dataset including all lakes and bays was also compared to the entire DMM dataset using the Wilcoxon Signed Rank Test for a paired comparison. The same analyses were carried out on a reduced MLA dataset that only included spring overturn samples, i.e., any samples taken before June 10 th in any given year. The MLA and DMM TP values were significantly correlated when the annual means from all shared stations were pooled (Figure 14). The DMM values were significantly greater than the MLA values (p = 0.04; df = 51) by an average of 0.4 μg/l. The DMM TP values were higher than the MLA values in 10 of the 13 shared sampling locations, but the only significant differences on a station-specific basis were in the main basin (p = 0.02) and East Portage Bay (p = 0.04) of Lake Rosseau (Figure 14). Figure 14. Relationship between MLA annual average TP ( ) and DMM average spring TP in main basins and embayments of lakes Joseph, Muskoka, and Rosseau. The dashed line is the 1:1 line (y=x) y = 0.67x R² = 0.53 p < DMM TP (ug/l) R _150074_MWQMLSH_final.docx 37

74 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 12. Comparison of Average TP Concentrations Collected by Both the DMM and MLA Lake Basin/Embayment Mean TP (μg/l) in Paired Test Statistics DMM MLA p df* Lake Joseph main basin Cox Bay Hamer Bay Little Lake Joseph main basin Lake Muskoka main basin Bala Bay Dudley Bay Muskoka Bay Whiteside Bay Lake Rosseau main basin Brackenrig Bay East Portage Bay Skeleton Bay** Notes: Bold values indicate significant difference at p<0.10. *The degrees of freedom (df) for this analysis equal the number of data pairs minus one. **All data from Skeleton Bay were from one year, so no multi-year comparison was possible. Higher TP for the DMM dataset may be due to the fact that samples were collected during spring overturn (i.e., the period of complete vertical mixing which precedes thermal stratification of the water column), There is a natural seasonal pattern of higher spring TP and lower summer TP due to consumption of algae by zooplankton and losses of suspended particles to the lake bottom via sedimentation during the summer months. This is supported by the lack of significant differences between the datasets when comparing average spring phosphorus data only (Table 13, Figure 15). Shorter term (days to weeks) temporal variation in TP concentrations may also explain some of the differences between the two datasets. Differences in sample collection methods among agencies or individual samplers, could also have contributed to the observed differences. A primary concern regarding the MLA data was that sample contamination by particles due to lack of coarse filtering prior to 2011 could have caused inaccurately high TP values. Overall, analyses indicate that the MLA total phosphorus data, after removal of outliers, are comparable to those collected by the DMM. The large amount of embayment data collected by the MLA can therefore be used with confidence to assess differences between main basin locations and embayments in lakes Muskoka, Rosseau, and Joseph. R _150074_MWQMLSH_final.docx 38

75 MLA TP (ug/l) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 13. Average Spring TP Concentrations (May-early June) Collected by the DMM and MLA Lake Basin/Embayment Mean TP (μg/l) in Paired Test Statistics DMM MLA p df Lake Joseph main basin Cox Bay Hamer Bay Little Lake Joseph main basin Lake Muskoka main basin Bala Bay Dudley Bay Muskoka Bay Whiteside Bay Lake Rosseau main basin Brackenrig Bay East Portage Bay Skeleton Bay Figure 15. Relationship between average MLA and DMM spring TP concentrations ( ) in main basin and embayments of Lakes Joseph, Muskoka, and Rosseau :1 (x=y) y = 0.40x R² = 0.25 p < DMM TP (ug/l) R _150074_MWQMLSH_final.docx 39

76 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Main Basin vs. Embayment Comparisons Data analysis Paired statistical comparisons of embayment TP values were made with main basin values to control for intrastation temporal variability in TP concentrations. Paired t-tests were used for the normally distributed data and the Wilcoxon signed-rank test was used for the non-normal data. Normality was tested using the Shapiro- Wilk test. For the MLA dataset, data for the period were used, but the majority of the main-basin TP data were collected during for lakes Muskoka and Rosseau (87% and 86%, respectively), while more data were available for for Lake Joseph (53% of all years). No main-basin TP data were collected prior to 2005 in these three lakes. For Lake of Bays, the data we analysed were provided by the Lake of Bays Association (LOBA) for As its name suggests, Lake of Bays does not have a single, distinct main basin. Main basin TP data for this lake were therefore calculated as averages of the Bigwin East, Gull Rock, and Fairview stations, which are most representative of open-water conditions. The MLA TP data were collected according to a different method in There were insufficient data to test differences in TP among main-basin and embayment stations for the year 2011 alone, but the paired comparison tests were repeated with the 2011 data removed. This had a generally minor effect on the significance levels of the tests. We therefore present results of the entire dataset in the next section. Shapiro-Wilk tests, Student s T tests and Wilcoxon signed-rank tests were all performed using the R statistical package (R Development Core Team 2008) Results A total of 13 embayments had significantly higher TP concentration than the main basin of the lake. (Table 14; Figures 19-22). Seven of these embayments were included as separate basins in the 2005 DMM water quality model (Table 13). Differences in dissolved organic carbon (DOC) content may partly explain some of the differences between the embayment TP concentrations and those of the main lake basins. Muskoka Bay (DOC = 4.6 mg/l) and Brackenrig Bay (3.6 mg/l) both had higher levels of DOC (as averages) than their main basins in Lake Muskoka (3.9 mg/l) and Lake Rosseau (3.1 mg/l), respectively. On the other hand, Cox Bay and Lake Joseph did not differ in DOC (both 2.7 mg/l), and it is unlikely that the 0.5 mg/l difference in DOC between Brackenrig Bay and Lake Rosseau could fully explain the 4.9 mg/l difference in their TP concentrations. Differences between main basins and embayments were generally more pronounced when calculated using annual average data collected by the MLA than when calculated using spring data from either DMM or MLA (Table 14). This suggests that water quality in embayments changes throughout the season while that of the main basins is more stable. This pattern is expected as sheltered bays with small water volumes and little water exchange will be affected by inflow from creeks and overland flow while the open water is a large volume of water that buffers any small influences from the watershed. R _150074_MWQMLSH_final.docx 40

77 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 14. Comparison of Main Basin and Embayment TP and Physical Characteristics. Bold values are significant at p <0.05. Lake Lake Joseph (JOS-1) (240 ft) Lake Muskoka (MUS-3) 170 ft Lake Rosseau (ROS-1) 290 ft Lake of Bays 200 ft nficance of Difference (Main - B Bay-specific Characteristics Embayment df Mean diff. Mean Diff. River Depth bathymetry land use (MLA/LOBA) (DMM) Influence (ft) barrier Currently Modeled (2005)? Modeling Modeling Recommended? Cox Bay narrows, large Small 38 distance Golf Course Yes Yes Foot s Bay drainage from No 120 minor narrows GC No no Gordon Bay Small 114 narrows - No no Hamer Bay no, not DMM, not different No 150 No GC, wetland No enough; BUT: Trends? Little Lake Joseph Small 127 narrows, large distance 3 small wetlands Yes Yes Still s Bay Small 30 no GC, wetlands No Yes Stanley Bay Small 160 no - No No Arundle Lodge No 40 distance wetlands No No Bala Bay narrows, large 2 wetlands, No 80 distance village Yes Yes Beaumaris No 90 no GC No No Boyd Bay Small 24 minor narrows wetland No No, small and open to lake Dudley Bay No 60 narrows wetlands Yes Yes, model already set up East Bay No 50 no wetlands No No Eilean Gowan Island No 80 no - No No, open to lake Muskoka Bay shallow No 40 narrows urban Yes Yes Muskoka Sands GC, high Medium 40 no density residential No no, open to lake North Bay No 60 narrows - Yes Yes, model already set up Stephen s Bay No 40 no - No No Walker s Point No 30 no - No No Willow Beach No 50 no - No No Whiteside Bay No 30 narrows Wetland Yes Yes, large bay, model already set up Arthurlie Bay No 23 No wetlands No No shallow 60% cleared, Brackenrig Bay No 14 narrows Ag Yes Yes Morgan Bay 0 - Small 80 distance - Yes Yes, model already set up Minett No 40 No 2 GCs, wetland No No Muskoka Lakes Golf & Country Club No 30 No GC No No East Portage Bay No 40 open narrows roads, Ag Yes Yes Royal Muskoka Island No 120 No - No no Rosseau North Large 290 No - No no Skeleton Bay Medium 65 shallow narrows road Yes Yes Tobin s Island north of 3- Mile Lake Inflow 90 several narrows wetlands No No, too open Windermere south of 3- Mile Lake Inflow 80 several narrows GC, village, Ag No No, too open Dwight Bay Haystack Bay Large 170 narrows - No No. Difference due to river influence (regulating local sources not effective) No narrows - Yes Yes Ten Mile Bay Small 80 distance - Yes Yes, model already set up South Portage Bay small 155 shallow narrows Yes Yes, model already set up, difference ecologically significant R _150074_MWQMLSH_final.docx 41

78 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 16. Map of Median Total Phosphorus Concentrations at MLA Open Water Sampling Stations in Lakes Muskoka, Rosseau and Joseph ( ). R _150074_MWQMLSH_final.docx 42

79 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 17. TP concentrations in Lake Joseph and its embayments ( ; MLA data). Figure 18. TP concentrations in Lake Muskoka and its embayments ( ; MLA data). TP (ug/l) TP (ug/l) JOS COX FTB GNB HMB LLJ STI STN MUS ARN BAL BMR BOY DUD EAS ELG MBA MSN NRT STE WAK WLB WTS R _150074_MWQMLSH_final.docx 43

80 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 19. TP concentrations in Lake Rosseau and its embayments ( ; MLA data). Figure 20. TP concentrations in Lake of Bays and its embayments ( ; LOBA data). TP (ug/l) TP (ug/l) ROS ART BRA MGN MIN MLG POR RMI RSH SKB TOB WIN Open Water Dwight Bay Haystack Bay Ten Mile Bay Trading Bay Notes: The Open Water basin includes data from the Bigwin East, Fairview, and Gull Rock stations. R _150074_MWQMLSH_final.docx 44

81 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Recommendations for Modeling TP and Morphometric Barriers There are a number of different combinations of embayment criteria that are met for the studied embayments in the large lakes of Muskoka. These are classified into five categories with recommendations for modeling as follows: Different TP and/or clear morphometric barrier There are eight embayments with significantly different TP concentrations that are also well separated from the main basin by a clear morphometric barrier. These include Cox Bay, Brackenrig Bay, Muskoka Bay, Haystack Bay, Skeleton Bay, Little Lake Joseph, Bala Bay and East Portage Bay. All of these embayments were included in the 2005 model, and it is recommended that they continue to be modeled as separate basins. Different TP and limited morphometric barrier There are four sites with significantly different water quality and limited bathymetric barriers: Stills Bay, Boyd Bay, Tobins Island and Windermere. Stills Bay and Boyd Bay are both much shallower than the respective main basins, with maximum depths shallower than 10 m as opposed to main basin depths of ~60 m (Lake Muskoka) and 80 m (Lake Joseph). Stills Bay is a long, narrow bay with an open mouth leading into Foot s Bay, and Boyd Bay is separated from the main lake by a wider channel that potentially allows more water exchange with the main basin than those of the embayments discussed above. It is recommended that these embayments be included in the model given that they have different water quality and meet the minimum size criterion of 8 ha. Tobins Island and Windermere embayments are located at the eastern end of Lake Rosseau and both exhibit higher TP concentrations than the main basin. These locations are part of the eastern basin of Lake Rosseau, which is separated from the monitored main basin by a number of narrows and combines with the outflow from the main basin to form the Indian River at Port Carling. The Dee River discharges into Lake Rosseau in this area and may have some influence on the water quality, as it drains Three Mile Lake, a relatively nutrient-rich lake with a 10-year average TP concentration of 22 µg/l. In addition, local land use and creek drainage in Windermere could affect that station s water quality. Given the connectivity of Tobin Island and Windermere sites, it is recommended that these embayments be modelled together as a new embayment called Rosseau East. Different TP and no morphometric barrier There was one embayment, Eilean Gowan Island, that had significantly different water total phosphorus concentration than the main basin (north bay of Lake Muskoka), but which did not have bathymetric barriers to the main basin. For this location, run-off from local land uses may have caused changes in open-water quality. This scenario is unlikely as this station is open to the main north bay of Lake Muskoka where water exchange with the main basin constantly occurs. R _150074_MWQMLSH_final.docx 45

