Revised Water Quality Model and Lake System Health Program

Hutchinson Environmental Sciences Ltd.

Prepared for: District Municipality of Muskoka Job #: J150074

April 2016

Final Report

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

April 5, 2016 HESL Job #: J150074

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: Hutchinson Environmental Sciences Ltd.

Neil J. Hutchinson, Ph.D. President [email protected]

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

Signatures

Dörte Köster, Ph.D. Tammy Karst-Riddoch, Ph.D. Senior Aquatic Scientist Senior Aquatic Scientist

Brent Parsons, M.Sc. Aquatic Scientist

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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 Hutchinson Environmental Sciences Ltd. 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

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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 2005.

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 Ltd1. 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 capacities2. 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 2010-2015 revision project at Hutchinson Environmental Sciences Ltd. 2The 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.

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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 sensitivity3 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 2010. 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 20134.

3 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.

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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 2000-2014 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 http://www.muskokawaterweb.ca/lake-data/cwn/cwn-projects

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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 model6 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 accuracy- such 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.

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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 (http://www.muskokawaterweb.ca/waterfront-living/healthy-shorelines).

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

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program 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// X Full Servicing Slope Dependent Setback 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),

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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 http://www.muskokawaterweb.ca/lake-data/muskoka-data/shoreline-land-use-maps) 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.

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program 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 No Increasing TP Trend Documented Blue-Green Algal Bloom 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

No Development Related TP as Cause? Yes

Limit Lot Creation Remedial Action Plan

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J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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.

Hutchinson Environmental Sciences Ltd.

J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

Table of Contents

Transmittal Letter Signatures Acronyms Preamble 1. Introduction ...... 1 2. Review of Watershed Characteristics and Export Coefficients ...... 2 2.1 Addition of New Watersheds ...... 2 2.2 Confirmation of Watershed, Lake and Wetland Areas ...... 3 2.3 Loadings from Wastewater Treatment Plants ...... 5 2.4 Loadings from Golf Courses and Urban Runoff ...... 6 3. Review of Lake Water Quality ...... 7 3.1 Dissolved Organic Carbon ...... 7 3.2 Total Phosphorus ...... 10 3.2.1 Data Quality ...... 10 3.2.2 Outlier Detection and Removal ...... 10 3.2.3 Ten-Year Mean Spring Turnover TP...... 13 3.2.4 Total Phosphorus Trends ...... 14 3.3 Relationship between Total Phosphorus and Dissolved Organic Carbon ...... 15 4. Review of Oxygen and Phosphorus Dynamics ...... 16 4.1 Selection of Lakes for Identification of Internal Phosphorus Loading ...... 17 4.1.1 Selection process ...... 17 4.1.2 Scheduling ...... 21 4.1.3 Late Summer Hypolimnetic Phosphorus Results ...... 21 4.2 Relationships of Bathymetry with Oxygen Status ...... 25 4.3 Relationships of TP and DOC with Oxygen Status ...... 27 4.3.1 Ordination Analysis ...... 27 4.3.2 Multiple Regression Analysis ...... 28 4.4 Literature Review on Phosphorus Retention and Internal Load ...... 30 4.5 Summary ...... 32 5. Embayments ...... 33 5.1 Lakes Joseph, Rosseau, Muskoka and Lake of Bays Embayments ...... 33 5.1.1 Approach to Embayment Criteria ...... 33 5.1.2 Data Sources ...... 35 5.1.3 Quality Control of Data ...... 35 5.1.4 Main Basin vs. Embayment Comparisons ...... 40 5.1.5 Recommendations for Modeling ...... 45 5.1.6 Summary ...... 48 5.2 Georgian Bay Embayments ...... 48 5.2.1 Current Monitoring and Modeling ...... 48 5.2.2 District of Muskoka Monitoring Data ...... 48 5.2.3 Data Gaps and Recommendations ...... 49

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J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

5.2.4 Other Monitoring Programs ...... 51 5.2.5 Conclusions and Recommendations ...... 52 6. Muskoka Water Quality Model Review and Update ...... 53 6.1 Model Results and Validation ...... 53 6.1.1 Potential Sources of Error ...... 54 6.2 Summary ...... 63 7. Development of a Planning Approach ...... 64 7.1 Rationale for a Revised Approach ...... 64 7.2 Management Triggers ...... 66 8. Integration, Conclusions and Recommendations ...... 68 8.1 Lake Planning and Management Triggers ...... 70 8.2 Causation Studies ...... 72 8.2.1 Examples of Causation Studies ...... 73 8.3 District of Muskoka Planning Implications ...... 77 8.4 Recommendations ...... 78 9. References ...... 81

List of Figures

Figure 1. Distribution of the previous and updated watershed areas...... 4 Figure 2. Distribution of differences between the previous and updated wetland areas...... 5 Figure 3. Distribution of mean dissolved organic carbon in Muskoka lakes (n=195)...... 8 Figure 4. Dissolved Organic Carbon in Muskoka Lakes from 2004-2011...... 9 Figure 5. Changes in Dissolved Organic Carbon in DESC lakes from 1980s to 2004-2005...... 9 Figure 6. Distribution of ten-year mean spring TP (2005-2014) in Muskoka lakes (n=196)...... 14 Figure 7. Relationship of total phosphorus and dissolved organic carbon in Muskoka lakes...... 16 Figure 8. Lake area and depth relationship for Muskoka lakes. (n=188)...... 25 Figure 9. Lake area and depth relationship for all lakes except three large lakes (Lakes Muskoka, Rosseau and Joseph) (n=185)...... 25 Figure 10. Lake area as a function of depth for lakes with oxic and anoxic hypolimnia...... 26 Figure 11. Lake area as a function of depth for small (<100 ha) lakes with oxic and anoxic hypolimnia. .. 27 Figure 12. Principal Component Analysis plot of morphometric and chemical characteristics for lakes that are more than 3 m deep...... 28 Figure 13. Number of total phosphorus measurements at MLA open-water stations used for embayment analysis...... 36 Figure 14. Relationship between MLA annual average TP (2001-2011) 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)...... 37 Figure 15. Relationship between average MLA and DMM spring TP concentrations (2001-2011) in main basin and embayments of Lakes Joseph, Muskoka, and Rosseau...... 39 Figure 16. Map of Median Total Phosphorus Concentrations at MLA Open Water Sampling Stations in Lakes Muskoka, Rosseau and Joseph (2001-2011)...... 42 Figure 17. TP concentrations in Lake Joseph and its embayments (2005–2011; MLA data)...... 43 Figure 18. TP concentrations in Lake Muskoka and its embayments (2005–2011; MLA data)...... 43

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J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

Figure 19. TP concentrations in Lake Rosseau and its embayments (2005–2011; MLA data)...... 44 Figure 20. TP concentrations in Lake of Bays and its embayments (2002–2011; LOBA data)...... 44 Figure 21. Changes in Total Phosphorus from Spring to Summer at MLA Sampling Stations...... 47 Figure 22. Accuracy of the MWQM model to predict phosphorus concentration (n=206 lakes). Dotted lines enclose +/-20% about the 1:1 line...... 54 Figure 23. Model error compared to potential phosphorus load from development, all lakes...... 55 Figure 24. Model error for lakes with <10% potential development phosphorus load (D.I <1.1; n=36). ... 56 Figure 25. Comparison of 2005 and 2012 estimates of wetland areas for individual lakes in the Dwight subwatershed...... 57 Figure 26. Model error as a function of wetland area...... 58 Figure 27. Relationship of model error to DOC in Muskoka lakes...... 58 Figure 28. Relationship of model error to areal water load...... 59 Figure 29. Relationship of model error to ratio of watershed area/lake area...... 60 Figure 30. Relationship of model error to lake maximum (top) and mean (bottom) depth...... 62 Figure 31. Human phosphorus loading and relationship between TP and DOC in Muskoka Lakes...... 67 Figure 32. Proposed Lake System Health Planning Approach...... 80

List of Tables

Table 1. Revised Subwatershed Delineations for Model...... 3 Table 2. Annual Flows and Total Phosphorus Loads from Wastewater Treatment Plants in Muskoka (2006- 2010) ...... 6 Table 3. List of Outlier TP Measurements Detected in DMM Dataset 2000-2014 ...... 12 Table 4. Lakes With Significant Decreasing Trends (p<0.10) in Total Phosphorus: 2000-2014 ...... 15 Table 5. Screening Level 1 Candidate Anoxic Lakes (n=95) ...... 18 Table 6. List of Lakes Removed From the Anoxia Sampling List and Rationale ...... 19 Table 7. Forty Lakes Recommended for 2011 Late Summer Bottom TP Sampling...... 20 Table 8. Priority 1 Lakes, No Anoxia ...... 22 Table 9. Priority 2 and 3 Lakes, Substantial and Weak Anoxia ...... 23 Table 10. Hypolimnetic TP Measurements in Late Summer 2011...... 24 Table 11. Multiple Regression Statistics of Anoxia predicted by Depth, Area, DOC and TP ...... 29 Table 12. Comparison of Average TP Concentrations Collected by Both the DMM and MLA ...... 38 Table 13. Average Spring TP Concentrations (May-early June) Collected by the DMM and MLA ...... 39 Table 14. Comparison of Main Basin and Embayment TP and Physical Characteristics. Bold values are significant at p <0.05...... 41 Table 15. District of Muskoka TP Monitoring Data for Georgian Bay Embayments (2000-2011) ...... 49 Table 16. Recommendations for Monitoring Inner and Outer Bay Sampling Locations ...... 51 Table 17. Georgian Bay Embayment Sites and Inland Lakes monitored by Georgian Bay Forever...... 52 Table 18. Predictive Error of the MWQM (n=206 lakes) ...... 54 Table 19. Percentage Error of Phosphorus Concentrations in Lakes with Little Development ...... 56 Table 20. Relationship of model error to watershed position of lake...... 61 Table 21. Relationship of Model Error to Hypolimnetic Oxygen Status ...... 63 Table 22. Model Components and Evaluation of Confidence ...... 65 Table 23. Proposed BMPs for “Standard” and “Enhanced” Lake Classifications...... 79

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J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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 2005-2014 10-Year Mean and 15 year (2000- 2014) Trend Assessments (Excl. Outliers) Appendix D. Correspondence and Meeting Minutes Appendix E. 2014 Total Phosphorus Update Appendix F. List of MNR Lake Trout Lakes Within the District Municipality of Muskoka.

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J150074 , District Municipality of Muskoka Revised Water Quality Model and Lake System Health Program

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 1994.

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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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 Hutchinson Environmental Sciences Ltd. (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 2010-2015 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.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

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 New New Old “Parent” New “Parent” “Parent” Watershed Waterbody Catchment Subwatershed Watershed watershed size watershed Name Size (ha) Size (ha) size (ha) (ha)

Ivy Lake 2.5 17.9 Sparrow Lake Prospect Lake 951 827 Island Lake 8.2 56.5 Sparrow Lake Prospect Lake 951 827 Haller Lake 13 1,462 Lake Vernon Buck River 7,325 736 Samlet Lake 21.7 237 Lake Vernon Big East River n/a* 44,253 Kawpakwagog South Muskoka 8 4,770 Muskoka River 14,686 9,547 River River *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.

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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.

160

140

120

100

Previous Model Frequency 60 Updated Model

0 1 2 3 4 5 6 7 Watershed Area (km2)

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 2009. 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.

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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.

180 160 140 120 100

80 Frequency 60 40 20 0 -100 -50 -25 0 25 50 100 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 (2006-2010) 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 (2006-2010) 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.

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Table 2. Annual Flows and Total Phosphorus Loads from Wastewater Treatment Plants in Muskoka (2006-2010)

Total Allowable Total Flow Total TP Load P Municipality Receiver Loading m3/yr kg/yr kg/yr Min Max Ave. Min Max Ave. Moon Bala 60 117,660 142,700 130,635 2 3 3 River South Baysville Muskoka n/a 7,571 58,367 35,885 0 5 3 River Bracebridge - Mech. 949,530 1,184,628 1,070,267 63 296 184 Bracebridge - Lagoons 9,849 438,184 200,610 2 246 80 Muskoka Bracebridge Total 500 1,078,691 1,527,021 1,270,877 86 489 264 River Lake Gravenhurst 584 959,933 1,180,938 1,077,318 67 94 84 Muskoka Fairy Huntsville -MV & GP 892 1,150,105 2,244,067 1,843,153 149 283 213 Lake Conger Mactier n/a 23,554 51,594 40,179 2 4 3 Marsh Indian Port Carling n/a 120,382 203,785 153,361 19 43 33 River Severn Port Severn 100 61,400 75,124 65,499 2 5 3 River

2.4 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/m2/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/m2/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 m2/hole (2 ha) of cleared area assigned the export coefficient of 14 mg/m2/yr.

The export from cleared cottage lots was estimated on an assumed average cleared area of 2000 m2 for each cottage lot. A phosphorus export coefficient of 45 mg/m2/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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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/m2/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/m2/yr (Dillon et al. 1986) was therefore subtracted from 22.5 to produce a final export figure of 17 mg/m2/yr for cleared areas of cottage lots. The total export assumed for each cottage lot was therefore 2000m2 * 17 mg/m2/yr = 0.034 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/m2/yr versus 9.8 mg/m2/yr).

Runoff from urban areas of Muskoka was assigned an export coefficient of 39.5 mg/m2/yr, which was calculated as the value derived from the Lake Wilcox study minus the 5.5 mg/m2/yr for natural export. Published urban or high density residential phosphorus export coefficients range from 38 to 410 mg/m2/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/m2/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.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

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 2004-2011 50

20 Frequency 10

0 2 3 4 5 6 7 8 9 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 2011. 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.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

Figure 4. Dissolved Organic Carbon in Muskoka Lakes from 2004-2011.

Mean DOC of Muskoka Lakes 6 5 4 3

2 DOC(mg/L) 1 0 2004 2005 2006 2007 2008 2009 2010 2011 Year

Figure 5. Changes in Dissolved Organic Carbon in DESC lakes from 1980s to 2004-2005.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

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 2001. 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.

3.2.1 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).

3.2.2 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,

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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 2000-2011 dataset and 54 outliers were detected in the 2000-2014 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 2008. The remaining lakes were sampled once each year, during spring overturn.

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Table 3. List of Outlier TP Measurements Detected in DMM Dataset 2000-2014

Outliers (Lake Joseph) Outliers Site_Name TP (g/L) Date Site_Name TP (g/L) Year Joseph River 22.2 27-Aug-10 Bonnie Lake 24.4 2010 Lake Joseph-Cox Bay 31.8 27-Aug-10 Camp Lake 17.6 2014 Lake Joseph-Cox Bay 12.4 25-May-09 Clear Lake BB 11.9 2000 Lake Joseph-Cox Bay 10.3 17-Aug-09 Clearwater Lake HT 17.6 2000 Lake Joseph-Hamer Bay 10.6 26-Aug-09 Cornall Lake 14.4 2002 Lake Joseph-Main 11.4 17-Aug-09 Fawn Lake 52.1 2000 Lake Joseph-Main 9.6 4-Aug-09 Galla Lake 15.6 2000 Lake Joseph-Main 8.3 25-May-09 Go Home Lake 13.3 2000 Lake Joseph-Main 7 23-Jul-09 Gull Lake 18.4 2000 Lake Joseph-North 11.6 25-May-09 Lake Huron-Little Go-Home Bay 11.9 2009 Lake Joseph-North 8.1 4-Jun-08 Lake of Bays - Haystack Bay 7.5 2000 Lake Joseph-North 7.7 4-Aug-09 Lake of Bays - Rat Bay 9.1 2009 Lake Joseph-South 16.2 17-Aug-09 Lake Vernon - Main 14.5 2000 Lake Joseph-South 16 4-Aug-09 Lake Vernon - North Bay 19.9 2000 Lake Joseph-South 13.2 23-Jul-09 Lake Waseosa 27.1 2001 Lake Joseph-South 11.3 11-May-09 Little Lake Joseph 14.3 2014 Lake Joseph-South 7.7 15-Jul-10 Little Long Lake 22.5 2000 Little Lake Joseph 61 6-Aug-14 Longline Lake 17.5 2002 Longs Lake 27.0 2000 Loon Lake 66.8 2010 McKay Lake 30.0 2001 Morrison Lake 13.5 2013 Oudaze Lake 18.6 2000 Ril Lake 17.0 2001 Riley Lake 27.7 2000 Silver Lake GR 22.0 2002 Six Mile Lake - Cedar Nook Bay 11.1 2001 Skeleton Lake 7.2 2008 Spence Lake - North 22.6 2001 Stewart Lake 25.2 2010 Sunny Lake 16.7 2000 Tackaberry Lake 12.6 2011 Three Mile Lake - Hammels Bay 21.0 2000 Tooke Lake 11.1 2012 Toronto Lake 10.4 2001 Tucker Lake 21.3 2005

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3.2.3 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 (2005-2014) 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 1990-1998 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 3.2.4.

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 2005-2014 is largely reduced from the value of ~40% for data collected from 1990-1998 (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.

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Figure 6. Distribution of ten-year mean spring TP (2005-2014) in Muskoka lakes (n=196).

3.2.4 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 2000-20147. There are 196 lakes for which measured concentrations exist but for 6 of these8, 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 2000-2014. 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 2000. 8 Barrons Lake, Rogers Cove (Fairy Lake), Go Home Bay, Little Go Home Bay, South Basin of Lake Muskoka,and McLeans Bay of Sparrow Lake

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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 (2000-2012) 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 2000-2012 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: 2000-2014

Site residuals n slope R2 SLR p- normal Kendall's Kendall's value (if >= Tau p-value 0.05) Mirror.Lake 6 -0.24 0.98 0.00 0.21 -1.00 0.00 Tackaberry.Lake 4 -0.18 0.99 0.00 0.38 -1.00 0.08 Clark.Lake 5 -0.44 0.95 0.01 0.17 -1.00 0.02 Medora.Lake 6 -0.23 0.83 0.01 0.93 -0.69 0.06 Moot.Lake 6 -0.87 0.72 0.03 0.78 -0.73 0.06 Kahshe.Lake...Main 7 -0.32 0.62 0.04 0.84 -0.68 0.03 Lake.of.Bays...SMRB 6 -0.23 0.70 0.04 0.97 -0.73 0.06 Longline.Lake 5 -0.42 0.81 0.04 0.70 -0.74 0.08 Three.Mile.Lake...Hammels.Bay 7 -0.29 0.58 0.05 0.42 -0.43 0.24 Clearwater.Lake.GR 7 -0.23 0.57 0.05 0.64 -0.52 0.14 Six.Mile.Lake...Provincial.Park.Bay 7 -0.11 0.55 0.06 0.78 -0.59 0.07 Prospect.Lake 6 -0.30 0.63 0.06 0.35 -0.73 0.06 High.Lake 7 -0.31 0.54 0.06 0.72 -0.43 0.24 Lake.Joseph.Main 11 -0.20 0.23 0.14 0.00 -0.45 0.06 Gartersnake.Lake 5 -0.25 0.74 0.06 0.96 -0.80 0.08 Lake.Muskoka...Muskoka.Bay 8 -0.65 0.46 0.07 0.38 -0.50 0.11 Mary.Lake 7 -0.14 0.48 0.08 0.86 -0.49 0.13 Kahshe.Lake...Grants.Bay 6 -1.06 0.53 0.10 0.15 -0.60 0.14 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 (2004-2011) and average TP concentrations (2002 to 2011) improved this relationship (r2 = 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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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 30 28 26 24 22

g/L) 20  18 16 y = 1.3715x + 2.3406 14 R²(adj) = 0.45; p<0.001 12 10

Total Phosphorus ( Phosphorus Total 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 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).

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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.

4.1.1 Selection process

The selection process was carried out in the spring of 2011 using data from the previous 10 years (2000- 2010). 4.1.1.1 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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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 140-1 ATKINS LAKE Macaulay 4748-1 LITTLE LONG LAKE Cardwell 239-1 BASS LAKE Ryde 2773-1 LONG LAKE Bala 6929-1 BAXTER LAKE Baxter 6509-1 LONGLINE LAKE Ridout 276-1 BEARPAW LAKE Wood 6915-1 MAINHOOD LAKE Stephenson 6903-1 BEN LAKE Ryde 7245-1 McDONALD LAKE Gibson 441-1 BLACK LAKE Medora & Wood 3123-2 McKAY LAKE Draper 532-1 BONNIE LAKE Macaulay 7044-1 McREY LAKE Macaulay 604-2 BRUCE LAKE Medora (North) 3171-1 MEDORA LAKE Medora & Wood 616-1 BUCK LAKE Sinclair & Finlayson 3181-3 MENOMINEE LAKE McLean 676-1 BUTTERFLY LAKE Medora & Wood 3325-2 MOOT LAKE McLean 702-1 CAMEL LAKE Watt 3331-1 MORRISON LAKE Wood 747-1 CARDWELL LAKE Cardwell 3441-1 NEILSON LAKE Wood (South) 775-1 CASSIDY LAKE Medora (North) 3469-1 NINE MILE LAKE Wood (South) 7061-1 CHUB LAKE Brunel 7064-18 NORTH BAY Baxter 5419-1 CLEAR LAKE Medora & Wood 4074-1 NORTH MULDREW LAKE Muskoka 976-2 COOPER LAKE Franklin 4109-1 NUTT LAKE Watt 988-1 CORNALL LAKE Morrison 6916-1 OTTER LAKE Brunel 6913-1 DARK LAKE Bala 4202-1 OUDAZE LAKE Chaffey 1173-1 DEVINE LAKE Stephenson 5145-4 PAINT LAKE Ridout 1193-1 DICKIE LAKE McLean 4309-4 PENINSULA LAKE - EAST Sinclair & Finlayson 1237-1 DOTTY LAKE Sinclair & Finlayson 4365-1 PIGEON LAKE Muskoka 1326-2 ECHO LAKE McLean 4378-1 PINE LAKE Wood (South) 1420-4 FAIRY LAKE - NMRB Chaffey 4428-1 PORCUPINE LAKE Ridout 1440-1 FAWN LAKE Stephenson 4468-1 PROSPECT LAKE Draper 7243-1 FLATROCK LAKE Gibson 4567-2 REBECCA LAKE Sinclair & Finlayson 1524-1 FOOTE LAKE Sinclair & Finlayson 6921-1 RICKETTS LAKE Medora (North) 1550-1 FOX LAKE Stisted 4627-2 RIL LAKE Ridout 1626-1 GARTERSNAKE LAKE Draper 4628-1 RILEY LAKE Ryde 1644-2 GIBSON LAKE - NORTH Gibson 4696-1 ROSE LAKE Stephenson 1644-1 GIBSON LAKE - SOUTH Gibson 4750-1 RYDE LAKE Ryde 7042-1 GILLEACH LAKE Macaulay 4951-2 SILVER LAKE Muskoka 5750-1 GOLDEN CITY LAKE Stisted 4952-1 SILVER LAKE Port Carling 1777-1 GRINDSTONE LAKE Ridout 4959-1 SILVERSANDS LAKE Freeman 1798-1 GULLWING LAKE Wood (South) 4978-1 SIX MILE LAKE - CEDAR NOOK Gibson 6914-1 HALFWAY LAKE Macaulay 4980-1 SIXTEEN MILE LAKE Franklin 1863-1 HARDUP LAKE Sinclair & Finlayson 5058-17 SOUTH BAY Baxter 1923-1 HEALEY LAKE Macaulay 5094-3 SPARROW LAKE Morrison 1953-1 HESNER'S LAKE Bala 5102-1 SPENCE LAKE - NORTH Draper 1964-1 HIGH LAKE Watt 5166-1 STEWART LAKE Medora (North) 2154-2 JESSOP LAKE Chaffey 6923-1 STONELEIGH LAKE Macaulay 2460-8 JOSEPH RIVER Medora (North) 5303-5 TASSO LAKE Sinclair & Finlayson 2217-5 KAHSHE LAKE - Grants Bay Ryde 6925-1 THINN LAKE Draper 2465-16 LAKE MUSKOKA - WHITESIDE BAY Wood (South) 5361-1 THREE MILE LAKE Morrison 2465-10 LAKE MUSKOKA - MUSKOKA BAY Muskoka 5362-5 THREE MILE LAKE - HAMMEL'S Watt 6455-6 LAKE VERNON - HUNTERS BAY Chaffey 7235-1 TUCKER LAKE Brunel 6455-8 LAKE VERNON - NORTH BAY Chaffey 7064-24 TWELVE MILE BAY - WEST Freeman 2529-1 LEECH LAKE Oakley 5710-2 WALKER LAKE Sinclair & Finlayson Lake_# Lake_Name WardName 6927-1 WEISMULLER LAKE Draper 4748-1 LITTLE LONG LAKE Cardwell 5961-1 WOOD LAKE Oakley