82 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Overall, sites in this category are open to the water and the extent of land-use effects on them is very difficult to delimit; therefore, modeling these sites is not practical nor is it justified by the embayment criteria developed above. No difference in TP but significant bathymetric barrier Many of the currently modeled embayments did not differ significantly in TP concentrations despite the presence of bathymetric barriers. These embayments included: Bala Bay, Dudley Bay, Whiteside Bay, Morgan Bay, Ten Mile Bay, South Portage Bay, Rat Bay and Trading Bay. It is recommended that these embayments continue to be modeled as separate basins as they have potential to respond differently to future changes in land use than the main basins. Modeling embayments of this category can help to pro-actively manage these bays. It will require up-front effort, but will assess and prevent any undesired water quality effects from future shoreline development and can help support ongoing stewardship initiatives. An alternative approach would be to include new bays into the model only if they have significantly different water quality (TP) compared to the main basin. This reactive approach would entail continued monitoring and review of the data during the next model revision to assess if water quality has changed since the last assessment of model performance. This approach has the advantage of saving resources upfront in terms of modeling and managing additional bays. Under a worst-case scenario this may, however, allow development on the shores of an embayment that may deteriorate water quality, which would then trigger modeling. If the bay is found to be above threshold at that point, however, it may be difficult to return to nutrient levels below threshold with the development in place. No difference in TP and no bathymetry barrier All the remaining embayments fall into this category. These embayments are likely to have regular water exchange with the main basin and therefore can be included as part of the main basin in the water quality model. One area of concern, however, is the northern part of Lake Rosseau, where both DMM and MLA monitoring data show TP concentrations that are higher than that of the main basin and exceed the modeled threshold value of Background + 50%. Due to these concerns, and with consultation with the DMM and MLA, it is recommended that Rosseau North be included as a separate basin in the model with the southern limit of the basin at Royal Muskoka Island. The current MLA sampling site at Royal Muskoka Island therefore should also be modeled separately from the main basin Seasonal Patterns and their Significance for Modeling Most of the described water quality differences between embayments and open basins occurred in summer only and were not detected in spring data from the DMM and MLA, except the three highly significant differences across seasons in Cox, Muskoka, and Brackenrig Bays. Given that the model is based on spring overturn TP, one could argue that summer differences are not relevant for the model. On the other hand, differences in summer show that embayment water quality behaves differently than the main bay and that alone should warrant separate modeling. The seasonal differences also suggest that local watershed characteristics may be an important factor in determining the water quality in these bays. Lakes in the Muskoka area naturally exhibit somewhat lower TP concentrations during the summer than during the spring. In contrast to this general pattern, some monitored R _150074_MWQMLSH_final.docx 46

83 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m bays showed stable or increasing TP values throughout the summer, as indicated by the larger number of higher TP concentrations in bays than in the main basins in summer when compared to spring data (Figure 21). We recommend including embayments that show water quality differences in any season into the DMM model In order to protect the currently excellent water quality in the large Muskoka lakes and their embayments. Figure 21. Changes in Total Phosphorus from Spring to Summer at MLA Sampling Stations. R _150074_MWQMLSH_final.docx 47

84 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Summary Embayments in the large lakes Muskoka, Rosseau and Joseph were assessed to determine if they should be modeled separately in the DMM water quality model. After careful data quality control, a large dataset of TP measurements spanning the past 10 years and including 35 sampling sites provided by the MLA was analysed for statistically significant differences between embayments and main stations in the three lakes. Together with an assessment of bathymetric characteristics and in consultation with stakeholders, four additional embayments were recommended for inclusion in the model: Boyd Bay, Still s Bay, Rosseau North and Rosseau East. We recommend that all currently modeled embayments remain in the model. 5.2 Georgian Bay Embayments The shoreline of Georgian Bay in Georgian Bay Township is highly structured into bays, islands and peninsulas, similar to the large Muskoka lakes. Several of these embayments are being monitored by the DMM and Georgian Bay Forever (GBF), a subset of which are currently included in the DMM model as they have limited exchange with the open waters of Georgian Bay. This section reviews the current modeling and monitoring practices for Georgian Bay embayments, identifies data gaps and provides recommendations for future modeling and monitoring Current Monitoring and Modeling There is comparatively less monitoring data for embayments of Georgian Bay than for the large Muskoka lakes, precluding statistical analyses of differences in water quality as was done in Section 5.1. Assessment of embayments was instead based on qualitative evaluation of the DMM data as well as monitoring data and other information from the Georgian Bay Forever Coastal Monitoring Program Review (HESL 2011a) and the Georgian Bay Forever Causation Study Synthesis (HESL 2011b). In general, Georgian Bay embayments can be placed into one of three categories based on morphometric separation and differences in water quality: Type 1. Those that are effectively isolated from Georgian Bay with minimal water exchange, Type 2. Those that have variable water chemistry in some areas due to limited exchange with Georgian Bay, and Type 3. Those that have water quality primarily influenced by Georgian Bay District of Muskoka Monitoring Data The DMM has monitored water quality in eight embayments of Georgian Bay. Four embayments of Type 1 and two locations in Twelve Mile Bay (Type 2) have been modeled as distinct basins in the DMM water quality model (Table 15). The three embayments that are monitored but not currently included in the model as separate basins are Go Home Bay, Cognashene Bay and Wah Wah Taysee (Table 15). R _150074_MWQMLSH_final.docx 48

85 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 15. District of Muskoka TP Monitoring Data for Georgian Bay Embayments ( ) Embayment Type Location Latitude ( N) Longitude ( W) Total Phosphorus ( g/l) Modeled Embayments 1 Go Home River to Georgian Bay 1 North Bay South Bay Tadenac Bay Twelve Mile Bay - East Twelve Mile Bay - West Not Modeled Embayments 3 Wah Wah Taysee Go Home Bay Cognashene Bay Data Gaps and Recommendations Data gaps were identified and based on the basin types (Types 1-3), available monitoring data and location of monitoring sites, the following recommendations are provided for the embayments that are presently monitored by the DMM or included in the model as separate basins: Go Home Bay - Go Home Bay is not presently included in the DMM model as a separate basin. It has limited morphometric separation from Georgian Bay and is influenced by the Go Home River, and therefore it may be worth attempting to model the section of Go Home Bay between Georgian Bay and the section referred to as Go Home River to Georgian Bay. The District s monitoring site in Go Home Bay, however, is very close to potential influences from Georgian Bay such that total phosphorus concentrations may not be representative of the embayment for validation of the model results. Monitoring should be conducted at a location in the inner bay for validation of model results. Cognashene Bay Large portions of Cognashene Bay are sufficiently isolated from the main basin of Georgian Bay such that there is potential for this embayment to respond differently to land use changes than areas of the outer bay. It is therefore recommended that this embayment be included in the model as a separate basin. The District s monitoring site, however, is close to the mouth of the bay and is potentially influenced by Georgian Bay. Monitoring should be conducted at a location in the inner bay for validation of model results. R _150074_MWQMLSH_final.docx 49

86 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Go Home River to Georgian Bay This section of the Bay is modeled as a separate basin in the DMM model, but there is no water quality monitoring site to allow model validation. The modelling of riverine sites such as this is done only to link phosphorus loads of upstream areas to downstream areas. North Bay North Bay is included as a separate basin in the model, but water quality monitoring by the DMM has been temporarily discontinued due to overlap with Severn Sound Environmental Association (SSEA) research programs. The SSEA collects water quality data in this area that can be used to validate results of the model. Tadenac Bay Tadenac Bay is monitored and modeled by the DMM. The embayment is sufficiently isolated from Georgian Bay (Type 1) and so there is potential for this embayment to respond differently to land use changes. No change is recommended for monitoring or modeling. Twelve Mile East - This section of Twelve Mile Bay should measure and model correctly, as water exchange with Georgian Bay is likely limited. The anoxic hypolimnion at the east end of Twelve Mile Bay influences water quality. Twelve Mile West This section of Twelve Mile Bay may be influenced by exchange with Georgian Bay. The water quality monitoring site should probably be moved further inland to be better suited for model validation. Wah Wah Taysee - Wah Wah Taysee is directly connected over large areas with Georgian Bay such that it cannot be modeled with confidence. South Bay - South Bay is modeled but the water quality station in the bay has been temporarily discontinued due to overlap with SSEA research programs. The SSEA collects water quality data in this area that can be used to validate results of the model. In addition to recommendations mentioned in the above paragraphs it would be useful, in any given year, to know what the TP concentrations are in the open water of Georgian Bay (outer bay) near those bays that have the potential to exchange water with the main Bay (Type 2 and 3, Table 16). In some cases these data are already being collected. The two locations where the additional collection of outer Bay samples is recommended are Go Home Bay and Twelve Mile Bay West (Table 16). R _150074_MWQMLSH_final.docx 50

87 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 16. Recommendations for Monitoring Inner and Outer Bay Sampling Locations Location Current Monitoring Recommendations for Modeling and Monitoring Outer Bay Inner Bay Outer Bay Inner Bay Go Home Bay No Measured, not modeled. Cognashene Bay Yes Not measured or modeled. Go Home River to Georgian Bay Modeled, not measured Monitor Keep monitoring Not required Possibly model Move sampling site further in-bay and possibly model No change North Bay No Yes Not Required No change Tadenac Bay No Yes Not Required No change Twelve Mile Bay East Twelve Mile Bay West No Yes Not Required No change No Yes Monitor Possibly move sampling site further in-bay Wah Wah Taysee Yes Not applicable Not Required No modeling or monitoring required South Bay No Yes Not required No change Other Monitoring Programs The bays that DMM monitors, with the exception of Tadenac Bay, are also monitored by the Georgian Bay Forever (GBF) Coastal and Inland Lakes monitoring Program (Table 17). In some cases the sample locations are not the same as those visited by the DMM. GBF also collects data for a number of inland lakes in the Georgian Bay area that may be used to supplement data collected by DMM for validation of the model where applicable. All sample locations, protocols and data quality, however, should be reviewed before using data from other programs to validate model output. This is usually not an issue because the GBF monitoring programs have traditionally collected samples in the fall during turnover when water of the embayments should be mixed. In 2012, GBF began to collect spring turnover data at the locations noted in Table17 R _150074_MWQMLSH_final.docx 51

88 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 17. Georgian Bay Embayment Sites and Inland Lakes monitored by Georgian Bay Forever. Program Station Name Latitude ( N) Longitude ( W) Modeled GBF 12 mile # Yes GBF 12 mile # Yes GBF Go Home # Yes GBF Cog Lake # Yes GBF North Bay # Yes GBF North Bay # Yes GBF HH # No GBF HH # No GBF South Bay # Yes GBF South Bay # Yes Inland L Go Home Lake Yes Inland L Gibson Lake Yes Inland L Galla Lake Yes Inland L Baxter Lake Yes Inland L Six Mile Lake Yes Inland L Gloucester Pool Yes Inland L Severn River? Inland L Wah Wah Taysee No Conclusions and Recommendations There are a large number of bays and inlets in Georgian Bay that could potentially be modeled as distinct basins. It would be impractical, however, to try to include all of these in monitoring or modeling programs. The most cost effective approach would be to have a closer look at specific embayments when individual capacity assessments were deemed necessary due to development requests. With respect to the presently monitored and modeled locations in Georgian Bay, we recommend that: Go Home Bay be modeled with the caution that modeling may not provide accurate results due to water exchange with Georgian Bay, and a site in the outer bay be monitored to assess water exchange. Cognashene Bay be modeled and monitored at a site within the embayment. An outer bay monitoring location already exists to assess water exchange from this embayment with Georgian Bay. Modeling other embayments should be considered on a case-by-case basis as demands for additional development occur or that alternative approaches to development limits be considered for embayments that are not included in the model. R _150074_MWQMLSH_final.docx 52

89 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m 6. Muskoka Water Quality Model Review and Update The original objectives of the 2010 review included assessment of assumptions of phosphorus mobility that were included in the 2005 review (Gartner Lee Ltd., 2005), particularly those assumptions regarding lack of phosphorus mobility in soils and altering these assumptions in response to soil conditions and distance of a septic system from the lake. Although the evidence of phosphorus retention in acidic, mineral rich, unsaturated Shield soils continues to be strong (Robertson et al. 1998; Robertson, 2003; Zurawsky et al. 2004) the first step in the 2010 model revision was altering the model to match the recommendations made by MOECC (Ontario 2010). That is, previous factors accounting for phosphorus retention by soils and setback of a septic system were removed from the model. This step increased the degree of model over-prediction of phosphorus concentrations in developed lakes but decreased the degree of under prediction. The next steps involved a systematic evaluation of the model and various factors influencing its performance, as described in the following sections. In the end, although the published evidence continues to support retention of septic system phosphorus, retention was just one of many factors that could be altered, but which did not significantly improve model performance overall. The current version of the model was therefore finalized with no factors for soil retention for comparison of model results with measured phosphorus concentrations in the lakes. 6.1 Model Results and Validation Confidence in the ability of the MWQM to predict phosphorus concentrations requires validation of model results against measured values. The model is considered to provide reasonable estimates of phosphorus concentration if the measured and modeled values agree to within 20% (Ontario, 2010). The LCM is a steady-state model and therefore, results need to be validated against long-term mean measured data to account for inter-annual variability in phosphorus measurements. Results from the spring sampling surveys (Section 3.2.3) were compared to modelled phosphorus concentrations to assess the reliability of the model to predict responses of the lakes with phosphorus inputs from shoreline development. The phosphorus model predicts mean ice free total phosphorus (TPif) concentration, which was converted to spring turnover total phosphorus (TPso) concentrations for comparison to measured values following Hyatt et al. (2011), whereby: TPif = * TPso Overall, there was a poor relationship between measured and modelled estimates of total phosphorus for the 206 validation lakes for which measured data exist (Figure 22). The model tended, overall, to overestimate phosphorus concentrations in Muskoka lakes (Figure 22). The mean and median positive errors (overestimates) were 53% and 38% and the mean and median negative errors (underestimates) were 25 and 23% (Table 18). Error exceeded 20% in 69% of the validation lakes and exceeded 40% in 39% of the validation lakes. R _150074_MWQMLSH_final.docx 53

90 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 22. Accuracy of the MWQM model to predict phosphorus concentration (n=206 lakes). Dotted lines enclose +/-20% about the 1:1 line. Table 18. Predictive Error of the MWQM (n=206 lakes) + Error - Error Mean Error (%) Median Error (%) n = n >20% Error n >40% Error Notes: + Error is overestimation of phosphorus concentration by the model and -ve error is underestimation Potential Sources of Error A series of analyses was undertaken to determine if there were systematic errors or biases in the model approach that could account for the poor fit between measured and modelled phosphorus concentrations and which could be altered to improve model performance. Factors not related to development were assessed for undeveloped lakes only to eliminate the influence of error from assumptions related to development, such as the mobility of septic phosphorus Development The estimate of total phosphorus loading to a lake becomes increasingly uncertain as development is increases because of the uncertainty associated with the mobility of phosphorus from septic systems. There is also some uncertainty in development counts and estimates of occupancy rates, both of which influence the loading of phosphorus. R _150074_MWQMLSH_final.docx 54

91 % Error (Measured vs Modelled) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Although the model had a greater tendency to overestimate than to underestimate, this was not related to the level of development on lakes. That is, if the error was due to the model assuming that septic phosphorus was mobile when it was not, then the model error would be expected to increase as the potential septic phosphorus load increased. Potential development load is indicated by the Development Index (D.I), which is the ratio of total potential (human + natural) phosphorus load to the natural phosphorus load, such that a value of 1.0 indicates no human phosphorus load and 1.5 = addition of 50% of the natural load from human sources, or Background +50%. There was a greater tendency to lower negative error and higher positive error as the potential septic phosphorus load increased (Figure 23), suggesting that phosphorus was being retained by the soils around some lakes but the error range was wide and was not systematically related to the amount of shoreline development on lakes. Although adding the 2005 filters for soils back to the model did improve model performance, substantial variance remained and so phosphorus retention was not included in the revised model, in accordance with MOE guidance (Ontario, 2010). Figure 23. Model error compared to potential phosphorus load from development, all lakes. 100 Accuracy of Revised Muskoka Water Quality Model All Data Development Index The error in model results was also large for lakes with little to no development (Figure 24). The median model error ranged from -35% to 27% in undeveloped Muskoka lakes and the model underestimated phosphorus in 6 of the 9 undeveloped lakes (Table 19). Increasing the sample size to include lakes in which 3% or 6% of the total phosphorus load was from development showed the same general trend of median error ranging from -39% to 22%. The model error was therefore substantial for lakes with little to no development. R _150074_MWQMLSH_final.docx 55

92 % Error (Measured vs Modelled) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 24. Model error for lakes with <10% potential development phosphorus load (D.I <1.1; n=36). Accuracy of Revised Muskoka Water Quality Model Undeveloped Lakes Development Index Table 19. Percentage Error of Phosphorus Concentrations in Lakes with Little Development Percentage Error of Measured vs Modelled TP + error - error + error - error + error - error + error - error All Lakes D.I. <1.06 D.I. < 1.03 D.I. = 0 Mean Median n = n >20% It is clear that there is considerable error in model results that is not related to development, estimates of phosphorus loading to septic systems and assumptions regarding phosphorus mobility in Precambrian Shield soils Natural Phosphorus Loads from Wetlands Phosphorus loads for undeveloped lakes are based on a) measured regional estimates of atmospheric phosphorus deposition from MOECC s Dorset Environmental Science Centre (DESC), and b) measured relationships between wetland in the watershed of a lake and natural phosphorus export, both of which have been refined and published by the MOECC (Paterson et al. 2006; Ontario, 2010). Atmospheric phosphorus loading was not considered as a significant source of error as it is based on long-term measured and published values for Muskoka (Paterson et al. 2006). The estimate of wetland in the watershed of a lake, however, was investigated as a potential source of error as estimates were revised between the 2005 and 2012 models. R _150074_MWQMLSH_final.docx 56

93 Old Model km2 Old Model km2 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m The new estimates of wetland area based on revised GIS analysis were substantially different from the previous estimates. In the Dwight subwatershed, for example, the 2012 estimates of wetland areas in the watersheds of 33 lakes were higher by an average of 54% (23.6 km 2 ) or lower by an average of 91% (12.9 km 2 ) than the 2005 estimates (Figure 25). The major sources of the difference were a revised method of classifying wetlands between 2005 and 2012 and better resolution of the 2012 GIS methodology. The 2012 wetland areas are considered the most up to date and reliable for use in the model. Figure 25. Comparison of 2005 and 2012 estimates of wetland areas for individual lakes in the Dwight subwatershed. Wetland Area-Dwight Ave. + Difference = 23.6 km 2, 54.4 % Ave. - Difference = 12.9 km 2, 90.6% New Model km Wetland Area-Dwight New Model km2 Model error was not systematically related to the amount of wetland in the watershed, however, (Figure 26) or to the concentration of dissolved organic carbon (DOC) in the lake (Figure 27). Natural export of phosphorus from wetlands is tied to export of DOC as both are related to breakdown of vegetation in wetlands. Eimers et al. (2008) and Palmer and Yan (2013) both documented increasing DOC in Muskoka surface waters while Eimers et al (2009), conversely, reported reductions in the long-term export of phosphorus from forested Muskoka catchments. It is clear that the dynamics of natural phosphorus export are changing and may therefore play a role in the performance of the model. Therefore, although the concentration of phosphorus in Muskoka lakes is related to DOC (see Section 3.1), the error in model predictions of phosphorus could be R _150074_MWQMLSH_final.docx 57

94 Model Error in % Error in Percent J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m related to the dynamics of phosphorus export and resultant estimate of natural loadings as well as to conversion of phosphorus load to in-lake concentration by the model. Figure 26. Model error as a function of wetland area y = x R² = 5E Wetland in Percent Figure 27. Relationship of model error to DOC in Muskoka lakes DOC in mg/l Hydrology The conversion of phosphorus loadings to phosphorus concentration in a lake is dependent on the hydrology of the lake, and is accounted for in the model by the areal water load (m/yr), which is the total depth of runoff from the watershed (in m 3 /yr) applied to the surface area of a lake (in m 2 ). The depth of runoff for the 2005 version of the model was calculated using the average annual depth of runoff from the Canadian Water Atlas (Canada Department of Fisheries and the Environment, 1975). In the interim, the MOE refined these estimates (Hyatt et al. 2012) and the new estimates were used as input to the 2012 DMM model. The refined runoff R _150074_MWQMLSH_final.docx 58

95 Error in Percent Error in Percent J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m estimates were, on average, 29% higher than the original estimates. This likely reflects the finer spatial resolution of the 2012 estimates, as there is no evidence that runoff depth or precipitation has increased by 29% in Muskoka. Higher runoff should, in theory, lead to higher areal water loads, more flushing of lake volume and lower phosphorus concentrations in lakes, but the change in runoff estimate did not result in a systematic error in modelled TP concentrations. There was no systematic error in the model related to areal water load to lakes (Figure 30). Figure 28. Relationship of model error to areal water load y = x R² = 2E Areal Water Load in m y = x R² = 2E Areal Water Load in m R _150074_MWQMLSH_final.docx 59

96 Percent Error Percent Error J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Watershed Function Ontario s original Lakeshore Capacity Model (Dillon et al 1986) was developed from calibrated headwater lakes. Although the Province rightfully advises that any lake modelling effort be done in a watershed context (Ontario 2010), any modelling effort must proceed from the untested assumption that the model works as well for lakes downstream in a watershed as it does for headwater lakes, and that the assumptions and calibrations that apply to small lakes and small ratios of watershed area to lake area (i.e., for headwater lakes) also apply to all lakes in a watershed. The MWQM challenges the assumptions used for calibration of the LCM, as it includes large lakes, large watershed areas and many lakes besides headwater lakes. If the assumptions used to calibrate the LCM were violated when attempting to model the entire Muskoka River watershed, then one would expect to observe a systematic model bias related to a) lakes that were not headwater lakes, and b) the ratio of watershed area to lake area. Model error showed no systematic relationship with the ratio of watershed area to lake area, beyond a tendency to underpredict phosphorus concentration for lakes with relatively small watershed areas (ratios < 50:1, Figure 29). Figure 29. Relationship of model error to ratio of watershed area/lake area Watershed Area/Lake Area Watershed Area/Lake Area R _150074_MWQMLSH_final.docx 60