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Table 6. List of Lakes Removed From the Anoxia Sampling List and Rationale

Site Name Reason for Removal from Anoxia Sampling List Black Lake 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 hypoxic 2 out of 8 years at 6 m from bottom, always >6.7 at 5m, but has a history of algal blooms Dotty Lake hypoxic 1 year of 4, at 28 m but not 26 m Dickie Lake sampled by MOE Dorset, only stratifies weakly and hypoxia occurs occasionally Fairy Lake, North hypoxic 3 of 4 years but marginally so Muskoka River Flatrock Lake marginally hypoxic in 1 of 4 years, 1 other year was 1.6 mg/L at bottom Hammells Bay of hypoxic but well sampled by MOE work in the past Three Mile Lake High Lake hypoxic 2 of 4 years but only at bottom (14m) DO is very high at 12m Jessop hypoxic in 2 of 4 years at 3m, but DO very high at 2m Kahshe Lake Grants Bay is hypoxic, but DO in the Main Basin only drops to 2-2.5mg/L

North Bay (GB) hypoxic but is included in GBF/SDEA sampling program Joseph River hypoxic in 2 years out of 4 at 7m-8m Muskoka Bay hypoxic in 1 of 4 years (14m) but has been in past; kept in to document its recovery over time North Bay of Lake hypoxic in 1 of 6 years Vernon Morrison Lake hypoxic in 3 of 5 years but degree of anoxia is not strong Oudaze Lake hypoxic in 1 of 4 years at 32m depth, For 3 years max. measured depth was only 18-20m. Paint Lake hypoxic in 1 of 6 years and marginal Prospect Lake hypoxic in 1 of 4 years Rebecca hypoxic in 2 of 5 years but marginal Silver Lake GR hypoxic in 1 of 5 years Six Mile Lake covered by TGB program South Bay (GB) hypoxic but is included in GBF/SDEA sampling program Stoneleigh Lake hypoxic in 1 of 3 years - stratifies at 3m Tasso Lake hypoxic in 1 of 5 years Tucker Lake 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

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4.1.1.2 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 4365-1 PIGEON LAKE Muskoka 0.61 6 6921-1 RICKETTS LAKE Medora (North) 0.27 6 1440-1 FAWN LAKE Stephenson 0.88 7 1953-1 HESNER'S LAKE Bala 0.25 7 3181-3 MENOMINEE LAKE McLean 1.01 7 3325-2 MOOT LAKE McLean 0.49 7 239-1 BASS LAKE Ryde 0.41 8 276-1 BEARPAW LAKE Wood 0.26 8 775-1 CASSIDY LAKE Medora (North) 0.55 8 1923-1 HEALEY LAKE Macaulay 1.27 8 4109-1 NUTT LAKE Watt 0.08 8 5102-1 SPENCE LAKE - NORTH Draper 1.07 8 5361-1 THREE MILE LAKE Morrison 0.85 8 6903-1 BEN LAKE Ryde 0.36 9 1173-1 DEVINE LAKE Stephenson 0.39 9 1798-1 GULLWING LAKE Wood (South) 0.86 9 6455-6 LAKE VERNON - HUNTERS BAY Chaffey 0.81 9 3171-1 MEDORA LAKE Medora & Wood 0.41 9 6916-1 OTTER LAKE Brunel 0.21 9 988-1 CORNALL LAKE Morrison 0.25 10 7044-1 McREY LAKE Macaulay 0.2 10 3441-1 NEILSON LAKE Wood (South) 0.17 10 4627-2 RIL LAKE Ridout 1.45 10 4959-1 SILVERSANDS LAKE Freeman 0.17 10 4980-1 SIXTEEN MILE LAKE Franklin 0.8 10 6913-1 DARK LAKE Bala 0.16 11 1524-1 FOOTE LAKE Sinclair & Finlayson 1.25 11 4696-1 ROSE LAKE Stephenson 0.33 11 7061-1 CHUB LAKE Brunel 0.27 12 976-2 COOPER LAKE Franklin 0.27 12 1326-2 ECHO LAKE McLean 2.25 12 4750-1 RYDE LAKE Ryde 0.82 12 140-1 ATKINS LAKE Macaulay 0.13 13 702-1 CAMEL LAKE Watt 0.58 13 1550-1 FOX LAKE Stisted 1.45 13 2529-1 LEECH LAKE Oakley 0.81 13 5961-1 WOOD LAKE Oakley 3.9 13 1644-2 GIBSON LAKE - NORTH Gibson 2.64 14 1644-1 GIBSON LAKE - SOUTH Gibson 2.64 14 2465-10 LAKE MUSKOKA - MUSKOKA BAY Muskoka 4.1 14

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4.1.2 Scheduling

The DMM field crews were scheduled to sample 89 lakes in 2011. 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 9.

4.1.3 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.

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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

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Table 9. Priority 2 and 3 Lakes, Substantial and Weak Anoxia

Priority 2 Lakes Priority 3 Lakes (Substantial Anoxia) (Weak Anoxia) Atkins Bass Bearpaw Camel Ben Cassidy Gibson-North Chub Gibson-South Cooper Gullwing Cornall Hesner's Dark Lake Muskoka-Muskoka Devine Bay McRey Echo Menominee Fawn (Deer) Moot Foote Neilson Fox Otter Healey Ril Leech Rose Medora Ryde Nutt (Mud) Lake Vernon-Hunters Pigeon Wood Ricketts Silversands Sixteen Mile (Long) Spence-North Three Mile

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Table 10. Hypolimnetic TP Measurements in Late Summer 2011.

Surface TP Bottom Lake TP Sample Oxygen Recommen- 2002-2011 TP 2011 P Recycling Depth sampling above Status for dations for Lake (ug/L) (ug/L) confirmed? (m) depth (m) bottom (m) model monitoring Atkins Lake 8.7 6.3 No 13 4 9 anoxic deeper Bass Lake GR 15.2 22.5 Yes 8 3 5 anoxic deeper Bearpaw Lake 14.6 13.5 No 8 5 3 not anoxic later Ben Lake 8.5 11.9 Maybe 9 5 4 anoxic deeper, later Camel Lake 9.2 6.5 No 13 10 3 not anoxic same Cassidy Lake 10.8 10.9 No 8 8 0 not anoxic same Chub Lake HT 9.8 5.6 No 12 5 7 anoxic deeper Cooper Lake 8.8 8.0 No 12 4 8 anoxic deeper Cornall Lake 11.1 11.0 No 10 8 2 not anoxic same Dark Lake 9.7 9.8 No 11 8 3 not anoxic same Devine Lake 12.5 10.1 No 9 3 6 anoxic deeper Echo Lake 6.6 6.7 No 12 4 8 anoxic deeper Fawn Lake 17.0 27.0 Yes 7 7 0 anoxic less deep Foote Lake 9.4 12.0 No 11 6 5 not anoxic deeper Fox Lake 12.4 13.0 No 13 9.5 3.5 not anoxic deeper Gibson Lake - North 10.0 17.8 yes 14 11 3 anoxic same Gibson Lake - South 12.2 19.2 yes 14 13 1 anoxic same Gullwing Lake 13.5 21.5 yes 9 7 2 anoxic same Healey Lake 8.9 19.4 yes 8 6 2 anoxic same Hesners Lake 7.9 9.1 Maybe 7 7 anoxic later Lake Muskoka - Muskoka B. 10.3 7.3 No 14 10 4 not anoxic deeper, later Lake Vernon - Hunters B. 10.2 22.9 Yes 9 9 0 anoxic less deep Leech Lake 8.9 6.3 No 13 6 7 anoxic deeper McRey Lake 11.5 8.5 No 10 5 5 anoxic deeper, later Medora Lake 8.0 9 No 9 6 3 not anoxic same Menominee Lake 8.7 9.6 No 7 4 3 not anoxic later Moot Lake 11.7 13.3 Maybe 7 4 3 anoxic later Neilson Lake 14.7 62.7 Yes 10 10 0 anoxic less deep Nutt Lake 8.6 6.8 No 8 7 1 not anoxic same Otter Lake 8.0 26.9 Yes 9 6 3 anoxic same Ricketts Lake 10.8 6.8 No 6 4.5 1.5 not anoxic same Ril Lake 9.0 6.8 No 10 7 3 not anoxic later Rose Lake 14.1 17.3 Maybe 11 3 8 anoxic deeper Ryde Lake 21.1 13.1 No 12 8 4 anoxic deeper, later Silversands Lake 9.4 10.3 No 10 8 2 not anoxic same Sixteen Mile Lake 7.3 7.6 No 10 8 2 not anoxic later Spence Lake - North 11.4 7.3 No 8 9 -1 anoxic less deep Three Mile Lake GR 18.2 10.4 No 8 7 1 not anoxic same Wood Lake 7.2 13.3 Yes 13 10 3 anoxic same

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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 r2 = 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 r2 = 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).

1400

1200 R² = 0.3566 1000

800

Area (ha) Area 600

400

200

0 0 10 20 30 40 50 60 Depth (m)

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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.

1600 1400 1200 1000

800 Oxic

Area (ha)Area 600 anoxic 400 200 0 0 10 20 30 40 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.

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Figure 11. Lake area as a function of depth for small (<100 ha) lakes with oxic and anoxic hypolimnia.

100 90 80 70 60 50 Oxic

Area (ha)Area 40 Anoxic 30 20 10 0 0 10 20 30 40 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 well- mixed and would not respond to TP or DOC concentrations resulting from deepwater anoxia.

4.3.1 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.

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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.

4.3.2 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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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< 0.0007). 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 0.458 R Square 0.210 Adjusted R Square 0.164 Standard Error 0.452 Observations 74

Standard

Coefficients Error t Stat P-value Intercept -0.15 0.2708 -0.56 0.57 Depth 0.002 0.0077 0.28 0.78 Area 0.0005 0.0022 0.24 0.81 DOC -0.004 0.0412 -0.09 0.93

TP 0.0745 0.0210 3.55 0.0007

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 = 0.265 + 0.03 * 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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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 9 to overestimating phosphorus retention (RP, the loss of phosphorus from the water column to the sediments ), 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.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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 (Ƭw ) 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 Ƭw <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

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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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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 7. 5. 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.

5.1.1 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:

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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.

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5.1.2 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).

5.1.3 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.

5.1.3.1 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 2011. Screening through a 80 µm mesh is the standard method in DMM monitoring and was adopted by the MLA in 2011. 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 (2001-2011) 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 25th and 75th 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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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.

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5.1.3.2 Data Comparison Muskoka Lakes Association vs. District Municipality of Muskoka

The following sites were monitored by both the MLA and DMM during 2001-2011: 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 10th 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 (2001-2011) 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).

g/L) 8 

y = 0.67x + 1.62 6 R² = 0.53

MLA MLA TP( p < 0.001

0 0 2 4 6 8 10 12 14 DMM TP (ug/L)

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Table 12. Comparison of Average TP Concentrations Collected by Both the DMM and MLA

Paired Test Mean TP (μg/L) in 2001-11 Lake Basin/Embayment Statistics DMM MLA p df* Lake Joseph main basin 4.7 3.5 0.10 4 Cox Bay 5.0 4.6 0.26 6 Hamer Bay 3.9 4.2 0.33 3 Little Lake Joseph main basin 5.8 5.0 0.23 4 Lake Muskoka main basin 5.4 6.5 0.12 2 Bala Bay 6.1 5.7 0.72 4 Dudley Bay 5.9 5.2 0.23 3 Muskoka Bay 10.1 8.5 0.24 5 Whiteside Bay 5.7 5.3 0.50 1 Lake Rosseau main basin 5.8 4.9 0.02 2 Brackenrig Bay 8.9 9.7 0.75 3 East Portage Bay 7.0 4.8 0.04 3 Skeleton Bay** 7.7 4.2 - 0 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.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

Table 13. Average Spring TP Concentrations (May-early June) Collected by the DMM and MLA

Paired Test Mean TP (μg/L) in 2001-11 Lake Basin/Embayment Statistics DMM MLA p df Lake Joseph main basin 4.7 4.7 0.53 3 Cox Bay 5.2 5.8 0.19 6 Hamer Bay 3.9 5.3 0.25 3 Little Lake Joseph main basin 6.0 5.3 0.55 4 Lake Muskoka main basin 5.3 6.7 0.65 1 Bala Bay 6.6 6.3 0.09 2 Dudley Bay 5.9 5.0 0.15 2 Muskoka Bay 10.5 9.2 0.56 2 Whiteside Bay 6.0 5.7 0.50 1 Lake Rosseau main basin 5.8 5.0 0.30 2 Brackenrig Bay 8.9 7.9 0.39 3 East Portage Bay 7.0 5.6 0.47 1 Skeleton Bay 7.7 3.8 - 0

Figure 15. Relationship between average MLA and DMM spring TP concentrations (2001-2011) in main basin and embayments of Lakes Joseph, Muskoka, and Rosseau.

14 1:1 (x=y) 12

6 MLA MLA TP(ug/L) 4 y = 0.40x + 3.39 R² = 0.25 2 p < 0.001

0 0 2 4 6 8 10 12 14 DMM TP (ug/L)

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5.1.4 Main Basin vs. Embayment Comparisons

5.1.4.1 Data analysis

Paired statistical comparisons of embayment TP values were made with main basin values to control for intra- station 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 2005-2011 were used, but the majority of the main-basin TP data were collected during 2005-2007 for lakes Muskoka and Rosseau (87% and 86%, respectively), while more data were available for 2008-11 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 2002-2011. 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 2011. 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).

5.1.4.2 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 2004-11 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.

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Table 14. Comparison of Main Basin and Embayment TP and Physical Characteristics. Bold values are significant at p <0.05.

Signficance of Difference (Main - Bay) Bay-specific Characteristics Modeling Lake Embayment df Mean diff. Mean Diff. River Depth bathymetry land use Currently Modeling Recommended? (MLA/LOBA) (DMM) Influence (ft) barrier Modeled (2005)? Lake Cox Bay narrows, large Joseph 25 -1.19 0.25 Small 38 distance Golf Course Yes Yes (JOS-1) Foot’s Bay drainage from (240 ft) 3 0.11 No 120 minor narrows GC No no Gordon Bay 21 0.23 Small 114 narrows - No no Hamer Bay no, not DMM, not different 28 -0.48 0.65 No 150 No GC, wetland No enough; BUT: Trends? Little Lake narrows, large 3 small Joseph 22 -1.03 0.3 Small 127 distance wetlands Yes Yes Still’s Bay 23 -0.89 Small 30 no GC, wetlands No Yes Stanley Bay 40 0.01 Small 160 no - No No Lake Arundle Lodge 1 0.425 No 40 distance wetlands No No Muskoka Bala Bay narrows, large 2 wetlands, (MUS-3) 18 0.86 0.57 No 80 distance village Yes Yes 170 ft Beaumaris 20 0.21 No 90 no GC No No Boyd Bay 9 -1.39 Small 24 minor narrows wetland No No, small and open to lake Dudley Bay 19 0.54 -0.18 No 60 narrows wetlands Yes Yes, model already set up East Bay 17 -0.14 No 50 no wetlands No No Eilean Gowan Island 5 -1.24 No 80 no - No No, open to lake Muskoka Bay shallow 18 -2.41 -4.48 No 40 narrows urban Yes Yes Muskoka Sands GC, high density 19 -1.00 Medium 40 no residential No no, open to lake North Bay 6 1.20 No 60 narrows - Yes Yes, model already set up Stephen’s Bay 2 1.25 No 40 no - No No Walker’s Point 13 0.05 No 30 no - No No Willow Beach 19 -1.13 No 50 no - No No Whiteside Bay Yes, large bay, model 7 0.78 -0.12 No 30 narrows Wetland Yes already set up Lake Arthurlie Bay 8 -0.18 No 23 No wetlands No No Rosseau shallow 60% cleared, (ROS-1) Brackenrig Bay 16 -4.66 -3 No 14 narrows Ag Yes Yes 290 ft Morgan Bay 0 - Small 80 distance - Yes Yes, model already set up 2 GCs, Minett 17 -0.49 No 40 No wetland No No Muskoka Lakes Golf & Country Club 13 -0.30 No 30 No GC No No East Portage Bay 16 -0.67 -1.2 No 40 open narrows roads, Ag Yes Yes Royal Muskoka Island 8 -1.00 No 120 No - No no Rosseau North 12 -0.34 Large 290 No - No no shallow Skeleton Bay 0 - -2.12 Medium 65 narrows road Yes Yes north of 3- Mile Lake Tobin’s Island 5 -1.24 Inflow 90 several narrows wetlands No No, too open south of 3- Mile Lake GC, village, Windermere 16 -0.74 Inflow 80 several narrows Ag No No, too open Lake of Dwight Bay No. Difference due to river Bays 200 influence (regulating local 41 -0.41 -2.26 Large 170 narrows - No sources not effective) ft Haystack Bay 39 -0.61 0.54 No 50-130 narrows - Yes Yes Ten Mile Bay 28 -0.31 Small 80 distance - Yes Yes, model already set up South Portage Yes, model already set up, Bay shallow difference ecologically 4 - -1.9 small 155 narrows Yes significant

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Figure 16. Map of Median Total Phosphorus Concentrations at MLA Open Water Sampling Stations in Lakes Muskoka, Rosseau and Joseph (2001-2011).

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Figure 17. TP concentrations in Lake Joseph and its embayments (2005–2011; MLA data).

TP(ug/L)

JOS COX FTB GNB HMB LLJ STI STN

Figure 18. TP concentrations in Lake Muskoka and its embayments (2005–2011; MLA data).

TP (ug/L)

MUS ARN BAL BMR BOY DUD EAS ELG MBA MSN NRT STE WAK WLB WTS

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Figure 19. TP concentrations in Lake Rosseau and its embayments (2005–2011; MLA data).

TP (ug/L)

ROS ART BRA MGN MIN MLG POR RMI RSH SKB TOB WIN

Figure 20. TP concentrations in Lake of Bays and its embayments (2002–2011; LOBA data).

TP(ug/L)

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.

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5.1.5 Recommendations for Modeling

5.1.5.1 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.

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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.

5.1.5.2 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

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Figure 21. Changes in Total Phosphorus from Spring to Summer at MLA Sampling Stations.

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5.1.6 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.

5.2.1 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.

5.2.2 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).

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Table 15. District of Muskoka TP Monitoring Data for Georgian Bay Embayments (2000-2011)

Total Phosphorus (g/L)

Location

Type

2000 2002 2004 2006 2008 2009 2010 2011

Embayment

Latitude (°N)

Longitude (°W)

Modeled Embayments 1 Go Home River to Georgian Bay 1 North Bay 44.89 -79.79 13.8 10.9 12.1 10.8 10.6 1 South Bay 44.88 -79.79 11.3 9.3 11.2 17.1 13.3 1 Tadenac Bay 45.06 -79.98 7.1 6.2 6.8 2 Twelve Mile Bay - East 45.08 -79.95 9.3 13.0 11.1 2 Twelve Mile Bay - West 45.09 -80.06 5.4 9.2 5.3 3.8 Not Modeled Embayments 3 Wah Wah Taysee 45.06 -80.02 4.8 2.8 2 Go Home Bay 44.99 -79.94 6.9 1 Cognashene Bay 44.94 -79.92 4.9 8.0

5.2.3 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.

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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).

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Table 16. Recommendations for Monitoring Inner and Outer Bay Sampling Locations

Recommendations for Modeling and Current Monitoring Monitoring Location Outer Bay Inner Bay Outer Bay Inner Bay

Go Home Bay No Measured, not Monitor Possibly model modeled. Cognashene Bay Yes Not measured or Keep monitoring Move sampling site modeled. further in-bay and possibly model Go Home River Modeled, not Not required No change to Georgian Bay measured North Bay No Yes Not Required No change Tadenac Bay No Yes Not Required No change Twelve Mile Bay – No Yes Not Required No change East Twelve Mile Bay – No Yes Monitor Possibly move West 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

5.2.4 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

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Table 17. Georgian Bay Embayment Sites and Inland Lakes monitored by Georgian Bay Forever.

Latitude Longitude Program Station Name Modeled (°N) (°W) GBF 12 mile #3 45 05.297 80 01.485 Yes GBF 12 mile #4 45 05.073 79 59.896 Yes GBF Go Home #4 45 00.015 79 55.594 Yes GBF Cog Lake #3 44 57.027 79 55.110 Yes GBF North Bay #3 44 53.088 79 48.519 Yes GBF North Bay #5 44 53.481 79 47.575 Yes GBF HH #2 44 52.407 79 49.165 No GBF HH #4 44 52.473 79 48.403 No GBF South Bay #7 44 52.591 79 47.163 Yes GBF South Bay #6 44 52.035 79 47.164 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

5.2.5 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.

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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 = 0.992 * TPso - 0.563

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.

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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 (%) 52.7 -25.0 Median Error (%) 38.4 -22.9 n = 134 72 n >20% Error 100 42 n >40% Error 64 17

Notes: + Error is overestimation of phosphorus concentration by the model and -ve error is underestimation

6.1.1 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.

6.1.1.1 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.

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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.

Accuracy of Revised Muskoka Water Quality Model All Data 100

-25

-50

-75

1.0 1.5 2.0 2.5 3.0 3.5 4.0 % Error Modelled)(Measured vs Error % 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.

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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

100

-20

-40

-60 % Error (Measured vs Modelled) vs (MeasuredError % -80 1.0 1.1 1.1

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 52.7 -24.4 27.6 -36.5 28.6 -34.8 34.2 -34.0 Median 38.4 -22.6 22.3 -38.5 22.7 -38.5 26.7 -35.2 n = 134 72 14 15 5 9 3 6 n >20% 100 42 9 14 3 8 1 3

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

6.1.1.2 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.

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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 km2) or lower by an average of 91% (12.9 km2) 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

120 110 100 90 Ave. + Difference = 23.6 km2, 54.4 % 80 2 70 Ave. - Difference = 12.9 km , 90.6% 60 50 40

Model Old km2 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 110 120

New Model km2

Wetland Area-Dwight 40 36 32 28 24 20 16 12 Old Model Model Old km2 8 4 0 0 4 8 12 16 20 24 28 32 36 40

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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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.

400 350 300 250 200 150 100 y = -0.0061x + 25.575 R² = 5E-07

Error in in Percent Error 50 0 -50 0 10 20 30 40 50 -100

Wetland in Percent

Figure 27. Relationship of model error to DOC in Muskoka lakes.

250

200

150

100

ErrorModel % in 0 0 2 4 6 8 10 12 14 -50

-100 DOC in mg/L

6.1.1.3 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 m3/yr) applied to the surface area of a lake (in m2). 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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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.

400 350 300 250 y = -0.0006x + 25.571 200 R² = 2E-05 150 100

Error in Percentin Error 50 0 -50 0 50 100 150 200 -100 Areal Water Load in m

400 350 300 250 y = -0.0006x + 25.571 200 R² = 2E-05 150 100

Error in Percentin Error 50 0 -50 0 10 20 30 40 50 -100 Areal Water Load in m

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6.1.1.4 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.

250

200

150

100

50 Percent Error 0 0 100 200 300 400 500 600 -50

-100 Watershed Area/Lake Area

0 0 20 40 60 80 100

Percent Percent Error -20

-40

-60 Watershed Area/Lake Area

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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.