97 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Model error was not systematically related to headwater position of a lake in the watershed. Of the 209 validation lakes, 122 lakes were considered to be headwater lakes (with 0 or 1 upstream lakes in the watershed) and 87 lakes were non-headwater lakes (with >1 upstream lakes in the watershed). The model tended to overestimate phosphorus concentration to a greater degree in headwater lakes, and underestimate to a greater degree in the non-headwater lakes (Table 20). Table 20. Relationship of model error to watershed position of lake. Headw ater Lakes Non-Headw ater Lakes 0 or 1 upstream lake > 1 upstream lake +error (%) -error (%) +error (%) -error (%) N= Average Median The model error showed no systematic relationship with hydrological features including the depth of runoff (areal water load), the ratio of watershed area to lake area or headwater position Lake Depth Model error was not systematically related to lake maximum depth (Figure 30, top) or mean depth (Figure 30, bottom), for those lakes where depth was known, beyond a tendency to underestimate phosphorus concentrations in shallow lakes (<5m, Figure 30, top). This suggests that the use of settling velocities for stratified lakes in shallow lakes assumes too much removal of phosphorus from the water column, thus biasing the estimated concentrations to lower values (see Section 4.4). MOECC (Ontario 2010) qualifies the use of the model to stratified lakes but provides no guidance for modelling shallow lakes. Within the set of stratified lakes, depth does not explain model variance. R _150074_MWQMLSH_final.docx 61

98 Percent Error Error in Percent J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 30. Relationship of model error to lake maximum (top) and mean (bottom) depth y = x R² = Maximum Depth (m) y = x R² = Lake Depth (m) Oxygen Status The hypolimnetic oxygen status of a lake alters the internal processing of phosphorus load in a lake and how it is expressed as concentration (Section 4.3). The equation used in the model to determine phosphorus concentration in a lake includes a term for retention of phosphorus in the sediments; phosphorus that is lost to the sediments by retention is not expressed as a water borne concentration. Retention (R) is a function of the areal water load (qs) to a lake and the settling velocity (v) of phosphorus where R = v/(v+qs). The settling velocity of phosphorus is dependent on oxygen status, and is 12.4 m/yr for oxic stratified oligotrophic lakes on the Precambrian Shield and 7.2 m/yr for those lakes with anoxic hypolimnia (Dillon et al., 1986). The smaller value of 7.2 m/yr is used for anoxic lakes as a surrogate for internal loading of phosphorus from lake sediments (see Section 4.3). R _150074_MWQMLSH_final.docx 62

99 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m The results for lakes with anoxic and oxic hypolimnia were compared to determine if there was a systematic error induced in the model through the use of average settling velocities. The DMM monitors oxygen in 205 of the lakes in the model. The model overestimated phosphorus concentrations more often than it underestimated, and by a greater error percentage, regardless of oxygen status (Table 21). The magnitude of the error, whether positive or negative, however, was higher in lakes with anoxic than with oxic hypolimnia. This suggests that either a) not all lakes classified as anoxic have an internal load, or b) that the single settling velocity of 7.2 m/yr does not represent the range of anoxic conditions (i.e., extent and duration of anoxia) and hence the range in internal loading of the lakes. Any lakes in which the oxygen status is unknown were assumed to be oxic in the modelling exercise. Section 4 in this report describes the attempts made to estimate the status of hypolimnetic oxygen in those lakes where there are no measurements in support of the 2012 model revisions. It was not possible to predict oxygen status and so the 2012 model continues to assume that ~300 unmonitored lakes have oxic hypolimnia. Although some of these may, in fact, be anoxic, that cannot be confirmed or reliably estimated for the modelling exercise. This analysis, however, suggests that even if some of the 300 lakes were anoxic, the error in the model would not likely be improved by changing the settling velocity for those lakes to the anoxic value as the magnitude of error was greater for lakes known to be anoxic than for those known to be oxic. Table 21. Relationship of Model Error to Hypolimnetic Oxygen Status Anoxic Hypolimnion Oxic Hypolimnion + error (%) - error (%) + error (%) - error (%) n = Mean Median Summary The MWQM does not provide accurate predictions of phosphorus concentrations for most lakes. Evaluation of model input parameters related to development phosphorus loads, natural phosphorus loads, hydrological characteristics, lake depth and oxygen status failed to identify any single major source of systematic error. It is therefore likely that uncertainties associated with each of these parameters contribute to error in the model. Subsequent sections of this report therefore address potential means to develop technically sound and stable planning policies to protect water quality in Muskoka s lakes while acknowledging model shortcomings. R _150074_MWQMLSH_final.docx 63

100 7. Development of a Planning Approach J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Concerns with model fit informed the adoption of Lake Sensitivity and Lake Responsiveness, in addition to the threshold determination of Background + 50% phosphorus concentration in the 2005 Lake System Health program (Gartner Lee Ltd. 2005). The 2012 model results further support this concept and suggest a greater focus on the model to assess sensitivity, and less focus on the threshold calculation, with the understanding that other aspects of the existing DMM program, such as the requirements for BMPs, the Development Permit system and minimum lot frontages and sizes also act to protect water quality. This section of the report reviews the model attributes and performance and develops a planning approach that is supported by those aspects of the model that have the greatest reliability. 7.1 Rationale for a Revised Approach The revised Provincial Water Quality Objective (PWQO) for lakes on the Precambrian Shield allows a 50% increase in phosphorus concentration from a modeled baseline of water quality in the absence of human influence (background plus 50%, BG+50%) to a maximum cap of 20 g/l (Ontario 2010). The Province recommends the use of the Lakeshore Capacity Model (LCM) to determine the baseline or background phosphorus concentration of lakes and to assess the number of shoreline lots that can be developed without exceeding the revised PWQO, that is, the development capacity. The LCM must produce sufficiently accurate estimates of phosphorus concentration, however, in order to support this approach and provide the DMM with a defensible means to approve or decline shoreline development applications. The Province recognizes the need for accurate model results and has recommended that in cases where the model accuracy is not adequate, that the interim PWQO for phosphorus be followed as a guideline. The interim PWQO for phosphorus (MOE 1994) is an average ice-free concentration of 10 g/l for lakes naturally below this value, and a cap of 20 g/l to avoid nuisance concentrations of algae in lakes. This tiered approach, however, would eventually result in lakes converging on 10 g/l or 20 g/l and would not protect the diversity of water quality among lakes, in particular, the large number of very low productivity lakes in the DMM. Moreover, a model would still be required to assess lake response to phosphorus loads from development upon which to base capacity limits (i.e. how many lots could be added to maintain a lake below the 10 or 20 g/l PWQO) and to determine if a lake had naturally had a phosphorus concentration below 10 g/l. Our assessment is that the formulation of the model for Muskoka does not support use of the model with this degree of certainty. The LCM model results for the DMM lakes do not provide sufficiently accurate results to follow the Province s approach to set capacity limits (Ontario 2010) and the interim PWQO (MOE 1994) is not protective of diversity in water quality. Nevertheless, responsible planning to protect water quality requires some way of estimating capacity, of determining when enough is enough, or of managing development so that water quality is not impaired until such time as an improved model or alternate approaches are available (such as incorporation of phosphorus abatement into the Ontario Building Code for septic systems). Some form of modelling is therefore necessary to predict the response of lakes in the DMM to shoreline development, but planning decisions should be built around those components of the model in which we have higher confidence. Table 21 presents a summary of the model assumptions and input data and our assessment of the confidence placed in each, in order to guide its use for informing planning policy in Muskoka. R _150074_MWQMLSH_final.docx 64

101 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a R e v i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 22. Model Components and Evaluation of Confidence Component Lake, watershed areas Natural Atmospheric Load Natural Load from Wetland Depth of Runoff Settling Velocity Predicted BG and BG+50% Concentrations Anthropogenic Load to septic system Anthropogenic Load to lake from septics Anthropogenic Load to lake from runoff Usage Factor Predicted Present Day Concentration Measured Present Day Concentration High Confidence - based on recent data and GIS mapping Confidence High Confidence - long-term (17 years) measured data from MOE specific to Muskoka-Haliburton area Moderate Confidence - Measured and published relationship from MOE - Moderate - Wetland areas from GIS Moderate - Relationship of DMM to MOE wetland definition/classification Moderate - Wetland classes used by DMM differ from those used to derive the export equation used in the model. Low - Changing DOC and phosphorus export dynamics in Muskoka Moderate (changing but measurable and model can be adapted). Moderate Confidence - Data from long-term monitoring programs, but these are regional and not lake-specific. Low Confidence - Two values (oxic and anoxic) used for all lakes - No settling velocity has been developed specifically for shallow lakes - Insufficient data (lake depth, hypolimnetic oxygen status and phosphorus concentration) to assess all lakes in the study area Low Confidence - Model error >20% and not systematically related to model inputs. Moderate Confidence - Based on measured water usage and effluent phosphorus concentrations, but data are old and usage figures are not lake-specific Low Confidence - Published studies show that phosphorus is not always mobile, particularly in Shield soils - Increasing acknowledgment of attenuation by soils from the MOE and OMB Moderate Confidence - A known component, export coefficients are estimates only and not verified for the Precambrian Shield subwatersheds - Export coefficients taken from southern Ontario and cut by 50% based on published differences between export from forested watersheds on and off the Shield Moderate confidence - Three values, 354 lakes - Not updated in 20 years, based on Central Ontario surveys - Known site-specific errors Low confidence - Poor model performance for 60% of the validation lakes (n=65); 20% are underestimated by an average of 42% and 40% are overestimated by an average of 130%. Figure 22, Table 18 High Confidence - Documented improvements in accuracy of sampling and analytical techniques year record of high resolution TP measurements for DMM lakes. R _150074_MWQMLSH_final.docx 65

102 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Overall, although the MWQM does not provide sufficiently accurate estimates of phosphorus concentrations in specific lakes, it may be useful in a screening or diagnostic context to a) provide estimates of potential phosphorus loads to lakes (based on the amount of shoreline development) and b) to determine the relative sensitivity of lakes to those loads. There is a high degree of confidence in the water quality results obtained by the DMM though their lake monitoring program. In the past they have been used for comparison against model results and to inform residents regarding the status of their lakes. They could be used to greater advantage, however, to inform planning policy. We therefore propose that Muskoka s policies for management of shoreline development place a greater emphasis on Muskoka s record of measured water quality, for which there is greater confidence and which are more easily understood by the public. The MWQM should be used in a lake specific context, as one component of interpreting changes in water quality The combination of measurement and diagnosis would be used together to inform specific planning policies for lakes, as explained below. This approach can be used to provide a high level of protection for DMM lakes, and would provide the necessary defensibility and rigour to policy. 7.2 Management Triggers Three factors; phosphorus concentrations exceeding 20 g/l, trends in phosphorus concentration and occurrence of blue-green algal blooms should inform the management approach. These factors can be observed or measured with a high level of confidence. Trigger 1. Measured total phosphorus concentration exceeds the PWQO cap of 20 g/l for protection against nuisance algal and aquatic plant production. MOE (1994) provides an interim PWQO for total phosphorus that states To avoid nuisance concentrations of algae in lakes, average total phosphorus concentrations for the ice-free period should not exceed 20 µg/l. There is high confidence in measured data and the DMM water quality monitoring program collects data that can be used to assess this criterion. The intent of the Lake System Health Program is to manage human activities that contribute phosphorus to DMM lakes. Many of Muskoka s lakes, however, have high phosphorus concentrations in association with dissolved organic carbon as a result of the contributions from wetlands in their watersheds, and not from high phosphorus loads from human sources. The total phosphorus loading from human sources does not exceed 50% for any DMM lakes with TP >20 g/l and DOC >8 mg/l (Figure 31). In these cases, phosphorus concentrations naturally exceed the PWQO and are not necessarily caused by human influence. In contrast, phosphorus concentration in Three Mile Lake Main is 22 g/l and DOC is <6 mg/l. The D.I. value of 2.82 reflects a high human loading to the lake but the lake is also subject to internal loading and is known to develop blue-green algal blooms. Nevertheless, the MOE cap of 20 g/l does not differentiate the source of the phosphorus enrichment, only the increased potential for algal blooms and so is recommended as a trigger. Any lake in which TP exceeds 20 g/l should be considered at risk for algal blooms and the enrichment should not be exacerbated by additional loading from human sources. The role of DOC and internal loading should be considered, however, when developing a management plan for high phosphorus lakes. R _150074_MWQMLSH_final.docx 66

103 Total Phosphorus (ug/l) J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 31. Human phosphorus loading and relationship between TP and DOC in Muskoka Lakes. TP-DOC Relationship in 193 Muskoka Lakes y = x R² = Mile Main D.I. = Mile GR DI = 1.14 Black DI = 1.12 Halfway DI=1.17 Fox DI = 1.16 Clark DI Dissolved Organic Carbon (mg/l) Barrons D.I. = 1.01 Brandy D.I. = 1.27 Ryde D.I. = 1.05 Fawn D.I. = 1.31 Bearpaw DI = 1.06 Webster DI=1.0 Siding DI = 1.13 Perch DI=1.1 Buck HT DI = 1.19 Note: Development Index (D.I.) is the ratio of total assumed phosphorus load to natural load (i.e., a lake with no human development would have a D.I. = 1 and a lake where human development had increased the total load by 50% would have a DI = 1.5). Trigger 2. A statistically significant increasing trend in phosphorus concentration A long-term trend in total phosphorus concentration may indicate a response to human phosphorus loads such as increased development or delayed movement of phosphorus between a septic system and a lake. Other factors related to climate change and variability may also produce trends (both upward and downward) and so may not make a reliable input to planning policy. At least 15 years of data are recommended to assess long term changes. The DMM should evaluate total phosphorus data for trends annually (using all data extending back to 2000). If a statistically significant increasing trend is noted more investigation would be warranted to evaluate the cause of the trend and to respond as required by amendments to policy. Trigger 3. There is a history of blue green algal blooms The factors controlling bluegreen algal blooms are complex, but the risk of bloom activity is known to increase with increasing phosphorus concentration. Inclusion of the PWQO of 20 g/l as a trigger criterion for management is meant to protect lakes from nuisance growth of aquatic plants and algae, including bluegreen algae due to elevated phosphorus concentration. R _150074_MWQMLSH_final.docx 67

104 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Algal bloom activity, however, can also be triggered by factors other than elevated phosphorus concentrations resulting from human sources of phosphorus. For example, bluegreen algae are known to bloom in warm, shallow and still waters and so an extended period of hot, calm weather may trigger blooms despite relatively low total phosphorus concentration. Some species (i.e. Gloeotrichia echinulata) take phosphorus from lake sediments and then bloom in the euphotic zone (this was observed in Fairy and Peninsula lakes in Huntsville in 1995). Other bluegreen algal blooms occur in stratified lakes that have low surface water total phosphorus concentration (<20 g/l) but have elevated phosphorus concentration in the hypolimnion due to internal loading of phosphorus from anoxia. Unlike other types of algae, bluegreen algae can control their buoyancy and can move down in the water column to take advantage of high phosphorus concentrations at the top of the hypolimnion of these lakes. While factors other than human sources of phosphorus may trigger algal blooms in lakes, increasing phosphorus loads may contribute to the problem. If a bluegreen algal bloom is reported and confirmed, the lake would maintain its classification but more investigation is warranted to evaluate the cause of the bloom and to respond as required. 8. Integration, Conclusions and Recommendations The most recent review began in HESL revised the 2005 version of the MWQM to incorporate the most recent MOECC guidance (MOECC 2010, Paterson et al. 2006). These revisions included: Revised atmospheric loading coefficients for phosphorus, Revised wetland phosphorus export equation for phosphorus, Incorporation of smaller lakes (8ha and greater) in the model, Refined GIS mapping of lake areas, watershed areas and wetland areas by DMM staff, Updated estimates of existing shoreline development (including developed and vacant lots) from DMM records, Removal of the model factors that accounted for attenuation of septic system phosphorus (soil classification and staged attenuation of septic system phosphorus in 100m increments from the lakeshore to 300m) at the request of the MOECC, and Comparison of model output against the most recent 10 year record of total phosphorus measurements made in DMM lakes by the DMM. After extensive testing and analysis of the revised model we once again concluded that the modelled estimates of phosphorus concentrations in lakes were not reliable enough to set and defend specific lakeshore capacities as numbers of cottage or residential lots, as intended by the MOECC. Similar concerns were expressed by MOECC scientists, based on their recent experience, when we presented our findings to them in a meeting with DMM in January of We considered maintaining the 2005 approach to classify lakes according to their threshold and sensitivity to phosphorus loading but with some modifications based on the revised model and an improved understanding of model limitations. This approach, however, still relied on assumptions that may not accurately reflect processes in Muskoka lakes, or that could change over time in response to new scientific understanding or changing MOECC guidance. While changes may be technically valid, these and the known error in model predictions reduce public confidence in the Lake System Health program. Moreover, R _150074_MWQMLSH_final.docx 68

105 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m the approach remained focussed only on phosphorus and did not address other threats to the lakes. In addition, the emergence and testing of P reduction technologies for septic systems since 2010 resulted in OMB decisions favoring development beyond the Lakecap limits in several cases, such that the potential for OMB challenges, and resultant costs for the DMM, warranted reconsideration of those aspects of Lake System Health and District policy that were based on the water quality model. Muskoka s lakes are changing and are threatened by a variety of stressors in addition to shoreline development. The recent Canada Water Network Research Program in the Muskoka watershed, for example, concluded that the multiple stressors included: increasing concentrations of dissolved organic carbon and chloride, declining concentrations of calcium, invading species populating an increasing number of lakes and the changing climate with resultant changes in precipitation, temperature, runoff and evaporation that affect physical, chemical and biological conditions of lakes. Recent research by the MOECC also shows increasing reports of nuisance algal blooms across Ontario, a possible response to changing climate. At the same time, the DMM has developed and implemented an excellent program of water quality monitoring that obtains high quality data on phosphorus concentrations, dissolved oxygen status and water clarity for ~190 lakes or lake segments; and contributes data on major ion and DOC concentrations to the MOECC database. Analysis of the DMM phosphorus record from 190 lakes for the period from showed that phosphorus was not increasing significantly in any lakes but that three lakes showed a statistically significant decline. It is clear that planning policy that is focussed solely on phosphorus sources is not warranted by the accuracy of the model, the evidence that phosphorus concentrations are not increasing significantly in any lakes, the emerging support of Best Management Practices for control of phosphorus at the OMB and the other stressors acting in Muskoka s lakes. Given the issues with model inaccuracies, changes in scientific understanding and potential effects of multiple stressors, a new, holistic approach is recommended for the Lake System Health program that: a) Eliminates the classification of lakes based on modelled estimates of phosphorus concentration in recognition of the uncertainty that the modelling approach adds to the planning process, b) Provides increasing focus on the excellent water quality monitoring program that has been in place for 15 years in District planning policies, and c) Recognizes Best Management Practices and development standards that can effectively mitigate the impacts of shoreline development and which may address a host of other environmental concerns. Recommended Approach We therefore recommend that the Lake System Health program be based on: R _150074_MWQMLSH_final.docx 69

106 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m 1. A minimum and enforced standard of protection and Best Management Practices for new development and redevelopment on all lakes, 2. Use of the District monitoring program to track phosphorus on DMM lakes and classify them according to measured changes and observed quality, and 3. Implementation of enhanced planning requirements and Best Management Practices for individual lakes based on observed water quality concerns or triggers based on the District s monitoring program. These could include implementation of causation studies on individual lakes and focussed use of the existing model in response to the monitoring triggers. 8.1 Lake Planning and Management Triggers The intent of the Lake System Health Program is to manage human activities that contribute phosphorus to DMM lakes. The DMM water quality monitoring program collects data that can be used to assess lake status and there is high confidence in these data. The data that are routinely collected on Muskoka s lakes can be used to inform the following triggers of lake sensitivity: 1. Phosphorus concentrations exceeding 20 µg/l based on the most recent 10-yr average phosphorus concentrations measured in the DMM monitoring program, 2. A statistically significant increasing trend in phosphorus concentration, based on evaluation of the phosphorus concentration record measured in the DMM monitoring program since 2001, and 3. Occurrence of bluegreen algal (cyanobacterial) blooms as documented by public complaints to the MOECC or the Simcoe-Muskoka District Health Unit. These are recommended for the management approach, as triggers for additional study and, if required, a management and planning response. Trigger 1 - Total Phosphorus > 20 g/l The first trigger is measured total phosphorus concentration which exceeded the PWQO of 20 g/l for protection against nuisance algal and aquatic plant production. A data record of the most recent 5 spring overturn phosphorus measurements taken within 10 years is required to assess long term concentration and records should be reviewed annually. Most Muskoka lakes are sampled every two years - for lakes with a less frequent record (e.g. every 3 years) then the most recent five measurements would be used. For triggered lakes: Management recommendations such as Enhanced BMPs would be developed to protect water quality. These would be elaborated in, and drawn from, a schedule in the District Official Plan. Implementation would be a) encouraged through a stewardship program and b) required for any development or redevelopment. R _150074_MWQMLSH_final.docx 70

107 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m A Causation Study would be required to determine why phosphorus concentrations exceeded 20 g/l and the role of shoreline development or other human factors in the phosphorus enrichment would be examined. If the causation study concluded that shoreline development was responsible for, or a significant contributor to, the observed phosphorus enrichment then policy could limit further development or require a formal Remedial Action Plan. Trigger 2 - Increasing Trend in Total Phosphorus A long-term increasing trend in total phosphorus concentration may indicate a response to human phosphorus loads or other factors related to climate change and merits investigation and enhanced protection. A data record of at least 5 measurements is required to assess long term changes and the increase must be statistically significant at p<0.1. We recommend that trends be assessed each year using data beginning in 2001, when high quality phosphorus measurements were reliably available for the DMM program. The trend would be reassessed as more measurements were obtained but the starting point would remain at Using a more recent record (e.g. last 10 years) risks not capturing a long-term trend or a trend of small step changes that were not significant on their own but contributed to a trend over the long term. Records would be reviewed annually. For triggered lakes: Management recommendations such as Enhanced BMPs would be developed to protect water quality. These would be elaborated in, and drawn from, a schedule in the District Official Plan. Implementation would be a) encouraged through a stewardship program and b) required for any development or redevelopment. A Causation Study would be required to determine why phosphorus concentrations were increasing and any role of shoreline development or other human factors would be examined. If the causation study concluded that shoreline development was responsible for, or a significant contributor to, the observed phosphorus increase then policy could limit further development or require a formal Remedial Action Plan. Trigger 3 - Documented Blue-Green Algal Bloom The factors controlling blue-green algal blooms are complex, but the risk of bloom activity is known to increase with increasing phosphorus concentration. Inclusion of the PWQO of 20 g/l as a criterion for management is meant to protect lakes from nuisance growth of aquatic plants and algae, including bluegreen algae due to elevated phosphorus concentration. In many cases, however, algal bloom activity can be triggered by factors other than elevated phosphorus concentrations resulting from human sources. For example, blue-green algae are known to bloom in warm, shallow and still waters and so an extended period of hot, calm weather may trigger blooms despite relatively low total phosphorus concentration. Bluegreen algal blooms also occur in some stratified lakes that have low surface water total phosphorus concentration (<20 g/l). Unlike other types of algae, bluegreen algae can control their buoyancy and can move down in the water column to take advantage of high phosphorus concentrations in the hypolimnion or the sediment of lakes. Therefore, lakes that have elevated phosphorus concentrations in the hypolimnion due to internal loading from anoxia (Three Mile Lake) may be susceptible to blue-green blooms. In other lakes, blue-green algae may take phosphorus from lake R _150074_MWQMLSH_final.docx 71