Headwater Lakes Non-Headwater Lakes 0 or 1 upstream lake > 1 upstream lake

+error (%) -error (%) +error (%) -error (%)

N= 79 43 58 29 Average 63.2 -25.0 38.4 -26.5 Median 44.7 -20.3 33.2 -27.1

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.

6.1.1.5 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.

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Figure 30. Relationship of model error to lake maximum (top) and mean (bottom) depth.

400 350 300 250

200 y = -0.1255x + 31.337 150 R² = 0.0011 100

Error in in PercentError 50 0 -50 0 10 20 30 40 50 60 -100 Maximum Depth (m)

250

200

150 y = -0.2493x + 25.251 R² = 0.0005 100

50 Percent Error 0 0 5 10 15 20 25 30 -50

-100 Lake Depth (m)

6.1.1.6 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).

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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 = 50 21 83 51 Mean 66.1 -30.2 44.8 -24.6 Median 49.0 -30.9 35.0 -20.1

6.2 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.

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7. Development of a Planning Approach

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.

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Table 22. Model Components and Evaluation of Confidence

Component Confidence Lake, watershed High Confidence areas - based on recent data and GIS mapping Natural High Confidence Atmospheric - long-term (17 years) measured data from MOE specific to Muskoka-Haliburton area Load Moderate Confidence - Measured and published relationship from MOE - Moderate - Wetland areas from GIS – Moderate Natural Load - Relationship of DMM to MOE wetland definition/classification – Moderate from Wetland - 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 Depth of Runoff - 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 Settling Velocity - 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 Predicted BG Low Confidence and BG+50% - Model error >20% and not systematically related to model inputs. Concentrations Anthropogenic Moderate Confidence Load to septic - Based on measured water usage and effluent phosphorus concentrations, but data are old and system usage figures are not lake-specific Anthropogenic Low Confidence Load to lake - Published studies show that phosphorus is not always mobile, particularly in Shield soils from septics - Increasing acknowledgment of attenuation by soils from the MOE and OMB Moderate Confidence Anthropogenic - A known component, export coefficients are estimates only and not verified for the Precambrian Load to lake Shield subwatersheds from runoff - 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 Usage Factor - Three values, 354 lakes - Not updated in 20 years, based on Central Ontario surveys - Known site-specific errors Predicted Low confidence Present Day - Poor model performance for 60% of the validation lakes (n=65); 20% are underestimated by an Concentration average of 42% and 40% are overestimated by an average of 130%. Figure 22, Table 18 Measured High Confidence Present Day - Documented improvements in accuracy of sampling and analytical techniques Concentration - 10+ year record of high resolution TP measurements for DMM lakes.

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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.

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Figure 31. Human phosphorus loading and relationship between TP and DOC in Muskoka Lakes.

TP-DOC Relationship in 193 Muskoka Lakes 30 28 Barrons D.I. = 1.01 26 24 3 Mile Main D.I. = 2.82 Brandy D.I. = 1.27 22 Ryde D.I. = 1.05 20 3 Mile GR DI = 1.14 Black DI = 1.12 18 Fawn D.I. = 1.31 16 Bearpaw DI = 1.06 14 12 Webster DI=1.0 Siding DI = 1.13 10 Halfway DI=1.17 y = 1.3715x + 2.3406 Total Phosphorus (ug/L) Phosphorus Total 8 Fox DI = 1.16 Perch DI=1.1 6 R² = 0.4552 Clark DI - 1.29 Buck HT DI = 1.19 4 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Dissolved Organic Carbon (mg/L)

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.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

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 2010. HESL revised the 2005 version of the MWQM to incorporate the most recent MOECC guidance (MOECC 2010, Paterson et al. 2006). These revisions included:

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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.

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:

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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

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:

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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 2001. 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, blue- green 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

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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

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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 http://www.muskokawaterweb.ca/lake-data/muskoka-data/shoreline-land-use-maps) 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 2000-2014 (App. E). Five lakes had 10 year (2005-2014) 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 2000-2014 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.

8.2.1 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

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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 2002-2011. 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.

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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.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program 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.

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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.

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8.4 Recommendations

In conclusion, we recommend that:

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.

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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// X Full Servicing Slope Dependent Setback X Enhanced Building Setback X Limit Lot Creation X Remedial Action Plan X

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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 No Increasing TP Trend Documented Blue-Green Algal Bloom

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

No Development Related TP as Cause?

Yes

Limit Lot Creation Remedial Action Plan

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9. References

AECOM 2009. Development Capacity of Recreational Lakes in Seguin Township. Prepared for Seguin Township.

Brett, M.T. and Benjamin, M.M., 2008. A review and reassessment of lake phosphorus retention and the nutrient loading concept. Freshwater Biology, 53: 194-211.

Budd, L.F. and D.W. Meals. 1994. 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., 2000. P-Flux Determination: Juanita Creek analysis. http://courses.washington.edu/cewa/599c/paper2.html.

Canada Department of Fisheries and Environment, 1978. 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., 2002. 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. 2010. 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. 1994. Lakeshore Capacity Study: Part I – Test of effects of shoreline development on the trophic status of lakes. Lake and Reservoir Management. 8:121-129.

District Municipality of Muskoka 2010. Official Plan of the Muskoka Planning Area, Consolidated November 19, 2010. Pg. D7.

District Municipality of Muskoka 2011. Public Works Certificate of Approval files.

Fisheries and Oceans Canada 1995. Lake of Bays Nautical Chart. Scale 1: 25,000. 6023. Published by the Canadian Hydrographic Service.

Fisheries and Oceans Canada 2005. Lake Rosseau and Lake Joseph Nautical Chart. Scale 1: 25,000. 6022-1 and 6022-2. Published by the Canadian Hydrographic Service.

Fisheries and Oceans Canada 2004. Lake Muskoka Nautical Chart. Scale 1: 25,000. 6021-1 and 6021-2. 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, 1998.

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Gartner Lee Limited 2005. Recreational water quality management in Muskoka. Prepared for the Department of Planning and Economic Development, District Municipality of Muskoka. June 2005.

Gartner Lee Limited 2008. Review of long-term water quality data for the Lake System Health Program. September 2008.

Glenside Ecological Services Limited 2009. Species at Risk: Potentially Significant Habitat Mapping. Prepared for the District Municipality of Muskoka.

Hutchinson, N.J. 2002. 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.

Hutchinson Environmental Sciences Ltd. (HESL) 2011a. Georgian Bay Forever Causation Study Synthesis. Prepared for Georgian Bay Forever. October 2011.

Hutchinson Environmental Sciences Ltd. (HESL) 2011b. Georgian Bay Forever Coastal Monitoring Program Review. Prepared for Georgian Bay Forever. October 2011.

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). 2000. Madawaska Lake Total Maximum Daily (Annual) Load : Total Phosphorus: Final Lakes TMDL Report. DEPLW 2000-112

Nϋrnberg, G.K. 2009 Assessing internal phosphorus load – Problems to be solved. Lake and Reservoir Management, 25: 419-432.

Oberts, G.L., Wotzka, P.J. & Hartsoe, J.A. 1989. The water quality performance of select urban runoff treatment systems. Rept. to Legis. Comm. Minnesota Resources Metropolitan Council Pub. No. 590-89-062a. St. Paul MN.

Ontario Ministry of the Environment. 1994. Water Management Policies Guidelines and Water Quality Objectives of the Ministry of Environment and Energy, July 1994. ISBN 0-7778-8473-9 rev.

Ontario (Province of). 2010. 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 2010. PIBS 7642e © 2010, Queen’s Printer for Ontario.

Palmer, M.E., N.D. Yan, A.M. Paterson and R.E. Girard. 2011. 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: 1038-1049.

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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. 2006. 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 3-900051-07-0, URL http://www.R- project.org.

Reckhow, K.H., M.N. Beaulac & J.T. Simpson 1990. Modeling Phosphorus Loading and Lake Response under Uncertainty: a Manual and Compilation of Export Coefficients. 440/5-80-011, Environmental Protection Agency, Washington, DC.

Robertson, W.D., S.L. Schiff and C.J. Ptacek. 1998. Review of phosphate mobility and persistence in 10 septic system plumes. Ground Water 36 : 1000-1010.

Robertson, W.D., 2003. Enhanced attenuation of septic system phosphate in noncalcareous sediments. Groundwater 41: 48 – 56.

Scott, L.D., Winter, J.G., and Girard, R.E. 2006. Annual water balances, total phosphorus budgets and total nitrogen and chloride loads for Lake Simcoe (1998-2004). LSEMS Technical Report No. Imp. A.6

The Louis Berger Group Inc. 2010. Estimation of the phosphorus loadings to Lake Simcoe. Submitted to Lake Simcoe Region Conservation Authority. September 2010.

Tukey, J.W. 1977. Exploratory Data Analysis. Addison-Wesley.

Winter, J.G. and P.J. Dillon 2006. Export of nutrients from golf courses on the Precambrian Shield. Environmental Pollution 141: 550-554.

Winter, J.G., A.M. DeSellas, R. Fletcher, L. Heintsch, A. Morley, L. Nakamoto and K. Utsumi. 2011. Algal blooms in Ontario: Increases in reports since 1994. Lake and Reservoir Management 27:107- 114. Zurawsky, M.A., W.D. Robertson, C.J.. Ptacek and S.L. Schiff. 2004. Geochemical stability of phosphorus solids below septic system infiltration beds. . J. Contaminant Hydrology 33 : 129-143

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Appendix A. Methodology for GIS exercise providing new watershed, lake and wetland areas

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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 2012. 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).

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 2009. 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:

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 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”

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

 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.  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.

 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.

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)  Write a query to group and count the HESL property code by lake name. See below: o SELECT Dev_Stats_100.CONNECTION, Dev_Stats_100.WQ_CODE, Count(Dev_Stats_100.WQ_CODE) AS CountOfWQ_CODE o FROM Dev_Stats_100 o GROUP BY Dev_Stats_100.CONNECTION, Dev_Stats_100.WQ_CODE o ORDER BY Dev_Stats_100.CONNECTION, Dev_Stats_100.WQ_CODE;  Do this for the three tables  Export the results to xlsx files  These are the final development count tables  These can be added to the Watershed Data spreadsheet in different worksheets.

J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

Appendix B. List of Updated Lake, Watershed, and Wetland Areas

Hutchinson Environmental Sciences Ltd.

R05042016_150074_MWQMLSH_final.docx B1

2012 Watersheds for Water Quality Model Geomatics Section - District of Muskoka

Field descriptions available at bottom of watershed list.

M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL Black Big Orillia Lk Upper Black River -78.99624 45.09647 0.45 2.30 1.86 3.04 1.32 Black Black Lk Upper Black River -78.90765 45.16793 0.61 13.51 12.90 84.23 6.23 Black Blue Chalk Lk Upper Black River -78.93885 45.19868 0.51 1.65 1.14 0.00 0.00 Black Campstool Lk Upper Black River -78.9927 45.14328 0.13 1.59 1.46 3.53 2.23 Black Carcass Lk Upper Black River -78.95626 45.17869 0.19 2.09 1.90 13.48 6.46 Black Circular Lk Upper Black River -78.97959 45.11887 0.21 4.26 4.05 24.80 5.83 Black Clear Lk Upper Black River -79.01496 45.04153 1.00 2.81 1.81 2.79 0.99 Black Cream Lk Upper Black River -78.97877 45.16276 0.11 0.86 0.76 1.26 1.47 Black Crosson Lk Upper Black River -79.03557 45.08384 0.56 1.33 0.77 0.00 0.00 Black Dan Lk Upper Black River -78.86863 45.15271 0.17 0.76 0.58 7.20 9.52 Black Grindstone Lk Upper Black River -78.87539 45.19713 0.32 2.65 2.33 19.26 7.27 Black Grouse Lk Upper Black River -78.98276 45.12897 0.10 1.40 1.30 5.94 4.25 Black Gullfeather Lk Upper Black River -79.01938 45.09465 0.67 10.50 9.83 114.02 10.86 Black Horse Lk Upper Black River -78.88422 45.17164 0.16 0.63 0.47 1.94 3.07 Black Insula Lk Upper Black River -78.97286 45.18879 0.17 1.33 1.15 2.80 2.11 Black Jill Lk Upper Black River -78.95769 45.15163 0.10 2.04 1.93 18.17 8.93 Black Keyhole Lk Upper Black River -79.01095 45.06105 0.15 2.25 2.10 18.47 8.21 Black Little Orillia Lk Upper Black River -79.00066 45.08633 0.25 1.09 0.84 0.22 0.20 Black Lower Pairo Lk Upper Black River -78.94389 45.16696 0.15 1.60 1.45 0.69 0.43 Black Margaret Lk Upper Black River -78.88157 45.14392 0.58 2.07 1.49 2.21 1.07 Black McEwen Lk Upper Black River -78.87525 45.16014 0.24 1.53 1.29 11.39 7.45 Black Mug Lk Upper Black River -78.95128 45.15929 0.10 0.85 0.75 0.56 0.65 Black North Longford Lk Lower Black River -79.08186 44.89472 1.14 9.14 8.00 104.44 11.43 Black Porcupine Lk Upper Black River -79.00186 45.15432 0.54 3.72 3.18 40.96 11.02 Black Red Chalk Lk Upper Black River -78.94626 45.18979 0.56 4.52 3.96 18.37 4.07 Black Ridout Lk Upper Black River -78.97883 45.17518 0.49 2.63 2.14 7.20 2.74 Black Riley Lk Lower Black River -79.20755 44.83744 1.45 45.94 44.49 878.45 19.12 Black Saucer Lk Upper Black River -78.96881 45.14757 0.06 0.44 0.38 1.74 3.97 Black Saw Lk Upper Black River -79.02561 45.04593 0.29 3.27 2.98 59.24 18.11 Black Shoe Lk Lake of Bays -78.90984 45.208 0.39 1.36 0.96 0.00 0.00 Black South Longford Lk Lower Black River -79.07729 44.88189 0.92 8.51 7.60 116.20 13.65 Black Sugarbowl Lk Upper Black River -78.97603 45.15153 0.07 1.74 1.67 17.98 10.36 Black Sunken Lk Upper Black River -78.84571 45.25033 0.15 0.66 0.51 0.43 0.65 Black Teapot Lk Upper Black River -78.97962 45.13612 0.34 5.09 4.75 39.92 7.84 Black Three Island Lk Upper Black River -78.88624 45.1573 0.23 0.98 0.75 7.53 7.67 Black Upper Pairo Lk Upper Black River -78.95049 45.17228 0.12 4.57 4.45 53.83 11.77 Black Wren Lk Upper Black River -78.86301 45.18449 0.49 4.18 3.68 15.33 3.67 Black Wrist Lk Upper Black River -79.05201 45.08756 0.25 4.46 4.21 38.65 8.67 Dwight Big Hoover Lk Oxtongue / Dwight -78.95358 45.44778 0.09 1.72 1.63 4.54 2.64 Dwight Brooks Lk Oxtongue / Dwight -79.00099 45.45628 0.17 0.64 0.47 0.00 0.00 Dwight Buck Lk Lake of Bays -78.98978 45.39256 0.41 1.93 1.52 9.52 4.93

App.B WQ_Model_Watershed_Stats_2012.xlsx 1 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL Dwight Burns Lk Lake of Bays -78.98283 45.36404 0.14 1.91 1.77 20.48 10.74 Dwight Burnt Island Lk Oxtongue / Dwight -78.64742 45.65243 9.66 56.86 47.21 244.46 4.30 Dwight Canoe Lk Oxtongue / Dwight -78.71864 45.55401 3.58 154.57 150.99 758.61 4.91 Dwight Cooper Lk Lake of Bays -79.00728 45.34277 0.27 3.26 3.00 8.51 2.61 Dwight Cotter Lk Lake of Bays -78.99762 45.35484 0.07 1.42 1.34 9.12 6.43 Dwight Crotch Lk Lake of Bays -78.97262 45.38857 0.11 0.50 0.40 2.32 4.61 Dwight Docker Lk Lake of Bays -78.99051 45.34191 0.06 0.72 0.65 1.56 2.18 Dwight Dotty Lk Oxtongue / Dwight -78.99158 45.46193 1.59 18.24 16.66 94.69 5.19 Dwight Eastell Lk Lake of Bays -79.02357 45.3979 0.12 1.29 1.17 5.65 4.39 Dwight Eighteen Mile Lk Lake of Bays -78.9631 45.38229 0.35 6.28 5.93 34.77 5.53 Dwight Eiler Lk Lake of Bays -78.94314 45.37705 0.09 1.16 1.07 0.00 0.00 Dwight Fifteen Mile Lk Lake of Bays -78.96266 45.34987 0.81 4.11 3.31 19.67 4.78 Dwight Fowler Lk Bella / Rebecca Lakes -79.03326 45.40691 0.08 0.32 0.24 0.00 0.00 Dwight Hardup Lk Lake of Bays -78.97803 45.39247 0.21 0.92 0.72 3.93 4.26 Dwight Helve Lk Lake of Bays -78.9824 45.38434 0.19 0.62 0.43 0.00 0.00 Dwight Lake of Bays - Dwight Lake of Bays -79.01926 45.32275 2.28 43.06 40.79 478.21 11.10 Dwight Lee Lk Lake of Bays -79.0428 45.37894 0.22 6.07 5.85 102.90 16.96 Dwight Little Hoover Lk Bella / Rebecca Lakes -78.94395 45.43995 0.09 0.58 0.49 8.40 14.43 Dwight Little Nelson Lk Oxtongue / Dwight -78.95091 45.48005 0.07 0.90 0.83 4.14 4.61 Dwight Little Oxbow Lk Oxtongue / Dwight -78.96604 45.42485 0.07 2.80 2.73 22.43 8.01 Dwight Little Pell Lk Lake of Bays -79.05473 45.39883 0.09 0.66 0.57 2.16 3.29 Dwight Little Spaniel Lk Lake of Bays -78.9837 45.40995 0.06 0.46 0.41 2.14 4.60 Dwight McReynold Lk Lake of Bays -79.02793 45.38781 0.06 2.51 2.46 31.56 12.55 Dwight North Dotty Lk Oxtongue / Dwight -79.00536 45.48118 0.29 1.42 1.13 0.00 0.00 Dwight Oxbow Lk Oxtongue / Dwight -78.96631 45.43684 1.69 5.35 3.65 0.00 0.00 Dwight Oxtongue Lk Oxtongue / Dwight -78.92226 45.36545 2.41 231.83 229.42 1275.77 5.50 Dwight Peeler Lk Lake of Bays -79.0431 45.39884 0.09 0.86 0.77 7.84 9.08 Dwight Pell Lk Lake of Bays -79.06226 45.39418 0.37 1.79 1.42 20.83 11.65 Dwight Pup Lk Lake of Bays -78.97214 45.40446 0.03 1.13 1.09 4.71 4.19 Dwight Samlet Lk Oxtongue / Dwight -78.89885 45.42113 0.22 2.59 2.37 10.42 4.02 Dwight Seventeen Mile Lk Lake of Bays -78.97186 45.37462 0.18 0.90 0.73 0.00 0.00 Dwight Seventeen Mile Lk Bella / Rebecca Lakes -79.01231 45.40611 0.10 0.68 0.58 0.54 0.79 Dwight Sixteen Mile Lk Lake of Bays -78.96484 45.36264 0.81 5.31 4.49 18.87 3.56 Dwight Smoke Lk Oxtongue / Dwight -78.68044 45.51744 6.62 108.37 101.75 377.73 3.49 Dwight South Nelson Lk Oxtongue / Dwight -78.9556 45.47011 0.20 2.95 2.75 7.05 2.39 Dwight Spaniel Lk Lake of Bays -78.97081 45.39613 0.11 0.97 0.86 4.61 4.77 Dwight Steeple Lk Lake of Bays -78.98637 45.39963 0.08 0.66 0.59 1.77 2.67 Dwight Upper Oxbow Lk Oxtongue / Dwight -78.96606 45.40949 0.18 0.68 0.50 4.30 6.32 Dwight Wells Lk Lake of Bays -78.97993 45.34153 0.06 0.44 0.38 0.95 2.14 Dwight Wilson Lk Lake of Bays -78.97327 45.33106 0.08 0.75 0.67 1.93 2.59 Joseph Ada Lk Lakes Rosseau and Joseph -79.64017 45.09057 0.23 1.09 0.86 14.79 13.61 Joseph Anselmi Lk Lakes Rosseau and Joseph -79.71049 45.20961 0.10 1.30 1.20 9.13 7.03 Joseph Armishaw Lk Lakes Rosseau and Joseph -79.77446 45.23439 0.13 0.64 0.51 1.39 2.19 Joseph Bass Lk Lakes Rosseau and Joseph -79.69825 45.11018 0.94 4.86 3.92 16.92 3.48 Joseph Bruce Lk Lakes Rosseau and Joseph -79.64598 45.18623 0.98 4.30 3.32 23.97 5.57 Joseph Brush Lk Lakes Rosseau and Joseph -79.75774 45.27118 0.27 2.35 2.08 2.22 0.94

App.B WQ_Model_Watershed_Stats_2012.xlsx 2 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL Joseph Butterfly Lk Lakes Rosseau and Joseph -79.62859 45.09046 0.65 3.17 2.52 29.45 9.28 Joseph Clubbe Lk Lakes Rosseau and Joseph -79.73308 45.24359 0.13 2.03 1.90 16.14 7.96 Joseph Dick Lk Lakes Rosseau and Joseph -79.70726 45.20078 0.16 1.76 1.60 15.34 8.71 Joseph Draper Lk Lakes Rosseau and Joseph -79.70327 45.21115 0.15 0.69 0.54 0.00 0.00 Joseph Dyson Lk Lakes Rosseau and Joseph -79.65053 45.21616 0.95 2.73 1.78 3.66 1.34 Joseph Fair Lk Lakes Rosseau and Joseph -79.70399 45.22366 0.22 0.97 0.75 3.30 3.42 Joseph Fraser Lk Lakes Rosseau and Joseph -79.74907 45.25224 0.08 0.96 0.88 0.00 0.00 Joseph Gerow Lk Lakes Rosseau and Joseph -79.66652 45.21244 0.13 0.82 0.69 3.25 3.95 Joseph Hamer Lk Lakes Rosseau and Joseph -79.79179 45.23682 0.37 3.11 2.74 28.82 9.26 Joseph Henshaw Lk Lakes Rosseau and Joseph -79.59097 45.09792 0.28 1.21 0.93 3.59 2.96 Joseph Lake Joseph - Gordon Bay Lakes Rosseau and Joseph -79.78529 45.20637 1.00 2.73 1.73 0.63 0.23 Joseph Lake Joseph - Hamer Bay Lakes Rosseau and Joseph -79.77261 45.22213 1.16 4.41 3.25 19.74 4.48 Joseph Lake Joseph - Main Lakes Rosseau and Joseph -79.76656 45.18144 46.13 93.39 47.26 288.66 3.09 Joseph Lake Joseph - Stanley Bay Lakes Rosseau and Joseph -79.72957 45.21744 1.01 4.49 3.49 3.35 0.75 Joseph Lake Joseph - Still's Bay Lakes Rosseau and Joseph -79.71515 45.1266 0.34 1.99 1.65 7.93 3.98 Joseph Little Lake Joseph Lakes Rosseau and Joseph -79.68698 45.2015 2.98 9.45 6.47 34.71 3.67 Joseph Loucks Lk Lakes Rosseau and Joseph -79.8051 45.19371 0.26 1.49 1.23 9.49 6.37 Joseph Mary Jane Lk Lakes Rosseau and Joseph -79.66783 45.18546 0.10 0.42 0.32 0.34 0.82 Joseph McTaggart Lk Lakes Rosseau and Joseph -79.81123 45.23824 0.12 6.57 6.45 46.03 7.01 Joseph Mirror Lk Lakes Rosseau and Joseph -79.6936 45.21757 0.16 0.72 0.56 0.00 0.00 Joseph Pickering Lk Lakes Rosseau and Joseph -79.65288 45.20455 0.18 3.29 3.11 65.93 20.07 Joseph Portage Lk Lakes Rosseau and Joseph -79.79713 45.21231 0.98 4.62 3.64 34.77 7.53 Joseph Ricketts Lk Lakes Rosseau and Joseph -79.75174 45.15023 0.27 5.70 5.43 69.68 12.22 Joseph Roberts Lk Lakes Rosseau and Joseph -79.82317 45.24491 0.15 0.66 0.50 0.00 0.00 Joseph St Germaine Lk Lakes Rosseau and Joseph -79.67741 45.08808 0.23 1.25 1.02 5.13 4.10 Joseph Stewart Lk Lakes Rosseau and Joseph -79.76757 45.14217 1.52 10.75 9.23 130.30 12.12 Joseph Tiffin Lk Lakes Rosseau and Joseph -79.80313 45.22757 0.75 2.04 1.30 1.36 0.67 Joseph Tucker Lk Lakes Rosseau and Joseph -79.8086 45.25135 0.22 1.37 1.14 0.00 0.00 LOB Art Lk Lake of Bays -78.90214 45.30908 0.15 6.84 6.69 26.60 3.89 LOB Ashball Lk Lake of Bays -78.86519 45.2866 0.22 2.22 2.00 20.76 9.34 LOB Axle Lk Lake of Bays -79.12386 45.17577 0.20 0.56 0.36 0.80 1.41 LOB Campcot Lk Lake of Bays -78.89994 45.294 0.06 1.90 1.84 8.93 4.70 LOB Clinto Lk Lake of Bays -78.86809 45.31488 1.41 2.56 1.14 0.00 0.00 LOB Cod Lk Lake of Bays -78.90731 45.32153 0.18 1.16 0.98 0.32 0.28 LOB Dunn Lk Lake of Bays -79.15621 45.19781 0.25 3.42 3.17 68.73 20.11 LOB Fitzell Lk Lake of Bays -78.94976 45.29433 0.09 0.82 0.74 1.04 1.27 LOB Gosling Lk Lake of Bays -78.93539 45.28977 0.20 21.81 21.62 95.41 4.37 LOB Lake of Bays Lake of Bays -79.05161 45.24378 49.20 144.49 95.28 681.94 4.72 LOB Lake of Bays - Haystack Lake of Bays -79.02936 45.27973 4.10 12.23 8.13 20.41 1.67 LOB Lake of Bays - Rat Lake of Bays -79.05528 45.31668 0.43 5.32 4.88 79.76 15.00 LOB Lake of Bays - SMRB Lake of Bays -79.10977 45.16019 1.03 20.91 19.88 334.79 16.01 LOB Lake of Bays - South Portage Lake of Bays -79.07112 45.30937 1.75 10.04 8.29 55.95 5.57 LOB Lake of Bays - Ten Mile Lake of Bays -78.97858 45.27955 4.18 18.01 13.82 92.18 5.12 LOB Lake of Bays - Trading Lake of Bays -78.91846 45.24655 4.94 23.43 18.49 82.54 3.52 LOB Longline Lk Lake of Bays -78.97522 45.24915 0.27 1.17 0.90 0.00 0.00 LOB Lower Schufelt Lk Lake of Bays -79.12818 45.17766 0.12 0.48 0.36 2.60 5.40