108 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m sediments and then migrate into low phosphorus surface waters and bloom (Peninsula Lake in ~1995). Blooms in these lakes may therefore reflect natural conditions and not be related to shoreline development or other human sources. Lakes would be triggered when a bloom was reported to the MOECC Spills Action Line or the Simcoe- Muskoka District Health Unit and their investigations confirmed that the bloom was made up of cyanobacteria species. While factors other than human sources of phosphorus may trigger algal blooms in lakes, increasing phosphorus loads may also contribute to or exacerbate the problem. For triggered lakes: Management recommendations such as Enhanced BMPs would be developed to protect water quality. These would be elaborated in, and drawn from, a schedule in the District Official Plan. Implementation would be a) encouraged through a stewardship program and b) required for any development or redevelopment. A Causation Study would be required to determine the likely causes of the algal bloom and any role of shoreline development or other human factors would be examined. If the causation study concluded that shoreline development was responsible for, or a significant contributor to, the algal bloom then policy could limit further development or require a formal Remedial Action Plan. 8.2 Causation Studies Previous versions of the Lake System Health Program worked on the premise that increases in phosphorus concentration beyond the modelled estimate of Background 50% were related only to shoreline development and that lakes which the model showed to be sensitive to phosphorus loading should be managed to prevent increased phosphorus loading from shoreline development. The proposed changes acknowledge the problems with model accuracy, the potential for other causes of changed water quality and recognize the merits of a high quality record of water quality as determined through the DMM monitoring program as a more reliable trigger for management or planning action. Planning and management responses must, however, be based on an understanding of the factors that caused a) phosphorus concentrations to exceed 20 µg/l, b) phosphorus concentrations to increase in a trend or c) a cyanobacterial bloom. Causation studies are therefore recommended for triggered lakes to a) examine the cause of the trigger, b) examine the role of shoreline development in the observed trigger and c) develop the appropriate management response. These could include any or all of the following investigations: Detailed review of water quality monitoring data (e.g. Secchi depth, DO and DOC measurements), Collection of additional water quality data through the DMM monitoring program (e.g. hypolimnetic samples to assess internal load), Detailed and lake specific application of the Muskoka Water Quality Model to consider detailed counts of shoreline development and usage (seasonal vs permanent), land use in the watershed and catchment soil types and depth to assess phosphorus attenuation in the soil, R _150074_MWQMLSH_final.docx 72

109 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Site specific investigations of hydrology and inflows to assess any flooding in the catchment from road construction or beaver dams that may alter phosphorus dynamics, A septic system inspection program, A survey of shoreline disturbance (i.e. presence of lawns and budgets) A Limits to Growth assessment based on the present shoreline characteristics (see to determine any factors limiting shoreline development and the feasibility of additional shoreline development or redevelopment, which would help determine the need for and nature of a planning response or implementation of Enhanced BMPs. Causation Studies will be developed on any lakes triggered by the three criteria to evaluate the reasons they were triggered and determine the need for, and type of any lake-specific management responses. Causation Studies can include many of the investigations listed above but need not include all of them. We have developed a scope of work for three different Causation Studies to provide an idea of what type of information would be required to inform appropriate lake management in response to the various triggers, and associated costs with collecting and interpreting the required information. Scopes of work were developed for lakes that would be triggered under each of the proposed trigger criteria (i.e. TP > 20 µg/l, increasing trend in TP, or documented blue-green algal blooms). Our analysis was done using monitoring results for (App. E). Five lakes had 10 year ( ) average TP concentrations exceeding >20 µg/l. These were Ada Lake, Barrons Lake, Bass Lake (Gravenhurst), Brandy Lake and Three Mile Lake Main. Three of 190 lakes (Clark Lake, Mirror Lake, Tackaberry Lake) exhibited statistically significant decreasing TP concentrations from and no lakes showed an increasing trend. Lakes triggered by the third criterion of documented blue-green algal blooms included Bruce Lake and Three Mile Lake Examples of Causation Studies Conceptual Causation Study tasks are presented below for Bass Lake, Bruce Lake and Three Mile Lake. Bass Lake The following tasks are recommended for a causation study to examine a) the cause of elevated TP concentrations (20.2 µg/l) in Bass Lake, b) the role of shoreline development in causing the TP concentrations, and c) the appropriate management response. In some cases I have done the analysis based on an initial review of available data. 1. Examine all existing measured data from DMM and the MOECC Lake Partner Program (LPP) including TP, DOC, Secchi depth and DO concentrations to confirm the observed concentration is supported by all data and assess for any temporal or spatial patterns. o Review of the Bass Lake data sheet from the Muskoka Water Web shows: a) that it needs to be updated - it is current to 2012, b) that there may be a cyclical increase and decrease in TP concentrations such that the enrichment beyond 20 µg/l may not represent a long term condition (the R _150074_MWQMLSH_final.docx 73

110 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m existing 10 year mean is only 20.2 µg/l) such that no management action is warranted, c) that Secchi depth is very low (1.7m) as a result of high DOC and may be increasing, and c) there may be an internal load as the August DO profile approaches anoxia at the bottom. The Causation Study would therefore include completing a DO/temperature profile at the end of August and sampling water 1 meter above bottom for TP and Fe. 2. Review the Bass Lake data for TP and DOC against the updated relationship for all Muskoka Lakes to determine the role of DOC as a natural source of TP. The current relationship presented in Figure 33 is based on data collected from This data needs to be updated and reanalysed. 3. Review the wetland coverage in the Bass Lake watershed, including watershed to lake area ratio, through District of Muskoka s GIS data to determine contribution of inflowing water with high DOC concentrations. 4. Review the natural and human estimates of phosphorus loads from the Muskoka water quality model to determine the contribution from shoreline development. The current model formulation provides the following estimates: a. Total Natural : 1268 kg/yr (of which 1113 comes from three upstream lakes) b. Total Shoreline Load: 54 cottages within 300m, 34 of which are within 100m = 25 kg/yr + 43 kg/yr from upstream = 68 kg/yr c. The total potential human load of 68kg/yr represents background (1268 kg/yr) + 5% and so shoreline development represents a very small contribution of the allowable limit of MOECC (Background + 50%) and therefore to the enriched phosphorus concentrations in Bass Lake. The enrichment is therefore a result of naturally high DOC in the lake. 5. The MWQM shows that Bass Lake has a very high flushing rate (~117/yr or once every three days on average) and so the Kahshe River is likely the most important source of DOC and phosphorus to the lake. The Causation Study would therefore include sampling of the Kahshe River just upstream of the inlet to Bass Lake for TP and DOC on three occasions in the next summer by the DMM to confirm the inputs from the watershed. In this case, the Causation Study would conclude that there was no need for additional planning or management intervention as a) human sources were minimal, b) natural sources of DOC and TP came from the watershed and c) there may be an internal load. If the long term mean were to remain above 20 µg/l in subsequent years the DMM could recommend enhanced BMPs for development or redevelopment in recognition that Bass Lake had high phosphorus concentrations and was worthy of enhanced protection. The cost to complete the investigation and compile a report would be approximately $1,000. Costs do not include water sampling or laboratory analysis of TP and DOC in Bass Lake or the Kahshe River - we have assumed that any additional sampling would be completed as part of the DMM s Water Quality Monitoring Program. R _150074_MWQMLSH_final.docx 74

111 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Bruce Lake The following tasks are recommended for a causation study to examine a) the cause of algal blooms in Bruce Lake, b) the role of shoreline development in causing blue-green algal blooms, and c) the appropriate planning and management responses. 1. Examine all existing measured data from DMM and the MOECC Lake Partner Program (LPP) including TP, DOC, Secchi depth and DO concentrations to confirm the observed concentration is supported by all data and assess for any temporal or spatial patterns. 2. Examine all historical reports including reports commissioned by the Bruce Lake Water Quality Committee and The Rock Golf Course. 3. Collect any information on algal blooms that have occurred in Bruce Lake from MOECC including the dominant algal species and microcystin concentrations. 4. Describe limnological and climactic conditions prior to and during algal bloom formations based on existing data. Our opinion based on previous work on Bruce Lake is that, although there was an algal bloom in the year following construction of the golf course, that it has not persisted and that there were previous anecdotal reports of blooms. The Muskoka Water Web data show near anoxic conditions near the bottom in August 2013 that could indicate internal loading. a. The Causation Study would therefore include completing a DO/temperature profile at the end of August and sampling water 1 meter above bottom for TP.and Fe.. 5. Review the natural and human estimates of phosphorus loads from the Muskoka water quality model to determine the contribution from shoreline development. This would include confirming the number of residences by direct count and confirming approximate usage patterns. The current model formulation provides the following estimates: a. Total Natural : 40.3 kg /yr b. Total Shoreline Load: 85 cottages within 300m = 49 kg/yr of which 1.1 kg/yr come from the golf course. c. The total potential human load of 49 kg/yr represents background (40.3 kg/yr) + 5% and so shoreline development represents a very large contribution of Background + 120%) and therefore is a potentially significant contributor to the enriched phosphorus concentrations in Bruce Lake. d. Review of the MWQM shows that the model predicts 9 µg/l, which is close to the measured long term mean of 10.3 µg/l. 6. Complete an assessment of soils depth in the immediate Bruce Lake catchment to assess the likelihood that soils are attenuating phosphorus concentrations. In this case it is unlikely as the model classifies the soils as non attenuating based on coarse level mapping and the model provides reasonable agreement with the measured data. 7. Complete a Limits to Growth assessment to determine the potential for additional lot creation around the lake and to inform the need for BMPs or development controls. The existing model formulation shows 22 vacant lots of record. In this case, the Causation Study would conclude that there was a strong need for additional planning or management intervention as a) potential human phosphorus sources were high and well over MOECC recommendations, b) the MWQM provided a reasonably accurate estimate of existing TP concentrations R _150074_MWQMLSH_final.docx 75

112 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m c) there may be internal loading of additional phosphorus d) the history of algal blooms indicates that the lake is sensitive and e) there are vacant lots of record that can be developed. The DMM could recommend enhanced BMPs for any additional development or redevelopment and could implement a remedial program. This would have to be balanced against the observation that algal blooms are infrequent. The cost to complete this level of investigation would be approximately $6,000. Costs do not include water sampling or laboratory analysis of TP or Fe in Bruce Lake - we have assumed that any additional sampling would be completed as part of the DMM s Water Quality Monitoring Program. Three Mile Lake The following tasks are recommended for a causation study to examine a) the cause of algal blooms in Three Mile Lake, b) the role of shoreline development in causing blue-green algal blooms, and c) the appropriate planning and management responses. In this case, the Causation Study would be informed by the work completed by MOECC. 1. Examine all existing measured data from DMM, The MOECC and the LPP, including TP, DOC, Secchi disk depth and DO concentrations, and assess for temporal and/or spatial patterns. 2. Examine and summarize all historical reports including the 3 Mile Lake Algae Study Final Report (MOECC, 2010). This would include discussions with the MOECC scientists. 3. Collect and document information on all algal blooms that have occurred in Three Mile Lake from MOECC including the dominant algal species and microcystin concentrations. 4. Describe limnological and climactic conditions prior to and during algal bloom formations based on existing data. 5. Complete a dissolved oxygen profile and collect water samples 1 m off bottom for analysis of TP and iron from the Main basin and Hammel s Bay at the end of August to assess internal loading. 6. A detailed application of the lakeshore capacity model as was done for Bruce Lake including: a. an evaluation of internal phosphorus loading and retention b. detailed counts of shoreline development and usage (seasonal vs permanent) c. characterization of land use in the watershed and catchment soil types and depth to assess phosphorus attenuation in the soil and export to the lake d. a review of lake sensitivity as per the 2005 Lake System Health assessment. e. A Limits to Growth Assessment 7. Develop a recommended management response based on the findings of the above investigations detailing the potential drivers of bloom formation In Three Mile Lake and approaches to minimize the potential for future bloom formation. R _150074_MWQMLSH_final.docx 76

113 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m The cost to complete the investigation would be approximately $8,500. Costs do not include water sampling or laboratory analysis of TP or Fe in Three Mile Lake - we have assumed that any additional sampling would be completed as part of the DMM s Water Quality Monitoring Program 8.3 District of Muskoka Planning Implications Under the existing Lake System Health Program, proponents of development or redevelopment are responsible for the costs associated with the required Water Quality Impact Assessments, as these are triggered by applications for development or redevelopment. The revisions proposed herein would see Causation Studies that were triggered by the DMM water quality monitoring data. The DMM would therefore undertake the Causation Studies and post the results in a Schedule to the District OP along with the resultant requirements for development or redevelopment. We anticipate that only one Causation Study would be required for each lake - there would be no need to repeat the study if the lake remained triggered in subsequent years unless there was clear evidence that conditions had changed. One could anticipate the need for additional study, however, if a lake that had TP > 20 µg/l or an increasing trend in TP were to develop an algal bloom as well. The proposed revisions would also increase the need for enforcement of development and redevelopment conditions and standards and resultant costs. One cannot assume that water quality will be protected under the proposed planning controls and BMPs unless they were implemented and maintained as intended. We would propose that a position of Environmental Compliance Inspector at either the District or the local government level would be required for enforcement, and that fees for non-compliance, or breach of conditions be sufficient to assure encourage compliance. Proponents of development or redevelopment would be responsible for the costs associated with implementation of standard or enhanced BMPs. Development and redevelopment on lakes which were not triggered would proceed under standard planning requirements using the Standard BMPs listed below to protect water quality, Development and redevelopment on lakes which were triggered would proceed using the Enhanced BMPs listed below to protect water quality, Lakes which were triggered would also undergo a Causation Study to determine the need for additional development controls or management. Lake Trout Lakes The Lake System Health program also recognizes the sensitivity of the lake trout (Salvelinus namaycush) to changes in hypolimnetic oxygen status and respects the provincial policies for classification and management of shoreline development on lake trout lakes. Those lake trout lakes which are considered to be at capacity for shoreline development by the Province are listed in Appendix F. R _150074_MWQMLSH_final.docx 77

114 8.4 Recommendations In conclusion, we recommend that: J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m 1. All lakes are afforded a high degree of protection by a requirement for a minimum set of Standard BMPs for all new development or redevelopment. Examples are shown below as a starting point for discussion (Table 1). These will be further elaborated in subsequent work for inclusion in Schedules to the Official Plan. a. This would require assurance in the form of formal inspections and incentives or penalties for compliance or non-compliance with BMP implementation. 2. The monitoring records for all lakes be reviewed annually and results compared against the three triggers of: Total Phosphorus > 20 µg/l, an increasing trend in total phosphorus and documented presence of a blue-green algal bloom. 3. Triggered lakes be subject to: a. Enhanced BMPs (Table 22) for new development or redevelopment as a precaution against phosphorus loading, b. A detailed causation study to determine the role of shoreline development on water quality. i. This would include use of the District Water Quality Model but with detailed review of input data, review of land use patterns in the immediate watershed, review of settlement history, implementation of the DMM Limits to Growth assessment, assessment of Dissolved Organic Carbon and its role in phosphorus enrichment and remedial actions if warranted. c. A freeze on new lot creation and development of a Remedial Plan if the causation study determined that human phosphorus loading is likely the cause of increased phosphorus concentrations and/or the occurrence of cyanobacteria blooms. This approach would simplify policy implementation, provide a consistent and verifiable public and planning framework of lake status, provide protection for all lakes and enhanced protection for sensitive lakes and be based on the DMM s excellent record of lake water quality. The process is summarized in the following flow chart (Figure 34). Proposed Standard and Enhanced BMPs are presented in Table 22. R _150074_MWQMLSH_final.docx 78

115 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Table 23. Proposed BMPs for Standard and Enhanced Lake Classifications. Standard Enhanced Vegetated Buffers X X Shoreline Naturalization X X Soil Protection X X On-Site Stormwater Control X X Limit Impervious Surfaces X X Enhanced Septic Setback (30m) X X Enhanced Lot Size X X Securities and Compliance Monitoring X X Increased Monitoring Intensity X Site-Specific Soils Investigation X Septic Abatement Technologies OR// Full Servicing Slope Dependent Setback X X Enhanced Building Setback X Limit Lot Creation X Remedial Action Plan X R _150074_MWQMLSH_final.docx 79

116 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Figure 32. Proposed Lake System Health Planning Approach. All DMM Lakes Standard BMPs for New Development and ReDevelopment Sample Lakes and Review Data Annually TP > 20 µg/l Increasing TP Trend Documented Blue-Green Algal Bloom No Yes Enhanced BMPs Causation Study Detailed Water Quality Sampling and Review Review Land Use, Lake History and Development Detailed Lake Model Limits to Growth Assessment Development Related TP as Cause? No Yes Limit Lot Creation Remedial Action Plan R _150074_MWQMLSH_final.docx 80

117 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m 9. References AECOM Development Capacity of Recreational Lakes in Seguin Township. Prepared for Seguin Township. Brett, M.T. and Benjamin, M.M., A review and reassessment of lake phosphorus retention and the nutrient loading concept. Freshwater Biology, 53: Budd, L.F. and D.W. Meals Lake Champlain nonpoint source pollution assessment. Lake Champlain Technical Report No. 6. Lake Champlain Basin Program: Grand Isle, VT. Cammermeyer, J., Conrecode, P., Hansen, J., Kwan, P. & Maupin, M., P-Flux Determination: Juanita Creek analysis. Canada Department of Fisheries and Environment, Hydrological Atlas of Canada. Surveys and Mapping Branch, Dept. of Energy, Mines and Resources, Ottawa, Ontario. Chambers, P.A., Dupont, J., Schaefer, K.A. & Biaelek, A.T., Effects of agricultural activities on water quality. Canadian Council of Ministers of the Environment. Winnipeg, Manitoba. CCME Linking Water Science to Policy Workshop Series Report No. 1 Clark, B.J. and N.J. Hutchinson, 1992: Measuring the trophic status of lake: sampling protocols. Ontario Ministry of the Environment. Technical Report. 36 pp. Clark, B. J., A.M. Paterson, A. Jeziorski, Adam and S. Kelsey Assessing variability in total phosphorus measurements in Ontario lakes, Lake and Reservoir Management, 26: 1, 63-72, Dillon, P.J., W.A. Scheider, R.A. Reid and D.S. Jeffries Lakeshore Capacity Study: Part I Test of effects of shoreline development on the trophic status of lakes. Lake and Reservoir Management. 8: District Municipality of Muskoka Official Plan of the Muskoka Planning Area, Consolidated November 19, Pg. D7. District Municipality of Muskoka Public Works Certificate of Approval files. Fisheries and Oceans Canada Lake of Bays Nautical Chart. Scale 1: 25, Published by the Canadian Hydrographic Service. Fisheries and Oceans Canada Lake Rosseau and Lake Joseph Nautical Chart. Scale 1: 25, and Published by the Canadian Hydrographic Service. Fisheries and Oceans Canada Lake Muskoka Nautical Chart. Scale 1: 25, and Published by the Canadian Hydrographic Service. Freshwater Research, 1998: Complete revision of the water quality model of the District of Muskoka. Submitted to the District Municipality of Muskoka, May 26, R _150074_MWQMLSH_final.docx 81

118 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Gartner Lee Limited Recreational water quality management in Muskoka. Prepared for the Department of Planning and Economic Development, District Municipality of Muskoka. June Gartner Lee Limited Review of long-term water quality data for the Lake System Health Program. September Glenside Ecological Services Limited Species at Risk: Potentially Significant Habitat Mapping. Prepared for the District Municipality of Muskoka. Hutchinson, N.J Limnology, Plumbing and Planning: Evaluation of Nutrient-Based Limits to Shoreline Development in Precambrian Shield Watersheds. Ch. II.17 in : R. France, (ed). Handbook of Water Sensitive Ecological Planning and Design. CRC Press. Boca Raton Fla. (HESL) 2011a. Georgian Bay Forever Causation Study Synthesis. Prepared for Georgian Bay Forever. October (HESL) 2011b. Georgian Bay Forever Coastal Monitoring Program Review. Prepared for Georgian Bay Forever. October Loehr, R.C., Ryding, S.O. And Sonzogni, W.C. (1989) Estimating the nutrient load to a waterbody. In "The Control of Eutrophication of Lakes and Reservoirs, Vol. 1, Chapter 7. Maine Department of Environmental Protection (MDEP) Madawaska Lake Total Maximum Daily (Annual) Load : Total Phosphorus: Final Lakes TMDL Report. DEPLW Nϋrnberg, G.K Assessing internal phosphorus load Problems to be solved. Lake and Reservoir Management, 25: Oberts, G.L., Wotzka, P.J. & Hartsoe, J.A The water quality performance of select urban runoff treatment systems. Rept. to Legis. Comm. Minnesota Resources Metropolitan Council Pub. No a. St. Paul MN. Ontario Ministry of the Environment Water Management Policies Guidelines and Water Quality Objectives of the Ministry of Environment and Energy, July ISBN rev. Ontario (Province of) Lakeshore Capacity Assessment Handbook - Protecting Water Quality in Inland Lakes on Ontario s Precambrian Shield. Prepared by Ministry of the Environment, Ministry of Natural Resources and Ministry of Municipal Affairs and Housing. May PIBS 7642e 2010, Queen s Printer for Ontario. Palmer, M.E., N.D. Yan, A.M. Paterson and R.E. Girard Water quality changes in south-central Ontario lakes and the role of local factors in regulating lake response to regional stressors. Can. J. Fish. Aquat. Sci. 68: R _150074_MWQMLSH_final.docx 82

119 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Paterson, A.M., Dillon, P.J., Hutchinson, N.J., Futter, M.N., Clark, B.J., Mills, R.B., Reid, R.A. and Scheider, W.A A Review of the Components, Coefficients and Technical Assumptions of Ontario s Lakeshore Capacity Model. Lake and Reservoir Management, 22:7-18. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN , URL Reckhow, K.H., M.N. Beaulac & J.T. Simpson Modeling Phosphorus Loading and Lake Response under Uncertainty: a Manual and Compilation of Export Coefficients. 440/ , Environmental Protection Agency, Washington, DC. Robertson, W.D., S.L. Schiff and C.J. Ptacek Review of phosphate mobility and persistence in 10 septic system plumes. Ground Water 36 : Robertson, W.D., Enhanced attenuation of septic system phosphate in noncalcareous sediments. Groundwater 41: Scott, L.D., Winter, J.G., and Girard, R.E Annual water balances, total phosphorus budgets and total nitrogen and chloride loads for Lake Simcoe ( ). LSEMS Technical Report No. Imp. A.6 The Louis Berger Group Inc Estimation of the phosphorus loadings to Lake Simcoe. Submitted to Lake Simcoe Region Conservation Authority. September Tukey, J.W Exploratory Data Analysis. Addison-Wesley. Winter, J.G. and P.J. Dillon Export of nutrients from golf courses on the Precambrian Shield. Environmental Pollution 141: Winter, J.G., A.M. DeSellas, R. Fletcher, L. Heintsch, A. Morley, L. Nakamoto and K. Utsumi Algal blooms in Ontario: Increases in reports since Lake and Reservoir Management 27: Zurawsky, M.A., W.D. Robertson, C.J.. Ptacek and S.L. Schiff Geochemical stability of phosphorus solids below septic system infiltration beds.. J. Contaminant Hydrology 33 : R _150074_MWQMLSH_final.docx 83

120 J , D i s t r i c t M u n i c i p a l i t y o f M u s k o k a Rev i s e d Wate r Q u a l i t y M o d e l a n d L a k e S y s t e m He a l t h P ro g ra m Appendix A. Methodology for GIS exercise providing new watershed, lake and wetland areas R _150074_MWQMLSH_final.docx A1