App.B WQ_Model_Watershed_Stats_2012.xlsx 3 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL LOB Menominee Lk Lake of Bays -79.13532 45.19828 1.00 7.49 6.49 67.38 9.00 LOB Pretzel Lk North Muskoka River -79.16816 45.1736 0.09 1.50 1.42 52.40 34.82 LOB Roundabout Lk Lake of Bays -78.97329 45.24388 0.10 0.89 0.80 5.60 6.28 LOB Schufelt Lk Lake of Bays -79.12822 45.18222 0.10 0.46 0.35 0.00 0.00 LOB Shapter Lk North Muskoka River -79.17836 45.18418 0.18 1.23 1.05 31.66 25.75 LOB Tock Lk Lake of Bays -78.87828 45.27701 1.29 4.84 3.55 10.25 2.12 LOB Tooke Lk Lake of Bays -79.13814 45.17777 0.32 0.85 0.54 0.00 0.00 Mary Buchanan Lk Huntsville Lakes -79.12933 45.31625 0.12 0.40 0.28 0.00 0.00 Mary Chain Lk Huntsville Lakes -79.37886 45.22863 0.36 3.59 3.23 50.68 14.12 Mary Chub Lk Huntsville Lakes -79.23408 45.29525 0.27 1.12 0.85 7.76 6.94 Mary Fairy Lk Huntsville Lakes -79.17016 45.32884 6.63 46.96 40.34 216.12 4.60 Mary Fairy Lk - NMRB Huntsville Lakes -79.20592 45.30888 0.49 3.99 3.50 3.23 0.81 Mary Fleming Lk Huntsville Lakes -79.19749 45.20508 0.14 1.29 1.15 7.29 5.67 Mary Fleming Lk Huntsville Lakes -79.10145 45.37342 0.06 0.37 0.31 2.13 5.80 Mary Groves Lk Huntsville Lakes -79.38962 45.25684 0.23 1.88 1.65 7.91 4.21 Mary Harp Lk Huntsville Lakes -79.13459 45.37866 0.70 6.20 5.51 53.41 8.61 Mary Jerry Lk Huntsville Lakes -79.11074 45.38336 0.51 8.64 8.12 56.50 6.54 Mary Lancelot Lk Huntsville Lakes -79.37199 45.26205 0.11 0.76 0.65 9.31 12.26 Mary Lassetter Lk Huntsville Lakes -79.0459 45.36584 0.09 0.60 0.51 14.02 23.41 Mary Lena Lk Huntsville Lakes -79.19373 45.22504 0.09 2.17 2.08 16.97 7.82 Mary Lynch Lk Huntsville Lakes -79.19432 45.23873 0.10 0.38 0.29 4.03 10.48 Mary Lynx Lk Huntsville Lakes -79.1818 45.23746 0.22 2.70 2.48 37.21 13.78 Mary Mary Lk Huntsville Lakes -79.25646 45.24549 10.60 89.10 78.51 754.97 8.47 Mary Montgomery Lk Huntsville Lakes -79.18741 45.19541 0.21 1.20 0.99 5.84 4.87 Mary Otter Lk Huntsville Lakes -79.16978 45.29589 0.17 0.87 0.69 3.40 3.91 Mary Palmer Lk Huntsville Lakes -79.36059 45.22299 0.08 0.70 0.61 5.29 7.57 Mary Penfold Lk Huntsville Lakes -79.27971 45.26568 0.35 7.56 7.22 67.95 8.98 Mary Peninsula Lk Huntsville Lakes -79.09808 45.34462 8.42 43.57 35.15 197.03 4.52 Mary Rose Lk Huntsville Lakes -79.35659 45.24047 0.30 6.97 6.66 141.64 20.33 Mary Siding Lk Huntsville Lakes -79.31732 45.2834 0.59 3.84 3.25 70.14 18.28 Mary Slocombe Lk Huntsville Lakes -79.33384 45.29614 0.18 1.75 1.56 23.95 13.71 Mary Spider Lk Huntsville Lakes -79.28742 45.26086 0.07 44.42 44.35 786.68 17.71 Mary Stinking Lk Huntsville Lakes -79.37648 45.25328 0.08 0.61 0.53 19.76 32.48 Mary Toms Lk Huntsville Lakes -79.16841 45.20826 0.07 2.55 2.48 33.64 13.20 Mary Tongua Lk Huntsville Lakes -79.36742 45.22971 0.15 3.04 2.88 45.54 15.00 Mary Tucker Lk Huntsville Lakes -79.18053 45.20319 0.20 0.79 0.59 0.05 0.06 Mary Walker Lk Huntsville Lakes -79.08987 45.38007 0.69 3.39 2.69 7.56 2.23 Mary Weeduck Lk Huntsville Lakes -79.18776 45.30431 0.13 3.78 3.65 19.48 5.15 Moon Adams Lk Moon River -79.75963 45.05946 0.29 5.37 5.09 59.81 11.14 Moon Buckhorn Lk Moon River -79.85087 45.09783 0.39 15.33 14.94 335.49 21.89 Moon Cassidy Lk Moon River -79.71975 45.09919 0.49 8.74 8.25 147.30 16.85 Moon Duffy Lk Moon River -79.70358 45.06504 0.76 4.74 3.98 55.90 11.78 Moon Eagle Lk Moon River -79.88763 45.12452 0.27 1.42 1.15 35.08 24.75 Moon Fischer Lk Moon River -79.77177 45.08775 0.10 0.93 0.84 26.47 28.39 Moon Goldstein Lk Moon River -79.71447 45.04154 0.21 0.66 0.45 0.00 0.00 Moon Gooley Lk Moon River -79.82461 45.10251 0.22 3.74 3.51 69.90 18.71

App.B WQ_Model_Watershed_Stats_2012.xlsx 4 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL Moon Haggart Lk Moon River -79.74703 45.10838 0.35 6.69 6.34 153.65 22.96 Moon Hellangone Lk Moon River -79.80471 45.16634 0.21 1.25 1.05 2.42 1.93 Moon Hesners Lk Moon River -79.64344 45.01462 0.24 0.97 0.72 7.46 7.72 Moon Kenney Lk Moon River -79.89209 45.11893 0.14 1.98 1.84 32.00 16.17 Moon Little Hellangone Lk Moon River -79.80678 45.16395 0.10 3.95 3.85 97.09 24.55 Moon McMaster Lk Moon River -79.74886 45.07142 0.22 1.28 1.06 0.00 0.00 Moon McRae Lk Moon River -79.88033 45.13559 0.15 1.20 1.06 27.53 22.89 Moon Moon River Moon River -79.64749 45.02322 3.05 16.83 13.78 136.86 8.13 Moon Moon River Moon River -79.79837 45.06756 3.03 71.97 68.94 976.24 13.56 Moon Myers Lk Moon River -79.7494 45.09844 0.34 1.01 0.67 0.00 0.00 Moon Pennsylvania Lk Moon River -79.65297 45.00715 0.27 1.30 1.03 20.43 15.77 Moon Roderick Lk Moon River -79.71737 45.07775 1.15 13.96 12.81 288.99 20.70 Moon Sawyer Lk Moon River -79.69981 45.03537 0.26 3.86 3.60 37.12 9.61 Moon Silver Sand Lk Moon River -79.7491 45.08217 0.16 1.04 0.88 0.84 0.81 Moon Tar Lk Moon River -79.65099 44.99395 0.30 1.99 1.69 24.32 12.25 Moon Toronto Lk Moon River -79.75752 45.10639 0.17 0.84 0.67 1.66 1.98 Moon Vaughan Lk Moon River -79.87547 45.11435 0.23 9.87 9.64 261.61 26.51 Moon White Lk Moon River -79.73336 45.0672 0.38 1.56 1.19 5.60 3.58 Morrison Bearpaw Lk Severn River -79.48934 44.92769 0.23 6.08 5.85 152.85 25.13 Morrison Lamorie Lk Severn River -79.45196 44.89108 0.11 2.93 2.82 36.45 12.42 Morrison Little Lk Severn River -79.71549 44.819 3.68 10.04 6.36 60.01 5.98 Morrison Little Lk Severn River -79.46875 44.89344 0.29 1.48 1.19 7.70 5.21 Morrison Loon Lk Severn River -79.43195 44.92019 0.82 9.22 8.40 138.29 15.00 Morrison Lower Eagle Lk Severn River -79.46231 44.90471 0.07 0.50 0.43 6.80 13.69 Morrison Morrison Lk Severn River -79.45572 44.87034 2.49 15.07 12.58 166.71 11.06 Morrison North Muldrew Lk Severn River -79.4384 44.90638 1.50 14.55 13.05 239.82 16.48 Morrison South Muldrew Lk Severn River -79.44423 44.89778 2.70 16.40 13.71 171.30 10.44 Morrison Turtle Lk Musquash River -79.57817 44.93665 0.44 3.59 3.15 55.09 15.35 Morrison Upper Eagle Lk Severn River -79.46988 44.90798 0.10 1.23 1.13 17.67 14.33 Muskoka Bear Lk Musquash River -79.58957 44.98645 0.12 1.06 0.94 10.41 9.83 Muskoka Black Lk Lake Muskoka -79.55826 45.00107 0.53 4.68 4.15 78.41 16.74 Muskoka Brandy Lk Lake Muskoka -79.52892 45.10826 1.15 39.99 38.83 765.69 19.15 Muskoka Clear Lk Lake Muskoka -79.55578 44.98275 1.49 2.33 0.84 0.00 0.00 Muskoka Concession Lk Lake Muskoka -79.67122 45.07619 0.30 3.03 2.73 48.42 15.96 Muskoka Dark Lk Lake Muskoka -79.58527 44.99819 0.17 0.38 0.21 0.00 0.00 Muskoka Deer Lk Lake Muskoka -79.46152 44.96015 1.60 4.00 2.40 20.98 5.25 Muskoka Echo Lk Lake Muskoka -79.5419 44.96965 0.93 5.75 4.83 96.72 16.82 Muskoka Gull Lk Lake Muskoka -79.34867 44.91376 1.33 4.88 3.55 18.14 3.72 Muskoka Gullwing Lk Lake Muskoka -79.53578 44.97628 0.84 6.55 5.71 97.90 14.95 Muskoka Hardy Lk Lake Muskoka -79.5348 45.00635 0.99 4.89 3.91 57.86 11.83 Muskoka Hoc Roc River Lake Muskoka -79.36042 44.95175 0.04 40.33 40.29 485.90 12.05 Muskoka Indian River (North) Lake Muskoka -79.5805 45.11588 0.26 3.14 2.88 9.15 2.91 Muskoka Indian River (South) Lake Muskoka -79.56515 45.09867 0.25 3.33 3.08 35.17 10.56 Muskoka Lake Muskoka (North) Lake Muskoka -79.53725 45.05361 44.39 103.43 59.04 364.31 3.52 Muskoka Lake Muskoka (South) Lake Muskoka -79.48514 45.00426 62.56 118.93 56.37 299.48 2.52 Muskoka Lake Muskoka - Bala Bay Lake Muskoka -79.59295 45.01033 4.25 9.06 4.81 72.46 8.00

App.B WQ_Model_Watershed_Stats_2012.xlsx 5 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL Muskoka Lake Muskoka - Boyd Bay Lake Muskoka -79.39669 45.05065 0.60 15.60 15.00 99.92 6.40 Muskoka Lake Muskoka - Dudley Lake Muskoka -79.61534 45.04373 3.55 16.11 12.57 147.47 9.15 Muskoka Lake Muskoka - Whiteside Bay Lake Muskoka -79.61339 45.06507 1.14 8.34 7.20 144.59 17.34 Muskoka Leonard Lk Lake Muskoka -79.44499 45.0744 1.95 6.14 4.19 36.70 5.98 Muskoka Long Lk Musquash River -79.60503 44.99829 1.30 4.20 2.90 46.59 11.11 Muskoka Medora Lk Lake Muskoka -79.65395 45.06482 0.40 2.61 2.20 20.77 7.96 Muskoka Mirror Lk Lake Muskoka -79.57296 45.10777 0.46 1.41 0.96 3.45 2.44 Muskoka Muskoka Bay Lake Muskoka -79.4027 44.93786 4.03 24.03 20.00 256.79 10.69 Muskoka Neilson Lk Lake Muskoka -79.52727 44.98425 0.17 6.28 6.12 137.60 21.90 Muskoka Paul's Lk Lake Muskoka -79.28456 44.97102 0.09 7.26 7.17 79.42 10.95 Muskoka Pigeon Lk Lake Muskoka -79.47514 44.97374 0.55 3.25 2.70 28.48 8.76 Muskoka Pine Lk Lake Muskoka -79.49684 44.94195 1.56 16.87 15.32 280.03 16.60 Muskoka Silver Lk Lake Muskoka -79.31771 44.90163 0.58 8.80 8.22 140.17 15.94 Muskoka Silver Lk Lake Muskoka -79.56492 45.11731 0.56 1.15 0.59 0.00 0.00 Muskoka Thinn Lk Lake Muskoka -79.28088 44.95411 0.16 2.07 1.92 14.89 7.18 Muskoka River Allen Lk South Muskoka River -79.02418 45.19162 0.07 1.93 1.87 34.70 17.94 Muskoka River Angel Lk North Muskoka River -79.19335 45.17591 0.14 2.02 1.87 52.82 26.16 Muskoka River Atkins Lk North Muskoka River -79.22881 45.11968 0.13 1.71 1.58 32.52 18.99 Muskoka River Beattie Lk North Muskoka River -79.20649 45.19946 0.05 1.39 1.33 23.38 16.87 Muskoka River Big Stephen Lk North Muskoka River -79.19398 45.16674 0.39 1.76 1.37 36.29 20.66 Muskoka River Bigwind Lk South Muskoka River -79.05496 45.05747 1.08 5.24 4.16 30.75 5.87 Muskoka River Bird Lk South Muskoka River -79.06514 45.04327 0.63 6.79 6.16 53.67 7.91 Muskoka River Black Lk South Muskoka River -79.0426 45.21197 0.14 1.13 0.99 4.88 4.33 Muskoka River Bonnie Lk North Muskoka River -79.26175 45.14004 0.42 1.45 1.03 0.00 0.00 Muskoka River Chub Lk South Muskoka River -78.98551 45.2133 0.34 2.99 2.65 23.30 7.80 Muskoka River Clearwater Lk North Muskoka River -79.23536 45.20231 0.86 3.12 2.26 2.63 0.84 Muskoka River Devine Lk North Muskoka River -79.22923 45.19298 0.38 3.87 3.50 24.00 6.19 Muskoka River Dickie Lk South Muskoka River -79.08774 45.14768 0.91 5.28 4.37 60.13 11.39 Muskoka River East Buck Lk South Muskoka River -79.08693 45.03176 0.16 2.30 2.15 24.73 10.73 Muskoka River Echo Lk South Muskoka River -79.08028 45.17534 2.23 18.70 16.48 201.63 10.78 Muskoka River Ellis Lk South Muskoka River -79.14463 45.11048 0.06 8.11 8.05 129.42 15.95 Muskoka River Ennis Lk North Muskoka River -79.21585 45.08873 0.12 1.19 1.07 6.00 5.06 Muskoka River Fawn Lk North Muskoka River -79.24776 45.17213 0.84 15.34 14.50 311.30 20.29 Muskoka River Fly Lk South Muskoka River -78.96735 45.19901 0.07 0.36 0.29 0.00 0.00 Muskoka River Gilleach Lk North Muskoka River -79.21734 45.15871 0.36 1.68 1.32 12.66 7.53 Muskoka River Goodman Lk South Muskoka River -79.00997 45.13031 0.17 1.02 0.85 3.73 3.66 Muskoka River Grandview Lk South Muskoka River -79.05242 45.20138 0.74 2.30 1.55 2.57 1.12 Muskoka River Halfway Lk North Muskoka River -79.22707 45.10981 0.14 3.70 3.56 29.08 7.86 Muskoka River Healey Lk South Muskoka River -79.18811 45.08225 1.26 5.17 3.91 98.87 19.11 Muskoka River Heney Lk South Muskoka River -79.10383 45.12778 0.21 1.39 1.17 5.36 3.86 Muskoka River Hillman Lk North Muskoka River -79.3827 45.09221 0.14 3.61 3.47 19.79 5.48 Muskoka River Kawpakwakog River South Muskoka River -79.07662 45.09205 0.08 47.69 47.62 1072.54 22.49 Muskoka River Leech Lk South Muskoka River -79.09685 45.05222 0.81 4.21 3.40 26.68 6.33 Muskoka River Lower Twin Lk North Muskoka River -79.20578 45.18107 0.07 0.73 0.67 10.24 13.93 Muskoka River Martin Lk South Muskoka River -78.98994 45.19219 0.52 4.57 4.05 48.92 10.70 Muskoka River McKay Lk South Muskoka River -79.1828 45.055 1.31 8.23 6.91 63.12 7.67

App.B WQ_Model_Watershed_Stats_2012.xlsx 6 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL Muskoka River McRey Lk South Muskoka River -79.19947 45.07943 0.20 1.15 0.95 15.78 13.76 Muskoka River Moot Lk North Muskoka River -79.17335 45.14376 0.48 5.50 5.02 102.19 18.57 Muskoka River Muskoka River North Muskoka River -79.37657 45.02588 0.55 44.51 43.97 266.72 5.99 Muskoka River Muskoka River (north) North Muskoka River -79.30352 45.13907 1.45 179.67 178.22 1055.58 5.88 Muskoka River Muskoka River (south) South Muskoka River -79.30651 45.01781 0.21 5.30 5.09 0.00 0.00 Muskoka River Muskoka River (south) South Muskoka River -79.14613 45.07052 3.30 98.77 95.47 1315.92 13.32 Muskoka River North Healey Lk South Muskoka River -79.19176 45.09638 0.36 16.23 15.87 274.61 16.92 Muskoka River Pine Lk South Muskoka River -79.06748 45.06597 0.84 4.31 3.48 19.11 4.43 Muskoka River Ril Lk South Muskoka River -79.02728 45.16796 1.44 9.27 7.84 73.04 7.88 Muskoka River Sage Lk North Muskoka River -79.19484 45.15554 0.27 2.06 1.79 40.11 19.43 Muskoka River Shack Lk South Muskoka River -79.05926 45.10099 0.16 0.86 0.70 0.46 0.53 Muskoka River Spence Lk North South Muskoka River -79.2786 45.00382 0.91 20.59 19.68 216.41 10.51 Muskoka River Spence Lk South South Muskoka River -79.29656 44.99677 0.17 0.67 0.51 12.09 17.94 Muskoka River Splatter Lk South Muskoka River -78.99087 45.18209 0.13 1.49 1.36 18.39 12.34 Muskoka River Spring Lk South Muskoka River -79.13291 45.01318 0.27 1.34 1.08 11.43 8.50 Muskoka River Stoneleigh Lk North Muskoka River -79.22899 45.10042 0.52 6.53 6.01 116.03 17.77 Muskoka River Tackaberry Lk South Muskoka River -79.13997 45.11882 0.11 0.69 0.58 2.47 3.59 Muskoka River Upper Twin Lk North Muskoka River -79.2021 45.1847 0.08 1.50 1.41 38.47 25.67 Muskoka River West Buck Lk South Muskoka River -79.09278 45.02978 0.21 1.85 1.64 14.66 7.92 Muskoka River Whitehouse Lk South Muskoka River -79.06849 45.14077 0.11 1.45 1.34 16.68 11.49 Muskoka River Wildcat Lk South Muskoka River -79.03906 45.17665 0.17 1.48 1.31 0.00 0.00 Muskoka River Wood Lk South Muskoka River -79.07439 45.01506 3.84 16.55 12.71 110.83 6.70 Muskoka River Woodbine Lk North Muskoka River -79.2194 45.17929 0.11 3.29 3.18 74.09 22.49 Musquash 260 Musquash River -79.75211 44.9693 2.63 36.73 34.11 597.63 16.27 Musquash Bastedo Lk Musquash River -79.53925 44.94025 0.15 0.68 0.53 3.17 4.68 Musquash Bear Lk Musquash River -79.77265 44.96167 0.20 1.12 0.93 5.82 5.17 Musquash Big Otter Lk Musquash River -79.57584 44.95582 0.17 1.80 1.62 19.52 10.87 Musquash Boleau Lk Musquash River -79.56344 44.90457 0.39 5.85 5.45 111.52 19.07 Musquash Brophy Lk Musquash River -79.73165 44.97047 0.27 17.79 17.52 315.77 17.75 Musquash Brotherson's Lk Musquash River -79.58241 44.9262 0.58 4.18 3.61 61.02 14.58 Musquash Coldwater/Swan Lk Moon River -79.80484 44.99948 0.44 3.06 2.62 30.46 9.96 Musquash Flatrock Lk Moon River -79.84123 45.03325 0.62 8.46 7.84 141.96 16.78 Musquash Go Home Lk Moon River -79.85822 45.0056 6.70 29.63 22.93 225.23 7.60 Musquash Go Home River Moon River -79.89487 45.01205 0.92 14.39 13.47 219.05 15.22 Musquash Gray Lk Moon River -79.80847 45.03329 0.41 5.18 4.76 68.22 13.18 Musquash Hart Lk Musquash River -79.57788 44.91201 0.20 1.67 1.47 28.01 16.81 Musquash Harts Lk Moon River -79.67706 45.01521 0.10 1.20 1.11 16.88 14.02 Musquash Irvine Lk Moon River -79.8583 45.04242 0.45 4.33 3.87 70.65 16.33 Musquash Lafarce Lk Musquash River -79.74214 44.98905 0.23 4.53 4.30 41.18 9.09 Musquash Little Otter Lk Musquash River -79.56723 44.95753 0.09 2.09 2.01 38.67 18.46 Musquash Lower Musquash River Moon River -79.87519 44.9661 0.59 3.59 3.00 14.29 3.99 Musquash Lunnen Lk Moon River -79.79577 45.02211 0.25 1.41 1.15 9.38 6.67 Musquash Musquash River Moon River -79.7328 45.01107 2.37 42.20 39.83 402.72 9.54 Musquash Narrow Lk Musquash River -79.58473 44.92385 0.37 1.94 1.57 21.68 11.20 Musquash Nine Mile Lk Musquash River -79.57931 44.94831 2.27 34.45 32.18 771.92 22.41 Musquash Pence Lk Musquash River -79.51279 44.88141 0.42 8.91 8.49 137.55 15.43

App.B WQ_Model_Watershed_Stats_2012.xlsx 7 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL Musquash Rat Lk Musquash River -79.52048 44.86975 0.58 3.79 3.22 55.01 14.50 Musquash Sahanatien Lk Musquash River -79.75453 44.98873 0.20 1.38 1.18 33.41 24.16 Musquash Shaw Lk Musquash River -79.58462 44.90465 0.14 6.77 6.63 97.76 14.45 Musquash Surerus Lk Moon River -79.9044 45.02436 0.18 2.23 2.04 39.72 17.85 Musquash Turtle Lk Severn River -79.44523 44.92275 0.30 1.15 0.85 15.86 13.81 Musquash Twin Lakes Musquash River -79.63043 44.9413 0.28 19.56 19.28 372.50 19.04 Musquash Unnamed Lk Moon River -79.88068 45.02783 0.43 3.33 2.90 30.01 9.01 Musquash Upper Gibson River Musquash River -79.79555 44.97305 0.49 19.14 18.65 314.82 16.45 Musquash Webster Lk Musquash River -79.77256 44.99729 0.23 7.93 7.70 82.59 10.41 Musquash Woodland Lk Musquash River -79.55035 44.90378 0.82 12.33 11.52 176.53 14.32 Rosseau Barnes Lk Skeleton Lake -79.51579 45.2565 0.49 3.20 2.71 42.95 13.42 Rosseau Beaton Lk Skeleton Lake -79.53269 45.23423 0.17 0.73 0.56 3.63 4.97 Rosseau Blair Lk Lakes Rosseau and Joseph -79.66744 45.30752 0.06 14.47 14.42 193.95 13.40 Rosseau Bogart Lk Three Mile Lake -79.5252 45.19423 0.29 3.48 3.19 43.99 12.64 Rosseau Burr Lk Lakes Rosseau and Joseph -79.72997 45.26729 0.18 0.88 0.70 0.30 0.34 Rosseau Camel Lk Three Mile Lake -79.42334 45.15934 0.58 2.18 1.61 7.79 3.57 Rosseau Cardwell Lk Cardwell Lake -79.49054 45.33252 2.01 11.41 9.40 183.87 16.11 Rosseau Carter Lk Lakes Rosseau and Joseph -79.70321 45.24135 0.23 1.33 1.10 9.01 6.76 Rosseau Clark Pond Three Mile Lake -79.54993 45.17935 0.23 4.30 4.08 1.15 0.27 Rosseau Cowan Lk Lake Muskoka -79.49058 45.14895 0.21 0.97 0.76 20.30 20.92 Rosseau Gagnon Lk Lakes Rosseau and Joseph -79.5319 45.24489 0.03 0.40 0.37 6.36 15.77 Rosseau Hammell Lk Lakes Rosseau and Joseph -79.69488 45.22678 0.05 0.15 0.10 0.00 0.00 Rosseau High Lk Skeleton Lake -79.49825 45.23566 0.64 2.24 1.60 7.86 3.51 Rosseau Hurst Lk Lakes Rosseau and Joseph -79.71622 45.2462 0.11 0.52 0.41 9.56 18.31 Rosseau Joseph River Lakes Rosseau and Joseph -79.67373 45.15257 1.34 9.42 8.08 99.37 10.55 Rosseau Kimmins Lk Lakes Rosseau and Joseph -79.72442 45.25296 0.09 1.39 1.30 28.07 20.17 Rosseau Lake Joseph - Cox Bay Lakes Rosseau and Joseph -79.62231 45.1116 1.86 6.48 4.62 21.86 3.37 Rosseau Lake Rosseau - Brackenrig Lakes Rosseau and Joseph -79.52922 45.11853 0.43 2.57 2.14 6.53 2.54 Rosseau Lake Rosseau - East Lakes Rosseau and Joseph -79.56867 45.16757 7.18 17.41 10.23 26.97 1.55 Rosseau Lake Rosseau - East Portage Lakes Rosseau and Joseph -79.5245 45.15383 1.32 4.44 3.12 15.91 3.58 Rosseau Lake Rosseau - Main Lakes Rosseau and Joseph -79.59277 45.18297 35.04 61.94 26.90 107.89 1.74 Rosseau Lake Rosseau - Morgan Lakes Rosseau and Joseph -79.65681 45.23286 3.15 9.62 6.47 13.11 1.36 Rosseau Lake Rosseau - North Lakes Rosseau and Joseph -79.61685 45.2214 14.99 182.06 167.07 1845.85 10.14 Rosseau Lake Rosseau - Skeleton Lakes Rosseau and Joseph -79.57148 45.21365 1.69 17.62 15.92 108.15 6.14 Rosseau Lamberts Lk Skeleton Lake -79.4552 45.29483 0.10 0.52 0.41 9.40 18.21 Rosseau Lily Lk Cardwell Lake -79.57139 45.23803 0.12 0.43 0.31 0.00 0.00 Rosseau Little Long Lk Skeleton Lake -79.51804 45.2484 0.21 1.33 1.12 3.23 2.43 Rosseau Longs Lk Three Mile Lake -79.35362 45.20459 0.43 12.06 11.62 100.03 8.30 Rosseau Mainhood Lk Three Mile Lake -79.35076 45.21338 0.16 0.69 0.53 1.67 2.41 Rosseau Matthews Lk Cardwell Lake -79.46078 45.31384 0.03 0.47 0.44 5.50 11.68 Rosseau McCan Lk Lakes Rosseau and Joseph -79.65151 45.27999 0.10 2.99 2.88 17.35 5.80 Rosseau Motley Lk Lakes Rosseau and Joseph -79.7169 45.26694 0.07 1.34 1.27 8.10 6.04 Rosseau Nutt Lk Skeleton Lake -79.44915 45.21363 0.08 0.94 0.86 1.81 1.94 Rosseau Petty Lk Lakes Rosseau and Joseph -79.68715 45.31971 0.08 9.12 9.04 128.92 14.13 Rosseau Skeleton Lk Skeleton Lake -79.45713 45.24903 20.54 67.22 46.68 372.58 5.54 Rosseau Stock Lk Lakes Rosseau and Joseph -79.69277 45.22915 0.08 2.05 1.97 42.88 20.90

App.B WQ_Model_Watershed_Stats_2012.xlsx 8 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL Rosseau Sucker Lk Lakes Rosseau and Joseph -79.68132 45.24922 1.09 4.92 3.83 7.28 1.48 Rosseau Three Mile Lk Three Mile Lake -79.50887 45.17603 6.33 113.31 106.99 1367.05 12.06 Rosseau Three Mile Lk - Hammel Three Mile Lake -79.47162 45.19053 2.33 15.51 13.17 109.80 7.08 Rosseau Watson Lk Lakes Rosseau and Joseph -79.70705 45.25992 0.32 3.51 3.19 10.24 2.92 Rosseau Wier Lk Lakes Rosseau and Joseph -79.55015 45.23655 0.18 4.10 3.92 44.66 10.88 Rosseau Woods Lk Cardwell Lake -79.60549 45.26927 0.11 1.35 1.25 22.42 16.58 Rosseau Young Lk Skeleton Lake -79.54722 45.2044 1.09 5.99 4.90 24.92 4.16 Sparrow Barkway Lk Kahshe Lake -79.16187 44.9024 0.15 45.28 45.13 634.45 14.01 Sparrow Bass Lk Kahshe Lake -79.206 44.87278 0.40 17.40 17.00 177.34 10.19 Sparrow Ben Lk Kahshe Lake -79.20418 44.89159 0.36 2.93 2.57 21.15 7.22 Sparrow Cabin Lk Kahshe Lake -79.09766 44.94605 0.16 1.47 1.31 11.47 7.80 Sparrow Clearwater Lk Severn River -79.24231 44.80755 0.70 1.55 0.84 4.32 2.80 Sparrow Cornall Lk Severn River -79.32901 44.89078 0.25 7.43 7.18 99.70 13.43 Sparrow Doe Lk Kahshe Lake -79.28348 44.92274 0.37 4.59 4.22 85.47 18.63 Sparrow Fawn Lk Kahshe Lake -79.25973 44.91717 0.19 30.75 30.56 416.33 13.54 Sparrow Gartersnake Lk Kahshe Lake -79.12724 44.96193 0.31 12.44 12.14 148.98 11.97 Sparrow Island Lk Kahshe Lake -79.12195 44.98584 0.08 0.65 0.56 2.12 3.27 Sparrow Ivy Lk Kahshe Lake -79.12794 44.98563 0.03 0.20 0.18 2.23 10.87 Sparrow Jevins Lk Severn River -79.35013 44.90097 0.36 1.93 1.57 39.07 20.19 Sparrow Kahshe Lk Kahshe Lake -79.30142 44.854 8.21 40.74 32.53 471.11 11.56 Sparrow Little Sunny Lk Kahshe Lake -79.27308 44.87648 0.16 2.25 2.09 27.63 12.28 Sparrow Moose Lk Severn River -79.4141 44.85002 0.18 0.91 0.74 19.17 21.03 Sparrow Prospect Lk Kahshe Lake -79.13525 44.99256 0.69 8.96 8.27 88.28 9.85 Sparrow Ryde Lk Kahshe Lake -79.24677 44.90214 0.82 38.33 37.51 398.89 10.41 Sparrow Sparrow Lk - McLeans Severn River -79.39906 44.84198 1.05 25.69 24.63 250.78 9.76 Sparrow Sunny Lk Kahshe Lake -79.29814 44.91015 0.50 2.16 1.67 23.02 10.65 Sparrow Three Mile Lk Kahshe Lake -79.26336 44.88503 0.83 6.62 5.79 76.26 11.51 Sparrow Weismuller Lk Kahshe Lake -79.21417 44.93762 0.14 1.78 1.64 1.17 0.66 Trading Bay Charcoal Lk Lake of Bays -78.89575 45.26662 0.20 2.34 2.14 0.00 0.00 Trading Bay Lake of Bays - Pancake Lake of Bays -78.88579 45.25082 0.71 409.26 408.55 1525.50 3.73 Trading Bay Luck Lk Kawagama Lake -78.69837 45.44024 0.35 1.02 0.67 0.27 0.26 Trading Bay Mink Lk South Muskoka River -78.97631 45.22186 0.37 1.72 1.35 4.26 2.47 Trading Bay Paint Lk Lake of Bays -78.94701 45.21843 1.57 14.12 12.55 114.56 8.12 Trading Bay Tom Lk Lake of Bays -78.98016 45.23167 0.18 0.75 0.57 0.59 0.78 Trading Bay Wolfkin Lk Lake of Bays -78.9522 45.23558 0.19 1.74 1.54 5.40 3.11 Vernon Arrowhead Lk Lake Waseosa -79.20181 45.39603 0.65 27.34 26.69 397.99 14.56 Vernon Bella Lk Bella / Rebecca Lakes -79.03051 45.44308 3.57 15.06 11.49 66.60 4.42 Vernon Benson Lk Bella / Rebecca Lakes -79.06675 45.41284 0.36 2.74 2.37 26.11 9.54 Vernon Big East River Bella / Rebecca Lakes -79.1118 45.4281 2.86 459.96 457.10 2398.98 5.22 Vernon Bing Lk Lake Waseosa -79.23011 45.43629 0.26 1.05 0.80 2.99 2.83 Vernon Bittern Lk Huntsville Lakes -79.3035 45.44128 0.56 4.38 3.82 3.87 0.88 Vernon Buck River Huntsville Lakes -79.3574 45.40014 0.14 7.49 7.36 50.77 6.77 Vernon Camp Lk Bella / Rebecca Lakes -78.90852 45.43937 2.02 8.28 6.26 10.95 1.32 Vernon Clark Lk Lake Waseosa -79.29758 45.40416 0.27 4.60 4.33 66.16 14.39 Vernon Dot Lk Bella / Rebecca Lakes -78.90607 45.42283 0.05 0.42 0.37 0.00 0.00 Vernon Doughnut Lk Bella / Rebecca Lakes -78.90823 45.4645 0.11 0.70 0.59 0.00 0.00

App.B WQ_Model_Watershed_Stats_2012.xlsx 9 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL Vernon Fawn Lk Huntsville Lakes -79.37989 45.41507 0.14 3.00 2.86 19.28 6.43 Vernon Flossie Lk Bella / Rebecca Lakes -78.88655 45.44795 0.33 5.66 5.33 11.10 1.96 Vernon Foote Lk Bella / Rebecca Lakes -79.18233 45.47431 1.27 10.87 9.60 51.34 4.72 Vernon Fox Lk Huntsville Lakes -79.35602 45.3834 1.42 6.65 5.23 16.96 2.55 Vernon Golden City Lk Huntsville Lakes -79.41576 45.31514 0.18 0.89 0.71 10.25 11.49 Vernon Greenish Lk Bella / Rebecca Lakes -79.00407 45.5028 0.09 8.26 8.17 48.80 5.91 Vernon Haller Lk Huntsville Lakes -79.30858 45.43075 0.13 14.75 14.62 273.87 18.57 Vernon Heck Lk Bella / Rebecca Lakes -79.0336 45.46189 0.33 2.91 2.58 21.84 7.51 Vernon Hendersons Lk Huntsville Lakes -79.36989 45.33312 0.09 2.11 2.03 22.57 10.69 Vernon Jessop Lk Lake Waseosa -79.26952 45.38787 0.29 2.19 1.90 24.56 11.24 Vernon Lake Vernon Huntsville Lakes -79.2955 45.33502 13.71 109.85 96.15 1278.46 11.64 Vernon Lake Vernon - Hunter's Huntsville Lakes -79.24004 45.32369 0.79 6.51 5.72 9.86 1.51 Vernon Lake Vernon - North Huntsville Lakes -79.33308 45.3548 0.96 18.66 17.70 158.88 8.52 Vernon Little Arrowhead Lk Lake Waseosa -79.19234 45.4085 0.21 1.62 1.41 9.47 5.85 Vernon Little Clear Lk Bella / Rebecca Lakes -79.00764 45.40227 0.11 1.30 1.19 2.61 2.02 Vernon Little Hardy Lk Bella / Rebecca Lakes -78.8692 45.4795 0.19 4.71 4.52 11.01 2.34 Vernon Loon Lk Bella / Rebecca Lakes -78.98637 45.42259 0.32 2.03 1.72 3.69 1.81 Vernon Lower Raft Lk Bella / Rebecca Lakes -79.12887 45.46309 0.20 2.76 2.56 25.40 9.20 Vernon Mansell Lk Bella / Rebecca Lakes -79.02861 45.42094 0.12 0.57 0.46 2.95 5.15 Vernon McCraney Lk Bella / Rebecca Lakes -78.90616 45.57048 3.92 57.01 53.08 240.86 4.23 Vernon Mirage Lk Bella / Rebecca Lakes -79.21771 45.47516 0.57 5.53 4.95 0.00 0.00 Vernon Nelson Lk Bella / Rebecca Lakes -78.95754 45.49201 0.33 2.31 1.98 1.54 0.67 Vernon Onawaw Lk Huntsville Lakes -79.32417 45.30548 0.14 2.68 2.54 97.58 36.35 Vernon Oudaze Lk Bella / Rebecca Lakes -79.19552 45.45374 1.27 9.81 8.55 26.12 2.66 Vernon Palette Lk Lake Waseosa -79.26888 45.41704 0.15 0.74 0.59 0.03 0.04 Vernon Perch Lk Lake Waseosa -79.23206 45.45388 1.01 47.48 46.47 174.22 3.67 Vernon Rebecca Lk Bella / Rebecca Lakes -79.05973 45.42903 2.13 11.41 9.28 90.56 7.94 Vernon Ripple Lk Lake Waseosa -79.27663 45.42118 0.18 0.50 0.32 0.00 0.00 Vernon Sims Lk Huntsville Lakes -79.45525 45.33756 0.16 17.46 17.30 507.17 29.05 Vernon Skunk Lk Bella / Rebecca Lakes -78.86735 45.47104 0.06 0.39 0.34 0.79 2.01 Vernon Slim Lk Bella / Rebecca Lakes -78.89055 45.47657 0.08 3.99 3.90 10.62 2.67 Vernon Sly Lk Bella / Rebecca Lakes -78.89131 45.46304 0.11 1.62 1.52 5.35 3.30 Vernon Solitaire Lk Bella / Rebecca Lakes -79.00795 45.3918 1.22 3.78 2.57 10.32 2.73 Vernon South Tasso Lk Bella / Rebecca Lakes -78.92662 45.43011 0.19 1.02 0.83 0.00 0.00 Vernon Surprise Lk Bella / Rebecca Lakes -79.15798 45.45943 0.29 1.55 1.25 3.45 2.23 Vernon Tasso Lk Bella / Rebecca Lakes -78.93404 45.45916 1.75 12.21 10.46 26.62 2.18 Vernon Toad Lk Bella / Rebecca Lakes -78.93876 45.43866 0.34 2.19 1.85 5.83 2.67 Vernon Trackler Lk Huntsville Lakes -79.37046 45.3208 0.12 0.55 0.43 1.65 3.01 Vernon Unnamed Lk Huntsville Lakes -79.36795 45.36455 0.24 1.62 1.38 13.31 8.24 Vernon Upper Raft Lk Bella / Rebecca Lakes -79.13459 45.47431 0.35 6.37 6.02 2.30 0.36 Vernon Verner Lk Bella / Rebecca Lakes -79.14189 45.45647 0.20 0.84 0.65 0.00 0.00 Vernon Waseosa Lk Lake Waseosa -79.2754 45.40487 1.55 8.49 6.95 53.91 6.35 Vernon West Frog Lk Bella / Rebecca Lakes -78.88301 45.46393 0.06 0.61 0.56 0.00 0.00 West Barron's Lk Severn River -79.75094 44.83049 0.55 7.81 7.27 164.18 21.01 West Baxter Lk Severn River -79.75845 44.88252 0.40 2.71 2.30 15.31 5.66 West Buck Lk Severn River -79.79304 44.90572 0.23 0.67 0.44 3.68 5.52

App.B WQ_Model_Watershed_Stats_2012.xlsx 10 M_Wshd_Name Watershed_Name Q_Wshd_Name Long Lat A_sqkm_WB A_sqkm_Wshd_Total A_sqkm_Wshd_ExWB A_ha_WL A_percent_WL West Davies Lk Moon River -79.88549 45.05261 0.12 1.13 1.01 7.60 6.75 West Galla Lk Moon River -79.86062 45.06072 0.49 5.03 4.54 43.61 8.67 West Leclaric Lk Moon River -79.9143 45.03603 0.17 0.73 0.56 2.56 3.51 West Little Go Home Bay Severn River -79.73077 44.8609 1.12 5.37 4.25 44.92 8.37 West Lone Lk Severn River -79.67829 44.93254 0.30 3.45 3.15 61.06 17.72 West Lower Galla Lk Moon River -79.89131 45.06551 0.34 9.83 9.48 109.07 11.10 West McDonald Lk Severn River -79.78046 44.93065 0.14 2.93 2.79 41.43 14.14 West Mosquito Lk Severn River -79.61934 44.91426 0.14 1.44 1.30 14.38 10.02 West Six Mile Channel Severn River -79.69103 44.88375 0.60 3.16 2.56 12.36 3.91 West Six Mile Lk Severn River -79.75544 44.91631 12.15 44.10 31.95 442.85 10.04 West Six Mile Lk - Cedar Nook Severn River -79.74923 44.90512 0.23 2.33 2.10 34.70 14.92 West Six Mile Lk - Prov Park Severn River -79.73498 44.88722 1.31 5.12 3.81 40.55 7.92 West South Bay Severn River -79.79664 44.8716 1.46 13.58 12.12 167.35 12.33 West Stuart Lk Severn River -79.78776 44.91801 0.16 0.96 0.80 13.23 13.78 West Tadenac Bay Moon River -79.97157 45.04548 4.12 24.07 19.95 272.69 11.33 West Tadenac Lk Moon River -79.92848 45.04514 2.69 16.91 14.22 182.76 10.81 West Twelve Mile Bay Moon River -80.04313 45.08808 2.98 30.16 27.18 421.85 13.99

Field Descriptions: M_Wshd_Name: Name of the larger watershed as defined by the Model. Watershed_Name: same as the waterbody name - basic unit of analysis. Connection: Unique name for each waterbody / watershed. This was created because there are many lakes with duplicated names. Q_Wshd_Name: Name of the larger watershed as defined by the MNR Quaternary Watershed layer. Q_Wshd_Num: Number of the larger watershed as defined by the MNR Quaternary Watershed layer. 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 including the surface area of the main waterbody

App.B WQ_Model_Watershed_Stats_2012.xlsx 11 J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

Appendix C. Phosphorus Data used for Calculation of the 2005- 2014 10-Year Mean and 15 year (2000-2014) Trend Assessments (Excl. Outliers)

Hutchinson Environmental Sciences Ltd.

R05042016_150074_MWQMLSH_final.docx C1

Table 3. Total Phosphorus Concentration in District of Muskoka Lakes (n=196)

Total Phosphorus Concentration (g/L) Site Mean Mean 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (2005- (2000- 2014) 2014) Ada Lake 22.4 12.2 18.5 16.6 30.5 21.9 20.0 Atkins Lake 8.2 9.2 8.2 8.7 7.3 8.1 8.3 Axle Lake 11.6 6.3 7.5 4.6 6.4 6.2 7.3 Barrons Lake 28.4 28.2 28.2 28.3 Bass Lake GR 22.9 21.3 22.8 17.6 20.2 21.2 Bass Lake ML 6.4 7.5 5.8 7.2 6.8 6.7 Bastedo Lake 12.7 8.2 6.2 12.8 5.5 8.2 9.1 Baxter Lake 10.3 9.0 10.9 12.2 9.0 12.3 10.7 10.6 Bearpaw Lake 13.0 13.5 18.7 11.5 14.5 14.9 14.2 Bella Lake 8.4 7.8 9.1 9.5 8.4 5.4 6.3 7.7 7.8 Ben Lake 8.5 9.3 7.7 10.6 9.2 9.0 Bigwind Lake 6.1 5.2 6.7 5.7 5.9 6.7 5.7 6.1 6.0 Bing Lake 6.4 4.4 6.2 5.5 4.6 5.4 5.4 Bird Lake 9.8 12.4 12.4 12.4 10.1 10.3 8.5 10.3 10.8 Black Lake 20.8 16.0 21.3 15.3 12.9 16.5 17.3 Bonnie Lake 6.6 5.0 4.5 5.8 5.2 5.5 Brandy Lake 22.9 20.5 18.2 21.1 20.7 20.7 Brooks Lake 6.6 7.2 13.8 4.5 8.2 15.6 9.9 9.3 Bruce Lake 8.0 9.1 13.4 9.8 12.5 12.2 9.1 7.8 10.3 10.2 Buck Lake HT 11.5 11.0 14.9 13.2 13.0 12.7 Buck Lake LOB 8.3 8.0 6.6 8.4 5.8 7.2 7.4 Butterfly Lake 12.3 11.9 16.5 10.2 14.4 13.7 13.1 Camel Lake 9.3 9.3 7.3 10.9 6.3 8.2 8.6 Camp Lake 4.6 2.1 4.0 5.1 2.4 3.8 3.6

Hutchinson Environmental Sciences Ltd.