121 2012 Watersheds for Water Quality Model Process Description Geomatics Section District of Muskoka The purpose of this is to provide watershed boundaries to be used in the Water Quality Model for the Muskoka Watershed Council as well as statistics such as the total area of features (i.e. wetlands) and developed statistics. The initial watershed boundaries were digitized in 2011 by the water quality students and QSP (GIS consultant) from the archived OBM tiles with the hand sketched watershed lines. These OBM tiles only covered the area of the District of Muskoka and not the entire Muskoka River or Black/Severn watersheds which extend far outside the boundaries of Muskoka. The first step in preparing the data for use was to eliminate all topology errors present in the watershed dataset. This was done in ArcGIS using the topology toolbar. The eight hectare lake layer needed to be modified to represent the lakes in the water quality model. This process was done by referencing the excel spreadsheet Lakes_in_model_2011.xlsx which has a complete list of all lakes in the model. The Connection field was updated in this spreadsheet as well as in the GIS data to ensure there was a unique identifier for all lakes. The new layer is called WQ_Model_Lakes. All of these lakes are clipped to the watershed boundaries to ensure that there is only one waterbody per watershed. Certain errors were noted in the digitized watershed layer and these were corrected by interpreting the contours and the water features. This entire process was run in 2011, but new information was brought forward that affected the study, so it was started again in New catchment areas were required for the study which extended into Seguin Twp (Lake Joseph, Lake Rosseau, certain bays, and other lakes). In order to get the development statistics for this area Seguin sent us their parcel fabric for the areas in question. New catchment areas were required for the study which extended into Parry Sound East, Algonquin, Algonquin Highlands, Haliburton. These areas will not have development information. Quaternary Watershed designations for each catchment was a new requirement, as well as the Long Lat of the centroid of each lake. Also, the development counts process needed to be modified. Now that 100, 200, and 300m buffers are to be used, there were many properties being counted in the wrong watersheds due to the larger buffers. The solution is outlined in this document. Also, it is now required that municipally services properties are identified in the development counts process. Crown land is now excluded from the vacant lot count. All of this is now outlined below as well. Stuart Paul 2012/10/02 (Note: The following portion of the analysis was performed in MapInfo Professional in this location: S:\WATER\Watqual\Watershed_Digitization\WQ_Model_2012).

122 A wetlands layer was used to generate a total area of wetland in each watershed. The District of Muskoka acquired an updated wetland layer for the Natural Heriatge Review (NHR) project in It was determined that this was the most appropriate layer to use for this process. The Muskoka district administrative boundary represents the extents of this layer which represented a problem as many of the watersheds extended outside of this boundary. The new NHR wetland layer ( NHR_Wetlands_Model ) had to be modified by adding wetland polygons from the MNR wetland layer to the areas that fell outside of the district boundary. Also, certain wetlands in the NHR layer had to be either deleted or clipped if a waterbody in the water quality model had been re-designated in the new NHR layer as a wetland. For the purposes of the water quality model it had to stay as a waterbody. Next the wetland layer had to be clipped to the extents of the watersheds being analyzed. The first step here was to create a copy of the Watershed_Subset layer and call it Watershed_Subset_Boundary. Select all objects on this new layer and combine them (set data aggregation as no data ). The wetland layer was set as the editable layer and all objects were selected and combined as one object. Then with all the wetlands (as one object) still selected, it was set as the target and an erase outside function was performed to get rid of all wetlands outside of the area of interest (Watershed_Subset_Boundary). This new layer was called NHR_Wetlands_Model_Sub. Then the newly clipped wetlands object was selected and set as the target layer. All the watersheds were then selected and a split operation was performed to clip all the wetlands to the watershed boundaries. A connection field was added to the browser and updated by a geographical based join (take the connection field from the watershed object where the wetland is within the watershed). Two new fields were added to the NHR_Wetlands_Model_Sub layer: A_ha_WL (area of the wetland in hectares) and A_sqkm_WL (area of the wetland in square kilometers). These columns were updated using the area function. With the new wetland layer prepared for use, the waterbody layer had to be clipped to the wetlands to ensure there was no overlap of objects. This new layer was called WQ_Model_Lakes_WLclip. One new field was added to the this layer: A_sqkm_WB (area of the waterbody in square kilometers). This column was updated using the area function. The copy of the watershed layer was created (Watershed_Subset) and all incomplete watersheds were deleted. This is the new master watershed layer. Here is the structure of the table and the field definitions:

123 Q_Wshd_Name: Name of quaternary watershed that the catchment falls in Q_Wshd_Num: Quaternary watershed number that the catchment falls in M_Wshd_Name: Model watershed name that the catchment falls in Watershed_Name: same as the waterbody name Connection: Unique name for each waterbody / watershed. This was created because there are many lakes with duplicated names Long: Longitude Lat: Latitude A_sqkm_Wshd_Total: Total area in square kilometers of the watershed including the surface area of the main waterbody A_sqkm_WB: Total area in square kilometers of the main waterbody A_sqkm_Wshd_ExWB: Total area in square kilometers of the watershed excluding the main waterbody A_ha_WL: Total area in hectares of the wetlands in the watershed A_percent_WL: Percentage of wetland area in the watershed In order to update the quaternary and coordinate fields, a copy of the WQ_Model_Lakes_WLClip layer saved as WQ_Model_Lakes_WLClip_LL with a Lat Long projection. This was opened and coordinate fields were added and then updated. This file was exported as a.csv called WQ_Model_Lakes_WLClip_LL_txt.csv. All tables were closed. The csv file was opened in MapInfo and the points were mapped from the coordinate fields creating centroids for the lakes. This result was saved as a new native tab file in UTM 83 projection called WQ_Model_Lakes_WLClip_LL_Map.tab. All

124 tables were closed and the new tab file was opened. The structure of this file was modified to include the fields Q_Wshd_Name and Q_Wshd_Num. The Watershed_4_Quaternary layer was opened. The fields were updated using a geographical join. Not all points fell within a Q watershed (although they should have) so these needed to be manually updated. The Watershed_Subset Layer was added to the map. The four fields (Q_Wshd_Name, Q_Wshd_Num, Long, Lat) in the watershed_subset layer were updated from the WQ_Model_Lakes_WLClip_LL_Map layer using the connection field as a join. The M_Wshd_Name was updated by importing the Lakes in Model 2011.xlsx located here: S:\WATER\Watqual\Model Review. The column was populated by using a tabular join on the connection field. This updated 402 out of the 472 watersheds. Rebecca Willison updated the remainder from checking with the previous model. The area function was used to update the A_sqkm_Wshd_Total field. The A_sqkm_WB field was updated from the area field in the WQ_Model_Lakes layer. The A_sqkm_Wshd_ExWB field was calculated by subtracting the first two fields. The A_ha_WL field was updated from the area fields in the NHR_Wetlands_Model_Sub layer. The A_percent_WL was updated using the following sql statement: A_ha_WL/A_ha_Wshd_Total(temp field)*100. A copy of the master watershed layer was then created and called Watersheds_Subset_Dev. A new copy had to be created because some of the waterbodies being analyzed fall outside of the District Boundary and we only have development information within the district boundary. A copy of the WQ_Model_Lakes_WLclip layer was created and called WQ_Model_Lakes_WLclip_Dev (for development). On both these two dev layers, the watershed and the waterbody for those instances where the waterbody fell outside of the district boundary had to be deleted. We can only get development statistics for a subset of the watersheds. See the process below for doing this stage. Creating the Lake and Watershed Subsets for Development Analysis Select all WQ_Model_Lakes_WLClip objects that are completely within the Muskoka Boundary layer. Save a copy of this query as WQ_Model_Lakes_WLClip_Dev

125 Scan the border for any lakes that were not included but should be (and vice versa). Select all watersheds for the development analysis.

126 These will be the two base layers you will use for the development stats generation. Creating the Base Development Layers Modify the Seguin Parcel layer structure to match that of the Muskoka Property Code Layer. Update the additional fields using the Prop_Codes_All_.tab layer. Create a copy of the Muskoka_Property_Codes layer and call it Muskoka_Property_Codes_WQ.tab. Copy and paste all Seguin parcels to the Muskoka_Property_Codes_WQ layer. Open the Muskoka_Crown_Land_MNR layer. Select all objects from the Muskoka_Property_Codes_WQ and Muskoka_Crown_Land_MNR layers where PropertyNum equals PropertyNum. Delete this selection (all crown Land parcels) from the Muskoka_Property_Codes_WQ layer. Save and pack. Select all objects from the Muskoka_Property_Codes_WQ layer that intersect the Watersheds_Subset_Dev layer. Invert selection and delete all parcels that fall outside of the development study area. Modify the table structure of Muskoka_Property_Codes_WQ to add a new field called WQ_Code.

127 Select all parcels from Muskoka_Prop_Codes_WQ where Classification = VOL, VRL, or AGG. Update the WQ_Code field for this selection to Vacant. Select all parcels from Muskoka_Prop_Codes_WQ where Classification = CP or COP. Save this into a Selection set called Comm. Select all from Comm where PropCode<>386 and PropCode<>400 and PropCode<>405 and PropCode<>406 and PropCode<>480 and PropCode<>482 and PropCode<>486 and PropCode<>490 and PropCode<>495 and PropCode<>496. Save this into a Selection set called Comm2. Update the WQ_Code field for this selection to Commercial. Select all parcels from Muskoka_Prop_Codes_WQ where PropCode = 381 or 382 or 486. Update the WQ_Code field for this selection to Trailers and Camping. Select all parcels from Muskoka_Prop_Codes_WQ PropCode=441 or PropCode=445 or PropCode=450 or PropCode=451 or PropCode=460 or PropCode=461 or PropCode=462 or PropCode=491. Update the WQ_Code field for this selection to Resort. Select all parcels from Muskoka_Prop_Codes_WQ where PropCode = 490. Update the WQ_Code field for this selection to Golf Course. Select all parcels from Muskoka_Prop_Codes_WQ where QW_Code =. Update the WQ_Code field for this selection to Developed. Open MuskokaUSA.tab. Select all from Muskoka_Property_Codes_WQ and MuskokaUSA where Muskoka_Property_Codes_WQ.PropertyNum = MuskokaUSA.Property_Num and MuskokaUSA.sewer="A". Update the WQ_Code field for this selection to Serviced. This is the base development layer to be used in the analysis. Getting the Stats for each buffer area Note: The following analysis needs to be performed in ArcGIS. Export the following layers to shapefile: Muskoka_Property_Codes_WQ, WQ_Model_Lakes_WLClip_Sub_Dev, Watershed_Subset_Dev Directory: \\sdmvfs02\ped\water\watqual\watershed_digitization\wq_model_2012\dev_s tats_in_esri Intersect the properties (Muskoka_Property_Codes_WQ) to the catchments (Watershed_Subset_Dev) and call the resulting layer Muskoka_Property_Codes_WQ_Int. Add a field to Muskoka_Property_Codes_WQ_Int (text 250 chrs) called Buff_Name and update it with the connection field using the field calculator. Delete the connection field. Create three buffer layers around the WQ_Model_Lakes_WLClip_Sub_Dev layer at distances of 100m, 200m, and 300m. Do this using the buffer tool in the analysis toolbox. The results will retain the attribute table which is required for this process.

128 Spatial join Muskoka_Property_Codes_WQ_Int to WQ_Model_Lakes_Buffer100 with a one to many join relationship. See below: Select all from Muskoka_Property_Codes_WQ_Int_SJ100 where field Buff_Name = Connection. These records will be used to create the development counts. Export all selected records to a new feature class called Muskoka_Property_Codes_WQ _Int_SJ100_Sel.shp. Export the table to a dbf file. Do this for the 200m and 300m parcel selections as well.

129 The results of the developed land buffer analysis will look something like this: Development Counts Property Code: Open the dbf files in excel and save them as xlsx files. Open Microsoft Access Import the three property buffer xlsx files (100, 200, 300m)