M250914_J100059_TP update 7

Total Phosphorus Concentration (g/L) Site Mean Mean 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (2005- (2000- 2014) 2014) Cardwell Lake 9.7 7.7 8.9 6.8 10.3 8.7 8.7 Cassidy Lake 11.5 11.2 9.6 8.7 11.2 10.4 10.4 Chub Lake HT 10.3 9.6 8.1 11.2 10.0 9.7 9.8 Chub Lake LOB 10.4 8.4 6.9 13.1 9.8 9.6 9.7 Clark Lake 14.9 12.6 11.7 9.9 9.6 10.4 11.7 Clear Lake BB 5.9 6.4 6.1 6.3 6.1 Clear Lake ML 6.5 7.4 5.8 7.7 7.0 6.9 Clearwater Lake GR 8.3 4.8 5.9 6.1 4.4 3.9 4.7 5.0 5.4 Clearwater Lake HT 10.0 6.6 7.3 5.0 3.8 6.7 5.9 6.6 Cooper Lake 9.1 11.3 6.3 10.5 9.4 9.3 Cornall Lake 10.2 9.2 10.4 9.0 9.7 9.7 Crosson Lake 10.1 8.6 10.5 9.6 12.2 7.5 10.0 9.8 Dark Lake 14.1 7.9 8.1 11.3 7.2 8.5 8.6 9.5 Deer Lake 5.8 8.6 5.2 5.9 7.0 6.3 6.6 6.5 Devine Lake 14.8 15.4 15.3 10.6 11.5 9.6 13.3 12.1 12.9 Dickie Lake 8.4 6.2 8.7 9.8 7.1 8.5 8.0 Doeskin Lake 18.6 14.1 14.9 15.8 14.9 15.9 Dotty Lake 6.6 5.2 7.3 6.4 6.0 6.6 6.3 Echo Lake 9.2 7.1 7.0 5.7 8.2 7.0 7.4 Fairy Lake - Main 9.3 9.4 7.2 8.2 9.7 7.4 7.4 9.5 8.4 8.5 Fairy Lake - NMRB 8.7 7.7 7.8 10.3 8.6 8.6 Fairy Lake - Rogers Cove 12.0 9.1 10.5 10.5 Fawn Lake 21.1 15.0 15.9 19.6 13.3 12.6 15.3 16.3 Fifteen Mile Lake 6.7 6.0 6.0 3.3 4.5 6.0 5.2 5.4 Flatrock Lake 8.6 6.8 5.6 7.8 11.1 9.4 8.1 8.2 Foote Lake 8.8 9.2 8.7 11.0 10.0 9.7 9.5

Hutchinson Environmental Sciences Ltd.

M250914_J100059_TP update 8

Total Phosphorus Concentration (g/L) Site Mean Mean 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (2005- (2000- 2014) 2014) Fox Lake 12.9 11.4 13.5 12.2 13.0 12.9 12.6 Galla Lake 6.7 8.1 8.7 6.8 7.9 7.6 Gartersnake Lake 16.1 14.3 12.8 14.0 12.3 13.0 13.9 Gibson Lake - North 13.3 9.4 10.7 9.8 10.3 10.3 10.7 Gibson Lake - South 17.4 10.6 13.4 12.5 10.8 12.2 12.9 Gilleach Lake 15.7 10.1 8.8 9.2 11.2 9.7 11.0 Go Home Bay 6.9 8.4 7.7 7.7 Go Home Lake 6.3 7.9 7.4 7.0 5.0 7.6 7.0 6.9 Golden City Lake 19.3 12.7 12.6 8.7 18.6 13.3 14.4 Grandview Lake 8.0 6.8 4.6 5.6 5.3 5.6 6.1 Grindstone Lake 7.2 9.1 22.5 16.7 7.5 8.3 13.8 11.9 Gull Lake 6.0 6.0 6.4 6.8 5.2 6.1 6.1 Gullfeather Lake 12.4 10.1 9.4 9.8 9.8 10.4 Gullwing Lake 15.6 12.7 14.2 13.7 7.0 11.6 12.6 Haggart Lake 14.6 9.6 10.3 8.6 11.0 10.0 10.8 Halfway Lake 20.2 12.2 13.3 15.7 12.1 13.7 14.7 Hardup Lake 7.0 9.6 7.4 6.4 7.6 7.6 Healey Lake 15.2 9.5 8.2 9.2 9.0 10.5 Heney Lake 6.9 6.5 9.3 9.7 4.5 4.9 6.4 7.0 Henshaw Lake 4.8 6.8 5.3 5.5 5.9 5.6 Hesners Lake 11.2 7.0 6.4 8.6 9.4 6.4 8.1 8.2 High Lake 9.3 4.0 6.2 5.8 3.5 3.6 4.5 4.7 5.3 Jessop Lake 21.2 13.4 14.3 10.6 12.5 12.5 14.4 Jevins Lake 19.9 10.5 12.0 14.4 11.8 12.7 13.7 Joseph River 7.9 6.4 9.3 11.1 8.8 5.6 5.3 6.0 6.2 7.1 7.3 7.4 Kahshe Lake - Grants Bay 28.5 16.2 12.7 10.6 17.8 10.1 12.8 16.0

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M250914_J100059_TP update 9

Total Phosphorus Concentration (g/L) Site Mean Mean 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (2005- (2000- 2014) 2014) Kahshe Lake - Main 16.2 12.3 13.6 11.5 11.6 10.4 11.5 11.3 12.4 Lake Huron-Cognashene 4.9 8.0 5.0 6.0 6.0 Bay Lake Huron-Little Go-Home 10.9 10.9 10.9 10.9 Bay Lake Huron-North Bay 13.8 10.9 12.1 10.8 10.6 11.2 11.6 Lake Huron-Tadenac Bay 7.1 6.2 6.8 5.2 6.1 6.3 Lake Huron-Twelve Mile 9.3 13.0 11.1 12.1 3.2 9.7 9.7 Bay - East Lake Huron-Twelve Mile 5.4 9.2 5.3 3.8 2.4 12.8 6.7 6.5 Bay - West Lake Huron-Wah Wah 4.8 2.8 3.2 3.6 3.6 Taysee Lake Joseph-Cox Bay 4.7 5.6 4.5 5.5 6.1 5.3 3.7 4.3 3.1 4.4 5.6 4.7 4.8 Lake Joseph-Hamer Bay 4.1 4.6 3.1 3.7 2.7 3.4 3.9 3.6 3.6 Lake Joseph-Main 5.3 5.0 5.5 9.1 3.9 3.6 3.3 4.4 2.9 3.5 4.0 4.5 4.6 Lake Joseph-North 4.1 3.6 3.3 3.5 2.8 3.4 4.0 3.5 3.5 Lake Joseph-South 4.5 6.5 3.9 3.8 2.8 3.8 4.2 4.2 4.2 Lake Muskoka - Bala Bay 8.1 7.2 4.8 6.1 6.5 4.9 5.1 6.1 5.7 6.1 Lake Muskoka - Dudley Bay 5.0 7.6 5.7 5.2 7.1 4.7 4.8 5.9 5.5 5.8 Lake Muskoka - Main 7.6 5.5 5.1 5.6 5.2 5.6 7.4 5.8 6.0 Lake Muskoka - Muskoka 20.5 10.9 9.4 12.3 12.2 6.6 5.7 9.6 9.3 10.9 Bay Lake Muskoka - South Basin 5.2 #DIV/ 5.2 0! Lake Muskoka - Whiteside 6.8 6.4 5.1 5.4 6.5 4.7 4.9 6.0 5.5 5.7 Bay Lake of Bays - Dwight Bay 4.9 6.2 8.5 6.4 8.7 5.8 6.9 7.3 6.8 Lake of Bays - Haystack Bay 4.7 4.7 4.0 4.2 4.0 4.6 4.3 4.4 Lake of Bays - Rat Bay 6.8 6.2 6.7 6.0 6.0 6.7 6.4 6.4

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M250914_J100059_TP update 10

Total Phosphorus Concentration (g/L) Site Mean Mean 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (2005- (2000- 2014) 2014) Lake of Bays - SMRB 5.8 6.2 4.2 4.0 4.1 3.9 4.5 4.7 Lake of Bays - South 4.6 5.5 7.4 4.8 6.3 5.8 5.3 5.9 5.7 Portage Bay Lake of Bays - Ten Mile Bay 6.5 5.3 4.4 3.7 5.2 3.7 4.7 4.3 4.8 Lake of Bays - Trading Bay 8.5 5.8 3.5 3.5 6.1 4.0 4.9 4.4 5.2 Lake Rosseau-Brackenrig 6.9 7.0 8.0 7.2 10.1 12.3 8.5 9.2 8.6 Bay Lake Rosseau-East Portage 7.9 6.4 7.4 6.0 7.8 6.4 7.2 7.0 7.0 Bay Lake Rosseau-Main 7.0 5.7 4.8 5.1 6.1 6.0 4.6 5.3 5.6 Lake Rosseau-North 6.8 10.3 5.4 7.5 7.5 Lake Rosseau-Skeleton Bay 6.6 6.0 9.7 6.9 9.1 2.1 6.0 6.6 6.6 Lake Vernon - Hunters Bay 13.2 9.5 7.9 9.0 11.1 13.5 7.8 10.1 10.3 10.3 Lake Vernon - Main 9.3 9.4 8.6 9.8 8.9 8.1 9.7 9.0 9.1 Lake Vernon - North Bay 9.9 8.9 10.2 10.8 9.9 8.1 9.8 9.8 9.7 Lake Waseosa 9.6 8.8 11.0 7.6 8.3 9.0 8.9 9.1 Leech Lake 10.3 8.5 8.9 10.7 7.4 6.2 8.5 8.3 8.6 Leonard Lake 4.6 7.4 6.4 6.0 5.3 6.3 5.9 Little Lake Joseph 5.4 5.3 5.3 6.6 6.4 6.4 5.1 5.2 4.2 4.7 6.5 5.6 5.6 Little Long Lake 6.5 7.4 6.1 7.2 8.0 7.2 7.0 Long Lake 3.2 8.2 7.0 6.9 6.0 7.1 7.0 6.4 Longline Lake 9.4 8.1 8.7 6.2 6.2 7.7 7.7 Longs Lake 10.5 8.0 8.4 10.2 7.9 8.6 9.0 Loon Lake 14.3 7.0 10.7 20.0 7.9 6.4 11.3 11.1 Mainhood Lake 10.0 7.3 7.8 9.8 7.0 8.2 8.4 Margaret Lake 9.0 3.7 4.3 8.4 3.4 5.4 5.8 Mary Lake 9.8 9.0 9.2 9.2 7.1 7.5 8.6 8.1 8.6

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M250914_J100059_TP update 11

Total Phosphorus Concentration (g/L) Site Mean Mean 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (2005- (2000- 2014) 2014) McCrae Lake 9.1 7.3 10.3 10.4 7.7 12.5 9.6 9.6 McDonald Lake 11.3 8.2 10.9 12.0 12.0 10.8 10.9 McKay Lake 9.9 11.7 7.6 7.2 8.8 11.9 9.4 9.5 McRey Lake 12.4 14.7 11.6 8.2 16.6 12.1 12.7 Medora Lake 9.0 9.0 7.1 6.9 7.2 6.4 7.3 7.6 Menominee Lake 9.9 8.3 9.9 8.0 8.2 8.9 9.2 8.8 8.9 Mirror Lake 8.7 7.6 6.7 6.5 6.0 5.7 6.5 6.9 Moot Lake 20.5 16.5 14.1 10.4 5.7 10.6 10.2 13.0 Morrison Lake 9.1 8.6 8.4 7.8 8.7 10.2 8.8 8.8 Myers Lake 12.5 8.3 8.3 9.6 10.3 9.1 9.8 Neilson Lake 19.4 13.1 11.9 14.4 17.3 14.2 15.2 Nine Mile Lake 9.3 9.3 10.0 8.8 10.7 12.9 9.8 10.4 10.1 North Muldrew Lake 12.5 11.4 8.9 10.9 9.4 10.4 10.6 10.3 10.6 Nutt Lake 10.4 5.0 11.1 5.0 8.0 5.7 7.0 7.5 Otter Lake 10.0 7.7 8.2 6.0 11.7 8.6 8.7 Oudaze Lake 9.5 10.0 9.4 10.0 11.9 10.7 10.5 10.3 Oxbow Lake 8.3 6.8 5.2 6.1 7.8 3.8 5.9 6.3 Paint Lake 9.0 8.1 8.4 8.3 9.0 6.0 7.1 7.8 8.0 Pell Lake 11.9 11.4 11.9 12.8 12.4 12.1 12.1 Penfold Lake 13.4 16.5 17.7 14.9 13.7 15.7 15.2 Peninsula Lake - East 11.1 13.0 6.8 15.2 11.8 8.0 7.2 20.0 12.4 11.6 Peninsula Lake - West 10.3 12.1 7.2 10.3 12.4 7.5 7.2 12.3 9.9 9.9 Perch Lake 15.8 11.4 16.1 13.8 9.8 13.2 13.4 Pigeon Lake 9.1 13.8 8.6 5.4 6.1 8.5 8.6 Pine Lake BB 7.3 4.6 7.6 8.3 6.6 7.5 6.9 Pine Lake GR 13.4 8.1 9.5 8.2 7.0 8.2 9.2

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M250914_J100059_TP update 12

Total Phosphorus Concentration (g/L) Site Mean Mean 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (2005- (2000- 2014) 2014) Porcupine Lake 7.8 8.1 6.4 9.5 5.5 6.7 7.2 7.3 Prospect Lake 10.5 9.3 8.1 6.8 9.1 6.0 7.5 8.3 Rebecca Lake 6.2 5.2 6.3 6.4 6.3 5.8 5.8 6.1 6.0 Ricketts Lake 14.5 9.1 13.1 10.1 7.2 10.1 10.8 Ril Lake 6.2 11.2 8.0 6.6 6.8 8.5 8.2 7.9 Riley Lake 13.4 17.3 15.4 15.0 15.9 15.3 Rose Lake 20.6 14.4 17.8 10.1 13.3 13.7 15.2 Ryde Lake 18.9 17.1 25.0 13.3 18.5 18.6 Shoe Lake 5.4 4.5 6.6 6.8 4.1 5.8 5.5 Siding Lake 17.2 11.9 17.6 13.8 10.8 14.1 14.3 Silver Lake GR 11.1 9.4 8.8 10.1 15.1 9.9 10.5 10.9 10.7 Silver Lake ML 10.6 6.1 23.2 10.9 12.7 12.7 Silversands Lake 7.4 9.4 10.4 6.8 8.2 8.5 8.4 Six Mile Lake - Cedar Nook 11.1 8.0 8.6 8.0 9.2 7.5 8.3 8.3 8.7 Bay Six Mile Lake - Main 9.1 7.0 8.8 7.8 8.5 8.8 8.1 8.4 8.3 Six Mile Lake - Provincial 8.7 8.8 8.6 8.8 8.1 8.5 7.0 8.2 8.4 Park Bay Sixteen Mile Lake 6.6 9.2 6.0 5.2 6.8 6.8 Skeleton Lake 2.5 3.8 2.9 3.0 2.4 2.7 2.7 2.9 Solitaire Lake 5.2 6.3 6.1 6.9 4.6 5.8 5.8 South Bay 11.3 9.3 11.2 17.1 13.3 13.9 12.4 South Muldrew Lake 8.8 10.3 7.6 7.2 8.7 9.7 7.2 8.2 8.5 South Nelson Lake 8.0 7.9 9.5 10.5 6.4 8.6 8.5 Sparrow Lake 10.0 14.1 11.9 10.2 13.0 10.5 15.3 12.3 12.1 Sparrow Lake - McLeans 17.7 13.4 13.4 15.6 Bay

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M250914_J100059_TP update 13

Total Phosphorus Concentration (g/L) Site Mean Mean 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (2005- (2000- 2014) 2014) Spence Lake - North 11.9 11.0 11.3 11.7 11.3 11.5 Spence Lake - South 9.7 7.1 5.2 7.4 6.0 6.2 7.1 Spring Lake 8.4 7.7 7.5 6.2 5.8 5.3 5.8 9.6 6.5 7.0 Stewart Lake 7.9 7.3 9.8 6.6 7.5 8.1 8.0 7.9 Stoneleigh Lake 15.5 11.6 11.6 13.0 12.1 12.1 12.8 Sunny Lake 6.9 7.8 8.2 6.3 7.4 7.3 Tackaberry Lake 6.2 5.8 5.3 4.1 5.1 5.4 Tadenac Lake 7.8 6.5 8.3 5.1 6.6 6.9 Tasso Lake 4.9 4.6 9.3 5.1 12.5 6.2 3.4 7.3 6.6 Thinn Lake 9.3 9.1 11.7 11.0 10.0 10.9 10.2 Three Mile Lake - Hammels 14.5 14.7 13.0 15.0 11.4 11.0 12.0 12.5 13.1 Bay Three Mile Lake - Main 25.1 22.5 24.7 21.3 26.6 18.5 16.8 20.8 22.2 Three Mile Lake GR 15.3 6.8 7.0 18.6 9.4 10.5 11.4 Tooke Lake 6.0 3.8 5.9 6.9 5.3 4.6 5.7 5.4 Toronto Lake 7.8 7.4 7.5 7.6 7.5 7.6 Tucker Lake 6.1 4.2 4.2 5.6 4.7 5.0 Turtle Lake 9.6 9.6 8.2 7.1 8.0 9.4 7.2 8.0 8.4 Walker Lake 5.6 8.5 5.8 4.9 3.9 4.9 5.8 5.1 5.6 Webster Lake 13.2 15.6 16.2 15.9 15.0 Weismuller Lake 20.4 11.5 23.1 16.0 7.2 15.4 15.6 Wildcat Lake 12.7 8.5 7.8 4.1 8.7 6.9 8.4 Wolfkin Lake 12.4 7.4 3.6 6.9 11.3 4.5 7.6 7.7 Wood Lake 9.2 6.2 7.3 6.0 7.2 6.2 6.6 7.0 Young Lake 10.8 15.0 6.8 5.3 5.5 7.1 7.9 8.4

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M250914_J100059_TP update 14

J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

Appendix D. Correspondence and Meeting Minutes

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3-1 Taylor Road, Bracebridge, ON P1L 1S6 ph: 705-645-0021 Meeting Agenda - Reconciling Lakeshore Capacity Models

Date: January 21, 2013, 1000-1500

Location: District of Muskoka – Oak Boardroom, Bracebridge

Present: DMM - Judi Brouse (DMM), Neil Hutchinson, Dörte Koster, Tammy Karst-Riddoch (HESL) MOE – Andrew Paterson, Eleanor Stainsby, Ed Snucins, Victor Castro

City of Greater Sudbury – Stephen Monet, Lana Haslam (via phone)

Purpose of Meeting

To discuss joint experience with calibration and performance of MOE Lakecap Model. To discuss ways to improve predictive performance. To reconcile model accuracy with management and planning needs.

1. Introductions – Judi a. Changes to agenda ? 2. Current status of municipal model needs – Judi and Stephen 3. Current status of model implementation – MOE 4. Discussion – Problems and successes with model accuracy - DMM example – Neil - Sudbury example – Tammy - MOE Watershed examples – Andrew, Ed, Victor, Eleanor 5. Troubleshooting scenarios run in response to January 11 meeting with Andrew Paterson – Neil and Tammy - Patterns vs scatter a. Discussion 6. Discussion – implications of model error and methods to reconcile in lake management a. The need for a reliable threshold b. The “line in the sand” vs the “broad ribbon” c. Lake sensitivity approach d. Focus on available shoreline e. Calibration vs loading focus – do we really need an accurate model to manage lakes ? i. Focus management on those aspects of the model that are trusted and validated 7. MOE Review Process 8. Next steps.

MM210113_J100059_Lakecap_agenda.docx

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3-1 Taylor Road, Bracebridge, ON P1L 1S6 ph: 705-645-0021 Memorandum

Date: February 20, 2013

To: Judi Brouse – Director of Watershed Programs – District Municipality of Muskoka.

Cc: Andrew Paterson, Eleanor Stainsby, Victor Cairns, Ed Snucins – Ontario Ministry of the Environment. (in attendance) Stephen Monet - City of Greater Sudbury (via telephone) Tammy Karst-Riddoch, Dörte Köster – HESL (in attendance)

From: Neil Hutchinson – HESL

Re: Summary of January 21, 2013 Meeting – Reconciling Accuracy of “Lakecap” Predictions

Please review the attached summary of our January 21 meeting. I have summarized the discussions from that day. I am now preparing some follow up analysis that I have completed since then and will present several approaches and my recommendation to the District of Muskoka for how to move forward in a subsequent memo.

Purpose of Meeting To discuss joint experience with calibration and performance of MOE Lakecap Model. To discuss ways to improve predictive performance. To reconcile model accuracy with management and planning needs.

Current status of municipal model implementation

Judi Brouse explained the recent history of implementation of the DMM ―Lake System Health‖ program and its history of implementation in Muskoka. The bottom line for the DMM is implementation of a recreational water quality program that is defensible at the OMB. Any program of the DMM, or another municipality is vulnerable if it is not defensible. The DMM program is based on Ontario case law and proceeds with the expectation that a property owner can develop their property to some extent.

The 2005 ―Lakecap‖ program was developed using the approach of lake sensitivity classification and a threshold of BG+50% because the Lakecap model (or the DMM variant) did not provide resolution fine enough to defend limiting single lot severances or setting lake capacities on the basis of the BG+50% threshold only – the model did not support a ―line in the sand‖. Nevertheless, any lake management program needs to be guided by knowledge of what a development threshold might be – where do you say ―no‖ and on what basis do you determine the limit, as water quality may not be the most sensitive determinant.

The DMM program has very restrictive policies on development but does not say ―no‖ to development on its lakes1. Except for lakes with low sensitivity, all shoreline development is guided by site plan control –

1 Development on lakes which are highly sensitive and over capacity (> BG+50% for measured and modeled TP) can only be developed where there is municipal sewage service or septic systems can be set back 300m. For much of Muskoka, this is an effective “no” to additional development.

M21022013-J100059-MOELAkecapMeeting.docx a 30m setback for septic systems (vs 15m OBC standard) and minimal removal of vegetation. Development on high sensitivity lakes is guided by a site-specific impact study to demonstrate how the development can proceed with minimal impact. DMM controls apply to existing development as well – minor variances or applications for building permits (i.e to build a deck) can trigger a requirement for the landowner to improve conditions on the property.

The DMM are currently reviewing the Lake System Health Program (model and policies) as part of their 10 year OP Review. They recognize that planning policies are best implemented in a stable planning context where residents understand the rules and the DMM is confident their policies would be supported at the OMB. The stability aspect comes into consideration when lakes change from over threshold to under threshold or vice versa – this can occur on the basis of a change to the model and so they need confidence in the model so that threshold and sensitivity classifications are defensible, as the public will challenge a change in classification.

Discussion Andrew Paterson asked if the DMM had ever been challenged at the OMB. Judi Brouse – The DMM has been challenged on the basis of phosphorus abatement but were successful. Andrew Paterson asked if the DMM handle applications for single and multiple severances the same way? Judi Brouse – Yes – the process may differ (consent vs plan of subdivision) but the policies are the same. Victor Castro – Does the DMM recognize phosphorus abatement in septic systems? Judi Brouse All systems must be Class 4 to start, then we recognize phosphorus attenuation in soils with <1% CaCo3 and high mineral content. On existing lots, we recognize attenuation as a function of enhanced setback and presence of soils. On new lots, we recognize this plus BMPs (e.g. for stormwater management)

The DMM Lake System Health program is based on total phosphorus but that is only one aspect of the total program. The DMM: encourages associations to develop lake plans to protect specific values and provides for Area Municipalities to implement these recommendations related to land use provided they have a defensible rationale. policy provides for minimum frontages (200’/60m) and lot sizes (1 acre/0.4 ha) and will limit alteration/development on the basis of fish habitat, wetlands and steep slopes.

Victor Castro – the DMM program appears to work because there is no ―line in the sand‖ to defend.

Stephen Monet described the development of the lake management program for the City of Greater Sudbury on the basis of Muskoka’s experience but with awareness of the differences in the Sudbury environment such as loss of soil, heavy industry impacts, lakes with heavy urbanization and non-Shield areas such as ―The Valley‖, The increasing sophistication and activity of lake stewardship groups must be addressed but the lake management tools must have a sound basis in science and policy and not simply react to public pressure. As with the DMM, the program must be defensible at the OMB.

Current status of MOE model implementation

Andrew Paterson described the implementation of ―Lakecap‖ via the 2010 handbook and the province’s adoption of BG+50% as a threshold for Shield lakes and the 7 mg/L VWHDO criterion for lake trout habitat. The most recent development was an OMB decision for Limerick Lake, an ―over capacity‖ lake trout lake which recognized the potential for abatement by mineral rich soils and allowed 8 lots, with a condition of three years of intensive monitoring of phosphorus in the tile fields. If the monitoring program

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M21022013-J100059-MOELAkecapMeeting.docx 2 demonstrated successful abatement then additional lots would be allowed. The MOE will audit the monitoring program.

Victor Castro stated that Eastern Region MOE continue to apply the 2010 handbook approach and assumptions regarding phosphorus mobility and had been ―relatively successful‖ with OMB challenges, although the only challenges have been for lake trout lakes. They recognize the evolution of understanding regarding phosphorus attenuation in soils and are trying to increase the body of knowledge on abatement as there is lots of interest in development on ―at capacity‖ lakes. At Limerick Lake, the feeling was that the science was strong enough and the risk low enough to allow testing of the technology on an ―at-capacity‖ lake trout lake. The Branson test case on Kushog Lake and the published work of Dr. Will Robertson provide MOE with enough confidence to consider pilot studies and the feeling was that three test cases should provide enough confidence to consider generic applications of a specific abatement technique. MOE recognize that non-capacity lakes are the ideal sites for pilot studies of abatement and are looking for other sites (The ongoing program in the City of Elliot Lake was recognized as a potential for this). More recently, several ―black box‖ abatement technologies have been producing promising results, and the MOE favour contained and confined treatment technologies as they can be verified. The MOE’s Standards Development Branch is reviewing these technologies.

Implementation of Lakecap within MOE Regions

Ed Snucins stated that the Northern Region approach was guided by the 2010 Lakecap handbook.

Victor Castro stated that ―Lakecap‖ is not a policy, it is a guidance tool that, if used according to the handbook, is considered to meet the intent of the Provincial Policy Statement. If other approaches are used, then the users must be prepared to defend them to the MOE. The biggest concern or challenge would likely come where a non Lakecap approach was used for a lake trout lake.

Judi Brouse stated that the DMM implements their program for recreational water quality, consults with MNR for lake trout lakes and relies on the MNR to implement and defend provincial policies on lake trout lakes.

Andrew Paterson said that Central Region MOE generally have no issues with the DMM approach, but defer to the Dorset Environmental Science Centre for interpretation. MOE recognizes that the DMM and the City of Greater Sudbury (CGS) have the resources and ability to manage lakes by their own programs and have no concerns with municipalities leading by good examples, but other jurisdictions and un- organized areas have less capacity to implement their own program. DMM and CGS have local government to implement specific BMPs and practices and so are ―atypical‖ northern Ontario municipalities. MOE envisioned ―Lakecap‖ as a tool to allow downloading of recreational water quality management to municipalities but it has not transpired that way, they do not expect municipalities to take it on unaided and there has not been a lot of municipal uptake on Lakecap training.

Discussion – Problems and successes with model accuracy

Neil Hutchinson reviewed the status of the revision of the DMM Model. In summary, the model provides a large range of error around comparisons of measured TP to modelled estimates of TP. The model has been set up using MOE coefficients and assumptions from the 2010 handbook and did not assume any attenuation of phosphorus from watershed soils, as it had in the 2005 version. Neil had met with Andrew Paterson previously and had run a series of diagnostics and checks on potential sources of model error. The results showed continued error of estimates, with no bias, and no consistent relationship of error to any of the following:

Degree of assumed phosphorus loading from shoreline development. Model error was unacceptable in undeveloped lakes, lakes with <10% anthropogenic load and lakes with

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higher anthropogenic loads. Thus the error was not related to assumptions regarding anthropogenic phosphorus loading and appeared to be more a function of how the model converted loadings to concentrations. Oxic vs anoxic status. Model error was independent of the assumptions related to hypolimnetic oxygen status and settling velocity (~internal load). The model assumes oxic conditions for the ~ 340 lakes for which there are no data on oxygen status. The error was greater for lakes known to be anoxic and so assuming that a greater portion of known lakes were anoxic instead of oxic would likely increase model error. Amount of wetland in the watershed – Model error was independent of the amount of wetland in the watershed. Headwater vs lower order lakes - Model error was similar in headwater lakes and lakes with one upstream lake, and did not change with location downstream in a watershed. Areal water load – Depth of runoff had increased by ~ 25% between the 2005 and 2012 model versions as a result of better resolution of runoff depths from MOE. The model error was not related to runoff depth. Size of lake or size of watershed – there was no relationship of error with the size of the lake and watershed modelled.

Neil Hutchinson also presented a comparison of lake area, watershed area and wetland area from the old model to the new model. It was clear that these estimates had changed and, aside from new definitions of wetland, there was no clear reason for the changes.

The conclusion of the presentation was that the model accuracy, and error in input parameters, clearly did not support use of the model to set lake development capacities. Specific thresholds could not be defended on the basis of model accuracy.

Tammy Karst-Riddoch presented a summary of her calibration efforts with the CGS model. The model contains 354 lakes, 64 calibration lakes (lakes with reliable measured data) and 23 headwater lakes. She had found many of the same problems presented for the DMM model, but with slightly larger errors and a tendency to over predict phosphorus concentrations. Potential sources of error of the greatest concern were related to the history of acidification, and its impact on export coefficients and the expression of loadings as concentrations in the lakes, but the large number of riverine lakes was also noted. There was no over-riding or systematic source of error and it was felt that a lake-by-lake examination, and lake- specific adjustment, may be required, but there was no strong rationale to support such an approach. The 2010 Lakecap handbook advises that, where the model produces unreliable results that the PWQO of 10/20 ug/L of phosphorus should be used as a threshold. This approach gets around the use of BG+50% but does not provide the necessary confidence in the model to predict or defend the responses of lakes to development.

Andrew Paterson stated that the handbook allows the use of good local information (such as usage factors) but others reminded that this is not possible with a large set of lakes. Other possible factors included the larger number of eutrophic lakes in Sudbury (TP > 20 ug/L) and the poorer relationship between TP measured at spring overturn and TP modelled as an ice free mean in eutrophic lakes.

Eleanor Stainsby reported that she had not modelled a lot of lakes but had mixed results with the ones she had modelled.

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Victor Castro reported similar results of large errors in model estimates in Eastern Region. Modelling lakes in the Algonquin Land Claim was hampered by a lack of measured TP, lack of data on oxygen status and poor data on existing development. Some lakes on the edge of the Shield did model well but results were overall mixed. Victor also reported some scepticism when the model does produce good results, because of the evidence showing poor mobility of septic system phosphorus. Andrew expressed some concerns with the settling velocity component of the prediction.

Ed Snucins reported that he had not had a lot of direct experience modelling lakes but that Todd Kondrat (Northern Region-Thunder Bay) had mentioned issues with model scatter.

Overall, there was agreement that the model performance was not ideal, that Dissolved Organic Carbon was not the likely unknown factor to consider and that there was no one factor or source of error, but that errors appeared to be lake-specific.

Stephen Monet wondered if some error could be attributed to the change in formulation of lawn fertilizers to low-phosphorus since 2005 in response to higher phosphorus prices but Andrew Paterson noted that the coefficients for cleared areas in the model did not include a fertilizer contribution.

General Discussion

The discussion began with the question of ―What are we concerned about?‖ or ―What are we trying to manage?‖ If anthropogenic phosphorus loading can be prevented by technology, are the other planning considerations going to protect lake attributes? Victor Castro expressed support for the DMM approach of ―Limits to Growth Analysis‖ where wetlands and natural features are brought into the capacity considerations.

If water quality can be managed and natural features protected then community social standards could be considered but it was noted that, even with ―black box‖ abatement for septic systems, a lake may still be sensitive to other sources. It was suggested that full BMPs should be the standard for all lakes but this was countered with the differing financial resources between lakes (some lakes support more expensive lots than others) and the fact that the model did not show a good correlation between loading and concentrations in lakes.

Moving the threshold to a loading basis (e.g. BG+50% load) plus lake responsiveness instead of a concentration basis would remove some concerns but there would still be the argument about one more lot when threshold was reached. Further discussion centred on consideration of loading sensitivity alone and development of first principles of lake sensitivity (This is the basis of the original Vollenweider equation for the Great Lakes – areal phosphorus load and depth of runoff that was refined into the Dillon—Rigler and Lakecap Models (Back To The Future!)).

Summary points:

need to distinguish approaches for DMM, CGS and other municipalities need to consider basic planning principles (implementation of BMPs will provide adequate protection in most cases) need to maintain the distinction of lake trout lakes need to focus management on those aspects of the model that are trusted the Lakecap Handbook is a guidance document and MOE will allow municipalities to develop and defend their own approaches in response to local needs MOE will manage implementation at the Provincial level.

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3-1 Taylor Road, Bracebridge, ON P1L 1S6 ph: 705-645-0021 Memorandum

Date: February 26, 2013

To: Judi Brouse – Director of Watershed Programs – District Municipality of Muskoka.

Cc: Margaret French – Commissioner of Planning - District Municipality of Muskoka.

Derrick Hammond, Samantha Hastings

From: Neil Hutchinson – HESL

Re: Revised Lake System Health Model - Summary of performance and recommendations

The revisions to the Lake System Health model were completed in November of 2012, according to the original work plan. The model has been updated with the most recent data and now includes lakes >8ha and more detail on certain portions of the Muskoka River watershed, most notably refinements to the Oxtongue River subwatershed. All lake, watershed and wetland areas have been updated with data provided by the DMM GIS group. Development counts and types have been updated on the basis of MPAC data provided by the DMM GIS group. The model has been revised to use the MOE (2010) coefficients for atmospheric deposition, wetland phosphorus export and other model coefficients.

Although the model still retains the ability to incorporate attenuation of septic system phosphorus where soils are suitable (as in the 2005 version) and the evidence that septic systems do attenuate phosphorus has only gotten stronger in the intervening years, we have prepared the final version of the model with the attenuation turned off. That is, the model does not account for attenuation in the current version, in line with MOE (2010) guidance. The results provided below show that this is not a significant source of model error. The model has retained the so-called “doughnut” approach, in which septic phosphorus is assumed mobile within 300m of the lake shore, but is reduced by one third with every 100m setback from the shoreline. This accounts for some attenuation of septic phosphorus by soils with distance but makes little difference to model performance, as 90% of all development in Muskoka is located within 100m of the waterfront (Table 1).

Table 1. Development with distance from shoreline in Muskoka.

Number Percentage 0-100m 27979 90

100-200m 1798 6 200-300m 1144 4

Total 30921 100

M26022013-J100059-DMMmeeting.docx Model Performance

Overall, the accuracy of the revised Muskoka Lake System Health Model does not support its use in setting defensible development capacities. Figure 1 shows significant scatter in the relationship between measured and modelled estimates of total phosphorus for the 206 “calibration” lakes for which measured data exist. The model tended to overestimate phosphorus concentrations in Muskoka lakes. The mean and median positive errors (overestimate) were 53% and 38% and the mean and median negative errors (underestimate) were 25 and 23% (Table 2). Error exceeded 20% in 69% of the lakes and exceeded 40% in 39% of lakes.

Figure 1. Accuracy of revised Muskoka water quality model. Dotted lines enclose +/-20% about the 1:1 line.

Accuracy of Revised Muskoka Water Quality Model (2012) 25

g/L m 15

Modelled TP in TP Modelled 5

0 0 5 10 15 20 25 Measured TP in mg/L

Table 2. Percentage error of revised model.

+ Error - Error Mean Error (%) 52.7 -25.0 Median Error (%) 38.4 -22.9

N = 134 72 > 20% Error 100 42 >40% Error 64 17

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Although the model had a greater tendency to over predict than to underpredict 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”, which is the ratio of total potential phosphorus load over natural phosphorus load, such that a value of 1.5 = Background + 50%). Although there was a greater tendency to lower negative error and higher positive error as the potential septic phosphorus load increased (Figure 2), the percentage error ranged from -60 to 100% in undeveloped lakes (Figure 3).

Figure 2. Model error compared to potential phosphorus load from development, all lakes.

Accuracy of Revised Muskoka Water Quality Model All Data 100 75

50 25 0

-25

-50

-75

1.0 1.5 2.0 2.5 3.0 3.5 4.0 % Error Modelled)(Measured vs Error %

Development Index

Figure 3. Model error on lakes with <10% potential development phosphorus load.

Accuracy of Revised Muskoka Water Quality Model Undeveloped Lakes

100

-20

-40

-60 % Error Modelled)(Measured vs Error % -80 1.0 1.1 1.1

Development Index

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The model performance is best tested for those lakes which are undeveloped, as the total phosphorus concentrations for these lakes are based on the fewest assumptions regarding potential phosphorus sources. Phosphorus loads for undeveloped lakes are based on a) measured regional estimates of atmospheric phosphorus load from MOE’s Dorset Environmental Science Centre 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 MOE (Paterson et al 2005). As development is added to a lake, the estimate of total phosphorus loading to the lake becomes increasingly uncertain because of the uncertainty in assumed mobility of phosphorus from septic systems.

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 3). Increasing the sample size to include lakes in which 6% of the total phosphorus load was from development showed the same general trend of median error ranging from -39% to 22%. It is clear that model error is not a function of assumptions regarding mobility of phosphorus from septic systems.

Table 3. Percentage error of phosphorus concentrations in lakes with little development.

Percentage Error of Measured vs Modelled TP + - + - + - + - All Lakes D.I. <1.06 D.I. < 1.03 D.I. = 0 Mean 52.7 -24.4 27.6 -36.5 28.6 -34.8 34.2 -34.0 Median 38.4 -22.6 22.3 -38.5 22.7 -38.5 26.7 -35.2 n= 134 72 14 15 5 9 3 6 >20% 100 42 9 14 3 8 1 3

Conclusion– Model error is not systematically related to estimates of phosphorus loading from shoreline development.

Potential Sources of Model Error – Wetlands and DOC

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. Model error was high for undeveloped lakes and developed lakes. The analyses therefore focussed on potential sources of error for undeveloped lakes, as the absence of development meant that human phosphorus sources need not be considered as a source of uncertainty. There are two sources of natural phosphorus load– atmospheric and overland runoff and the latter is best related to the percentage of wetland in the watershed of a lake (Dillon and Molot, 1997; Paterson et al, 2005)

Atmospheric phosphorus loading was not considered as a source of error as it is based on measured and published values for Muskoka (Paterson et al 2005). The estimate of wetland in the watershed of a lake was investigated as a potential source of error as estimates were revised between the 2005 and 2012 models. The new estimates (as provided from DMM GIS) were substantially different from the previous estimates. The major source of the difference was a revised method of classifying wetlands between 2005 and 2012 and better resolution of the GIS methodology. The 2012 estimates of wetland areas for 33 lakes in the Dwight subwatershed, for example, provided overestimates averaging 54% (23.6 km2) and underestimates averaging 91% (12.9 km2) (Figure 4). The new values differ from the previous values but are considered the most up to date and reliable.

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Figure 4. Comparison of 2005 and 2012 estimates of wetland areas for individual lakes in the Dwight subwatershed.

Wetland Area-Dwight Wetland Area-Dwight 120 40 110 36 100 32 90 2 Ave. + Difference = 23.6 km , 54.4 % 28 80 2 70 Ave. - Difference = 12.9 km , 90.6% 24 60 20 50 16

40 12 Old Model Model Old km2 Old Model Model Old km2 30 20 8 10 4 0 0 0 10 20 30 40 50 60 70 80 90 100 110 120 0 4 8 12 16 20 24 28 32 36 40

New Model km2 New Model km2

Model error was not related, however, to the amount of wetland in the watershed (Figure 5) nor to the concentration of Dissolved Organic Carbon (DOC) in the lake (Figure 6). Natural export of phosphorus from wetlands is tied to export of DOC as both are related to breakdown of vegetation in wetlands. Therefore, although the concentration of phosphorus in Muskoka lakes is related to DOC (Figure 7), the error in model predictions of phosphorus is independent of DOC, and therefore more likely related to the conversion of phosphorus load to in- lake concentration than to the estimate of natural loading of phosphorus from wetlands as a function of DOC.

Figure 5. Model error as a function of wetland area.

400 350 300 250 200 150 100 y = -0.0061x + 25.575 R² = 5E-07

Error in in Percent Error 50 0 -50 0 10 20 30 40 50 -100

Wetland in Percent

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Figure 6. Relationship of model error to DOC in Muskoka lakes.

250

200

150

100

Model ErrorModel % in 0 0 2 4 6 8 10 12 14 -50

-100 DOC in mg/L

Figure 7. Relationship of DOC and TP in Muskoka lakes.

TP-DOC Relationship in 193 Muskoka Lakes

30.0

25.0

20.0 y = 1.3715x + 2.3406 R² = 0.4552

15.0

10.0 Total PhosphorusTotal (ug/L)

5.0

0.0 0 2 4 6 8 10 12 14

Dissolved Organic Carbon (mg/L)

Conclusion– Model error is not systematically related to DOC or wetland estimates.

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Potential Sources of Model Error –Hydrology Estimates

The conversion of phosphorus loadings to phosphorus concentration in a lake is dependent on the hydrology of the lake, which is expressed as the areal water load in m/yr, or the total depth of runoff from the watershed (in m3/yr) applied to the surface area of the lake (in m2). The depth of runoff for the 2005 version of the model was calculated using average annual depth off runiff form the Canadaian Water (1975). In the interim, the MOE refined these estimates and the new estimates were used as input to the 2012 model. The refined estimates were, on average 29% higher than the original estimates. Higher runoff should, in theory, lead to lower estimates of phosphorus concentrations in lakes but the change in runoff estimate did not lead to a systematic error in model estimates of TP concentration.

Model error was not related to areal water load (Figure 8).

Figure 8. Relationship of model error to areal water load.

400 400 350 350 300 300 250 y = -0.0006x + 25.571 250 y = -0.0006x + 25.571 200 R² = 2E-05 200 R² = 2E-05 150 150

100 100 Error in Percentin Error

50 Percentin Error 50 0 0 -50 0 50 100 150 200 -50 0 10 20 30 40 50 -100 -100 Areal Water Load in m Areal Water Load in m

Watershed Function

Ontario’s original Lakeshore Capacity Model (Dillon et al 1986) and the MOE Lakecap Model (MOE 2010) were developed from calibrated headwater lakes. Although MOE’s Lakecap (2010) guidance manual rightfully advises that any lake modelling effort be done in a watershed context, 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 mall ratios of watershed area to lake area (i.e. for headwater lakes) also apply to all lakes in a watershed. The Muskoka model challenges the assumptions used for calibration of the Lakecap Model, as it includes large lakes, large watershed areas and many lakes besides headwater lakes. If the assumptions used to calibrate the MOE Lakecap lakes 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/lake area (Figure 9), beyond a tendency to underpredict for small watershed areas (ratios < 50:1, Figure 9). Model error was not systematically related to headwater position off a lake in a watershed (headwater lakes or lakes with 1 upstream lake) vs non-headwater lakes (lakes with > 1 upstream lake). 122 of 209 lakes were considered to be headwater lakes and 87 lakes were non-headwater lakes. The degree of model overestimate

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decreased for non-headwater lakes but the model tended to underestimate for non-headwater lakes (Table 4). Figure 9. Relationship of model error to ratio of watershed area/lake area.

250 60

200 40 150 20 100 0

50 0 20 40 60 80 100 Percent Error

Percent Percent Error -20 0 0 100 200 300 400 500 600 -50 -40

-100 -60 Watershed Area/Lake Area Watershed Area/Lake Area

Table 4. Relationship of model error to watershed position of lake.

Headwater Lakes Non-Headwater Lakes 0 or 1 upstream lake > 1 upstream lake

+error (%) -error (%) +error (%) -error (%)

N= 79 43 58 29 Average 63.2 -25.0 38.4 -26.5 Median 44.7 -20.3 33.2 -27.1

Model error was not related to lake mean depth (Figure 10, left), or maximum depth (Figure 10, right) for those lakes where depth was known.

Figure 10. Relationship of model error to lake mean (left) and maximum (right) depth.

250 400 350 200 300 y = -0.2493x + 25.251 150 250 R² = 0.0005 100 200 y = -0.1255x + 31.337 150 R² = 0.0011

50 100 Percent Error 0 in PercentError 50 0 5 10 15 20 25 30 0 -50 -50 0 10 20 30 40 50 60 -100 -100 Lake Depth (m) Maximum Depth (m)

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Model error showed no systematic relationship with the ratio of watershed area/lake area

Model error was not systematically related to headwater position off a lake in a watershed

Model error was not related to lake mean depth or maximum depth for those lakes where depth was known.

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. The equation used to determine phosphorus concentration in a lake includes a term for “retention” of phosphorus in the sediments – where phosphorus that is retained is not expressed as a water borne concentration. The equation is:

(7.2/(7.2+qs)) for lakes that have an anoxic hypolimnion, and

(12.4/(12.4+qs)) for lakes that maintain an oxygenated hypolimnion, where

12.4 and 7.2 are values in m/yr for the “settling velocity”, which describes how fast phosphorus settles to the sediment, and

qs is the areal water load to the surface of the lake – an index of how fast water in the lake is replaced.

The values for settling velocity are averages taken from a large set of studies and used by the MOE (Dillon et al. 1986) in their Lakecap Model to describe all stratified lakes on the Precambrian Shield. The smaller value of 7.2 is used for anoxic lakes as a smaller “settling velocity”, which is a surrogate for internal loading of phosphorus from lake sediments in anoxic lakes. In practice, lakes with an anoxic hypolimnion and the smaller settling velocity have a higher modelled concentration of total phosphorus for a given load.

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 monitor oxygen status in 205 of the lakes in the model. Any lakes in which the oxygen status is unknown are assumed to oxic. Section *** 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 unknown lakes have oxic hypolimnia. Table 5. Relationship of model error to hypolimnetic oxygen status.

Anoxic Anoxic Oxic Oxic Hypolimnion Hypolimnion Hypolimnion Hypolimnion + Error in % - Error in % + Error in % - Error in % N= 50 21 83 51 Average 66.10 -30.20 44.84 -24.56 Median 49.03 -30.93 35.03 -20.11

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The model overestimated phosphorus concentrations more often than it underestimated, and by a greater error percentage, regardless of oxygen status (Table 5). The magnitude of the error, whether positive or negative, was higher in lakes with anoxic than with oxic hypolimnia. ~300 of the lakes in the model are assumed to have oxic hypolimnia. Although some of these may, in fact, be anoxic, that cannot be confirmed for the model. The analysis in Table 5 shows, however, that, if some of the 300 lakes were anoxic, the error would not be improved.

Model error was not systematically related to oxygen status of the lakes.

Other Sources of Error

Section **, above, showed the differences in the estimates of wetland area between the 2005 and 2012 models, using the lakes in the Dwight subwatershed as an example. All model inputs were reviewed for 2012, and Figures 11 and 12 show that there were also differences in lake areas and watershed areas between the two versions. This variance is difficult to explain but is most likely related to the use of GIS to determine these areas on 2012, Older versions had used manual techniques such as planimetry or “dot counts” to estimate areas.

Figure 11. Difference in lake areas between 2005 and 2012 models.

Lake Area-Dwight Lake Area-Dwight 2.50 0.50 Ave. + Difference = 0.01 km2, 3.3% 2.00 Ave. - % Difference = 0.01 km2, 0.40

1.50 0.30

1.00 0.20 Old Old Model km2 0.50 Model Old km2 0.10

0.00 0.00 0.00 0.50 1.00 1.50 2.00 2.50 0.00 0.10 0.20 0.30 0.40 0.50

New Model km2 New Model km2

Figure 12.Difference in watershed areas between 2005 and 2012 models.

Watershed Area-Dwight Watershed Area-Dwight 45 6 40 Ave. + Difference = 0.03 km2, 1.06 % 5 35 Ave. - Difference = 0.29 km2, 35.02 % 30 4 25 3 20

15 2 Old Model Model Old km2 Old Model Model Old km2 10 1 5 0 0 0 5 10 15 20 25 30 35 40 45 0 1 2 3 4 5 6

New Model km2 New Model km2

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Summary

The attempts to improve the fit of modelled to measured estimates of phosphorus concentrations, shows that the model accuracy, and error in input parameters, clearly do not support use of the model to set lake development capacities. Specific thresholds could not be defended on the basis of model accuracy.

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. 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 DMM program, such as the requirements for BMPs, the Development Permit system and minimum lot frontages and sizes also act to protect water quality.

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Resolution - Recommended Approach to Lake System Health

Any program of recreational water quality management that depends on modelled estimates of phosphorus concentration to set “development capacities” will be easily challenged. The model simply does not provide defensible capacity figures, and the evidence against mobility of septic system phosphorus in the Shield environment has only gotten stronger in the past ten years.

The 2005 Lake System Health Classification is based on three lines of evidence:

1. Use of the model to estimate lake sensitivity: a. a standard areal load of phosphorus (corresponding to 1 cottage/1.6ha of lake surface) is applied and lake “responsiveness” assessed on the basis of the percentage change in modelled phosphorus concentration. If the standard areal load increases phosphorus concentration in a lake by >80% then the lake is considered to be “Highly Responsive” to shoreline development. The model is not, and need not be, calibrated to measured phosphorus concentration for this metric. b. The modelled prediction of present day phosphorus concentration is compared to the measured concentration. Phosphorus “mobility” is determined on the basis of how much of the predicted concentration is being expressed as measured phosphorus. This metric depends on the model to predict phosphorus in the lake. c. The lake is divided into three classes of sensitivity based on a 3*2 matrix of responsiveness and mobility. 2. Use of the model to set a phosphorus threshold a. The model is used to estimate the “background” phosphorus concentration (in the absence of shoreline development), the threshold concentration of “Background+50%” and the current day phosphorus concentrations. All three of these metrics depend on the ability of the model to produce accurate estimates of phosphorus concentration and so are not defensible. 3. Comparison of modelled to measured phosphorus concentrations. a. At present, a lake is considered to be “over threshold” if both the measured and modelled phosphorus concentrations exceed “Background + 50%”. This comparison depends on the ability of the model to produce accurate estimates of phosphorus concentration and so is not defensible.

It is important that the Lake System Health Program remain defensible. The ability to assess lake sensitivity is defensible but setting and defending thresholds is not, as the model provides too large an error between measured and modelled estimates of phosphorus concentration. Nevertheless, the Lake System Health Program 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 is available or alternate approaches are available (such as incorporation of phosphorus abatement into the Ontario Building Code for septic systems).

The Lake System Health Program is managing “recreational water quality” through phosphorus loadings to lakes to protect the aesthetics of water clarity for Muskoka residents and lake users. “Aesthetics” are subjective values and will vary with the user. Although the Lake System Health program protects the

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M26022013-J100059-DMMmeeting.docx 12 aesthetic value of water clarity, Dissolved Organic Carbon has a greater impact on water clarity in Muskoka than total phosphorus.

Some form of modelling is necessary to predict the response of lakes in Muskoka to shoreline development. The Lake System Health Program should be built around those components of the program in which we have higher confidence. What components of the model provide confidence and are defensible?

Component Confidence

Moderate Lake, watershed and wetland areas Low – comparison of 1997 vs 2012 estimates High– these things should not change, “we have it correct now”. Natural Atmospheric Load High confidence measured data from MOE Moderate Confidence Measured and published relationship from MOE – High Natural Load from Wetland Wetland areas from GIS – Moderate Relationship of DMM to MOE wetland definition/classification – moderate Moderate Confidence Depth of Runoff Data from long term monitoring programs but are regional and not lake specific Settling Velocity Low Confidence Two values (oxic and anoxic) used for all lakes. Predicted Background and Low Confidence Background + 50% Concentrations Figure 3, Table 3

Moderate confidence Anthropogenic Load to septic system Based on measured water usage and effluent phosphorus concentrations but data are old. Low confidence Anthropogenic Load to lake –septics Published studies of W. Robertson Increasing support of MOE and OMB Moderate confidence Anthropogenic Load to lake –runoff A known component, export coefficients are estimates only Moderate confidence Three values, 205 lakes. Usage Factor Not updated in 20 years. Known site-specific errors. Low confidence Predicted present day concentration Figure 1, Figure 2, Table 2 Measured present Day Concentration High confidence

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Predicting Background or Background + 50% Phosphorus Concentration in lakes

There is good confidence in the ability of the model to predict lake sensitivity. There is low confidence in the ability of the model to accurately predict phosphorus concentrations.

The Lake System Health Program should not place a lot of planning weight on modelled predictions of “Background”, “Background+50%” or “Present Day” phosphorus concentrations and should, instead, focus on those elements for which we have moderate and high confidence.

Our discussions with MOE have determined that they share the concerns and problems identified in the current review.

The problem appears to lie in the ability of the model to convert loading, whether natural or anthropogenic, into concentrations. The basic assumption that septic system phosphorus is mobile, can also be credibly challenged. Past experience with the Lake System Health Program (J. Brouse, pers. comm.) is that residents accept the modelled results (or do not attempt to understand the model workings) but are very uncomfortable when the measurements of phosphorus exceed the modelled “Background + 50%” threshold, despite the inherent contradictions in trusting a measured value compared to an uncertain modelled threshold. The point of sensitivity would be the individual lake data sheets that show a clear line at “BG+50%”, based on the model, and measured data points that exceed that line.

A four step process is proposed to reconcile these uncertainties and the desire to maintain a threshold classification of some kind.

1. Calculate the natural “Background” phosphorus and the “Background + 50%” as loads.

2. Calculate the “Background + 50%” lake concentration and report the measured current 10-year mean phosphorus concentration.

1. If the measured lake concentration exceeds “Background + 50%” and the calculated load exceeds “Background + 50%” then the lake is considered to be “over threshold”.

i. This removes the present-day requirement that the modelled lake concentration exceed “Background + 50%” and allows determination of over threshold status on the basis of load, without the need (and attendant model error) to convert present day load to present day concentration.

2. If the present day load is < BG+50% and the measured concentration exceeds “BG+50%” then the lake is not over threshold as the model prediction is inaccurate – there is not enough theoretical load to cause the lake to exceed “BG+50%”.

3. If the present day load is > BG+50% and the measured concentration does not exceed “BG+50%” then the lake is not over threshold as the model prediction is inaccurate – attenuation or some other process is preventing the over threshold load from being expressed as over threshold water concentration.

4. Do not calculate lake “mobility” any longer, and base lake sensitivity only on the “responsiveness” - the theoretical sensitivity to a standard areal load.

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Summary

Criteria-Over Threshold and Highly Sensitive Lakes

Criterion 1: Load > BG+50%

Criterion 2: Measured [TP] > BG+50%

Criterion 3: Standard Areal Load increases [TP] by >80%

Interpretation

If 1 is true and 2 is not: Model is in error – The load says TP in the lake should be > BG+50% but it is not. There is attenuation of phosphorus between the septic bed and the lake or the model is not correctly translating load into concentration. If 2 is true and 1 is not: Model is in error – It is underpredicting phosphorus concentrations in the lake or has not accounted for background concentrations correctly. If 1 and 2 are both true then we have some confidence in the threshold value of “BG+50%” and that the lake likely exceeds the threshold, even if the lake does not model exactly. If neither 1 nor 2 are true then we can be reasonably certain that the lake is “

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3-1 Taylor Road, Bracebridge, ON P1L 1S6 ph: 705-645-0021 Memorandum

Date: March 20, 2013

To: Judi Brouse – Director of Watershed Programs – District Municipality of Muskoka.

Cc: Margaret French – Commissioner of Planning - District Municipality of Muskoka.

Derrick Hammond, Samantha Hastings

From: Neil Hutchinson – HESL

Re: Revised Lake System Health Porgram – Recommendations for revision

This memo summarizes the approach and rationale that we discussed at our meeting of February 26 following comments on my earlier draft report. I have maintained the summary of model confidence from the original memo and elements of the rationale, removed those portions of the memo that discussed model error and our approaches to understand the reasons for it and added the new approach that we considered on February 26.

Background

In 2005, Muskoka‟s Lake System Health Program model and policies were reviewed. At that time, two important factors were considered and incorporated into the revised program: The first was the recent research of Prof. Will Robertson and the MOE which showed that the model needed to account for the fact that soils attenuated the movement of phosphorus between septic systems and lakes, The second was an emerging concern that even small differences between phosphorus measurements in a lake and predictions from the water quality model meant that it could be difficult to defend specific thresholds of water quality (or “Lake Capacity”) as an absolute limit to shoreline development, as had been done in the past.

These concerns with model fit and accuracy 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.

We have now completed our revisions to the model. The 2012 model results continue to show that the model accuracy does not support its use to set lake development capacities, based on phosphorus concentration. Our review, and discussions with MOE, show that they share some of these concerns and suggest: a greater focus on the model‟s ability to assess sensitivity, less focus on the model to calculate “threshold” concentrations or specific estimates of capacity and

M20032013-J100059-DMMmeeting+newapproach-final.docx continued reliance on the DMM‟s excellent record of measured phosphorus concentrations in their recreational lakes

These elements of the program are easily understood by the public, provide the necessary defensibility and rigour to the Lake System Health Program and will maintain the high level of protection of Muskoka‟s recreational water quality. In addition, the other aspects of the DMM program, such as the requirements for BMPs, the Development Permit system and minimum lot frontages and sizes also act to protect water quality.

Resolution - Recommended Approach to Lake System Health

Any program of recreational water quality management that depends on modelled estimates of phosphorus concentration to set “development capacities” can be easily challenged. The model simply does not provide defensible capacity figures, and the evidence against mobility of septic system phosphorus in the Shield environment has only gotten stronger in the past ten years.

The 2005 Lake System Health Classification was based on three components:

1. Use of the model to estimate lake sensitivity: a. a standard areal load of phosphorus (corresponding to 1 cottage/1.6ha of lake surface) is applied and lake “responsiveness” assessed on the basis of the percentage change in modelled phosphorus concentration. If the standard areal load increases phosphorus concentration in a lake by >80% then the lake is considered to be “Highly Responsive” to shoreline development. The model is not, and need not be, calibrated to measured phosphorus concentration for this metric. b. The modelled prediction of present day phosphorus concentration is compared to the measured concentration. Phosphorus “mobility” is determined on the basis of how much of the predicted concentration is being expressed as measured phosphorus. This metric depends on the model to predict phosphorus in the lake. c. The lake is divided into three classes of sensitivity based on a 3*2 matrix of responsiveness and mobility. 2. Use of the model to set a phosphorus threshold a. The model is used to estimate the “background” phosphorus concentration (in the absence of shoreline development), the threshold concentration of “Background+50%” and the current day phosphorus concentrations. All three of these metrics depend on the ability of the model to produce accurate estimates of phosphorus concentration and so are not highly defensible. 3. Comparison of modelled to measured phosphorus concentrations. a. At present, a lake is considered to be “over threshold” if both the measured and modelled phosphorus concentrations exceed “Background + 50%”. This comparison depends on the ability of the model to produce accurate estimates of phosphorus concentration and so is not highly defensible.

It is important that the Lake System Health Program remain defensible. The model can assess lake sensitivity with a high degree of confidence but the confidence in setting and defending thresholds and capacities is lower, as the model provides too large an error between measured and modelled estimates

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M20032013-J100059-DMMmeeting+newapproach-final.docx 2 of phosphorus concentration. In the absence of an explicit development capacity derived from the model, the Lake System Health Program still requires some way of determining when “enough is enough”, or of managing shoreline development so that water quality is not impaired

The model does provide: An estimate of the total natural and human phosphorus loading to a lake An estimate of how sensitive the lake is to additional load, without the need to calibrate the modelled concentration to measured concentrations, The background and rationale of how and why a lake can change in response to shoreline development

The Lake System Health Program should be built around those components of the program in which we have higher confidence. The table below reviews the key input parameters to the Provincial Model on which the Lake System Health model is based and my evaluation of the confidence and defensibility of each.

Overall, we conclude that:

There is good confidence in the ability of the model to predict lake sensitivity. There is lower confidence in the ability of the model to accurately predict phosphorus concentrations.

The 2012 Lake System Health Program, like the 2005 version should, therefore focus on those elements of the model and program in which confidence is highest, and place less planning weight on the modelled predictions of phosphorus concentrations.

The low confidence is related to the ability of the model to convert phosphorus loading from natural sources and shoreline development into concentrations in a lake. The basic assumption that septic system phosphorus is mobile is not supported by research and so can also be credibly challenged. Past experience with the Lake System Health Program (J. Brouse, pers. comm.) indicates that most residents do not understand the difference between measured and modelled results and accept „scientific data‟ provided to them as definitive. This includes both measured and modelled results. As a result the “scientific data” passed on to residents must be that for which the District has confidence Our analysis is that we have confidence in the measured phosphorus concentrations and less confidence in a modelled threshold or capacity estimate.

Hutchinson Environmental Sciences Ltd.

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Component/Input Confidence

Natural Atmospheric Load High confidence measured data from MOE High confidence – DMM now have a long term data set (~12 years) that Measured present day phosphorus is based on modern detection limits and methods– data are updated concentration every two years. Moderate-High confidence Anthropogenic Load to septic system Based on measured water usage and effluent phosphorus concentrations but data are old. Moderate Low – comparison of 1997 vs 2012 estimates shows that there is likely Lake, watershed and wetland areas some variance associated with the basic input parameters as mapping methods change High– these things should not change, “we have it correct now”. Moderate Confidence Measured and published relationship from MOE – High Wetland areas from GIS – Moderate – High. “We have it right now” Moderate - Relationship of DMM to MOE wetland Natural Load from Wetland definition/classification. There is moderate confidence that the DMM classification of a wetland is the same classification used to derive the Molot and Dillon export equation. The equation may differ slightly if it were to be derived using the DMM wetland classifications and the wetland areas may differ slightly. Nevertheless, the MOE recommends their equation for use across the province. Moderate Confidence Depth of Runoff Data are from long term monitoring programs but are regional and not lake specific. Moderate confidence Anthropogenic Load to lake –runoff A known component, export coefficients are estimates only Moderate confidence Three values, 205 lakes. Usage Factor Not updated in 20 years. Known site-specific errors. Low Confidence Settling Velocity Two values (oxic and anoxic) derived as averages for a data set and used for all lakes.

Predicted Background and Background + Low Confidence 50% Concentrations Figure 3, Table 3

Low confidence Anthropogenic Load to lake –septics Published studies of W. Robertson Increasing support of MOE and OMB

Predicted present day phosphorus Low confidence concentration Figure 1, Figure 2, Table 2

Hutchinson Environmental Sciences Ltd.

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I have therefore developed and recommend an approach that is based on:

1. The ten year record of measured phosphorus for each lake.

We have high confidence in the DMM measured data. An increasing trend would indicate a change to which managers should respond.

2. Basing the “Background + 50%” threshold on loading for each lake only, and not attempting to convert that to concentration.

We have Moderate-High confidence in the loading estimates, This maintains the MOE threshold without the uncertainty of converting it to a modelled concentration, This allows incorporation of abatement technology to reduce the load, Any perceived disadvantage or uncertainty in attenuation of septic phosphorus by soils is addressed by whether or not there is a trend in water quality (using measured data) and a conservative assessment of responsiveness (using those elements of the model in which we have higher confidence).

3. Defining “Lake Sensitivity” on the “responsiveness” metric only (High Confidence), and removing “mobility” from the calculation (Low confidence as it is based on comparing modelled to measured concentrations).

This incorporates a lake‟s inherent sensitivity to phosphorus loading.

Summary – Suggested Approach

Criteria-Over Threshold and Highly Sensitive Lakes

Criterion 1: Statistically significant increase in measured phosphorus over 11-year period of record. 1

Criterion 2: Modelled anthropogenic load of phosphorus > BG+50%.

Criterion 3: Standard Areal Load increases [TP] by >80% = “High” Sensitivity Standard Areal Load increases [TP] by 40% - 80% = “Moderate” Sensitivity Standard Areal Load increases [TP] by <80% = “Low” Sensitivity

1 This would be increased to 15 and 20 years as the period of record increased, based on formal review of the water quality record every 5 years. – using 10 years is necessary to start as that is the length of our reliable record now. Using only the most recent 10 years would risk a “sliding baseline”. So we now fix a measured baseline at 2002.

Hutchinson Environmental Sciences Ltd.

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Interpretation/Implementation

If 1 + 2 + 3 are true then: o the lake is changing, has a load that could cause it to exceed the MOE threshold and is likely to respond if that load reaches the lake o Highest level of protection in planning policies

If 1 is true and 2+3 are not, then: o the lake is changing, regardless of its sensitivity (there could be other causes that we do not fully understand and therefore cannot manage through the LSH program, e.g. climate change) o It is unlikely to exceed the MOE threshold as the load is < BG+50%, o It is not highly sensitive to additional load o Moderate level of protection in planning policies

If 2+3 are true and 1 is not then: o the lake is not changing but could, because the load exceeds the MOE threshold, o It is highly sensitive to additional load. o Moderate level of protection in planning policies

In all other cases then the lake is either : o Not developed to the point where load exceeds BG+50% so there is no risk of it exceeding the MOE threshold or o It is not sensitive if that load reaches it o Low level of protection in planning policies

Next Steps

1. I recommend that the District review this approach and provide comments back on any recommendations for change.

2. I have completed the modelling required for Criteria 2 and 3. We are working on the trend analysis (Criterion 1) and will have that completed shortly. When that is done I will prepare a summary that shows which lakes fall into each criterion and would therefore fall into the Low, Moderate or High planning categories.

I recommend that the District review those results and provide comments back on the acceptability of the resultant lake classifications and any recommendations for change.

3. When we have agreed on an approach then we will need to reopen our discussions with MOE to gain their acceptance.

Hutchinson Environmental Sciences Ltd.

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Hutchinson Environmental Sciences Ltd.

3-1 Taylor Road, Bracebridge, ON P1L 1S6 │ 705-645-0021 Suite 202 – 501 Krug Street, Kitchener, ON N2B 1L3 │ 519-576-1711 Memorandum

Date: May 6, 2013

To: Judi Brouse

From: Neil Hutchinson

Re: Revised Classification Scheme for DMM Lakes for Shoreline Development Planning Policies

Revised Approach to Lake Classification for Management Policies

The following provides a revised approach and rationale to the lake classification based our discussions and with input from Dr. Andrew Paterson at the MOE on management approaches for the District of Muskoka lakes. These revisions address:

Lakes with existing phosphorus concentrations that exceed the Provincial Water Quality Objective (PWQO) of 20 g/L (MOE, 1994; MOE et al., 2010),

Lakes that have a documented history of blue-green algal blooms,

Trends in total phosphorus concentration and lakes with insufficient long term data to assess changes in total phosphorus concentration over time

Re-evaluation of lake sensitivity into two categories instead of three categories.

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 to a maximum cap of 20 g/L (MOE et al., 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 water quality, 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 fails, 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

M070513_J100059_management classes.docx 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).

The LCM model results for the DMM lakes do not provide sufficiently accurate results to follow the Province’s revised PWQO approach to set capacity limits and the interim PWQO 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 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 1).

Table 1. Model Components and Evaluation of Confidence

Component Confidence High Confidence Lake, watershed areas - based on recent data and GIS High Confidence Natural Atmospheric Load - long-term (17 years) measured data from MOE, but is specific to Muskoka-Haliburton area. Moderate Confidence - Measured and published relationship from MOE - Wetland areas from GIS – Moderate - Relationship of DMM to MOE wetland definition/classification – Natural Load from Wetland Moderate - There is low confidence that the wetland classification used for DMM is identical to that used to derive the Dillon and Molot export equation used by the MOE model. The equation may differ slightly if it were to be derived using the DMM wetland classification. Moderate Confidence Depth of Runoff - 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. Settling Velocity - 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

Predicted Background and Background + Low Confidence 50% Concentrations - Model error >20% Moderate Confidence Anthropogenic Load to septic system -Based on measured water usage and effluent phosphorus concentrations, but data are old Low Confidence - Published studies of W. Robertson show that phosphorus is not always Anthropogenic Load to lake –septics mobile -Increasing acknowledgment of attenuation by soils from the MOE and OMB

Anthropogenic Load to lake –runoff Moderate Confidence

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- 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% in accordance with published differences between export from forested watersheds on and off the Shield. Moderate confidence - Three values, 354 lakes. Usage Factor - Not updated in 20 years, based on Central Ontario surveys - Known site-specific errors. Low confidence - Figure 4, Table 5 Predicted Present Day Concentration - Poor model performance for 60% of the validation lakes (n=65); 20% are underestimated by an average of 42% and 40% are overestimate by an average of 130%

Measured Present Day Concentration High Confidence

Overall, although the LCM does not provide accurate estimates of phosphorus concentrations in developed or undeveloped lakes, it can be used to provide good estimates of phosphorus loads to lakes and their relative sensitivity to those loads. Management of shoreline development should therefore be based on these aspects of the LCM for which there is greater confidence and on the record of measured phosphorus concentrations. These features can be used to provide a high level of protection for DMM lakes, would be easily understood by the public, and would provide the necessary defensibility and rigour to policy.

Primary Classification Criteria

The recommended approach to establish the level of protection required for lakes in the DMM therefore includes two primary criteria that can be calculated for each lake:

1. Whether or not the existing phosphorus load to the lake is 50% greater than the natural or “background” load.

This criterion meets the intent of the revised PWQO to limit the increase in phosphorus concentration of lakes to 50% over background, without the uncertainty of converting loads to a modelled concentration. If phosphorus loads exceed BG+50%, there is a potential for concentration to also exceed BG+50%.

2. Whether the lake has a High Sensitivity or Low Sensitivity to phosphorus loading.

Lake sensitivity is the degree to which a lake will respond to a standard addition of phosphorus and is a function of such attributes as the lake size, shape, surface area and flow of water. Lake sensitivity is assessed by use of a standard areal load of phosphorus – a fixed amount of loading that is applied to each ha of lake surface. The lake is modelled with a standard density of development equal to 1 lot/1.62 ha (or 4 acres) of lake surface area. This density is used as a “filter” by several Ontario municipalities to assess “crowding”, social density or recreational use of lake surface areas. The lake is then classified as having “High Sensitivity” to phosphorus loading if the standard areal load of 1 lot/1.62 ha results in phosphorus concentration changing by 50% or more from the background concentration or “Low Sensitivity” if the change is less than 50%.

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Classification Triggers

Three other factors; phosphorus concentrations exceeding 20 ug/L, trends in phosphorus concentration and occurrence of bluegreen algal blooms are also considered in the management approach, not as criteria for classification, but as triggers for additional study and if required, a management response.

1. Whether or not the measured total phosphorus concentration exceeds the PWQO cap of 20 g/L for protection against nuisance algal and aquatic plant production.

Figure 1. TP vs DOC in DMM lakes.

TP-DOC Relationship in 193 Muskoka Lakes 30 28 26 24 22 20 18 16 y = 1.3715x + 2.3406 14 R² = 0.4552 12 10

Total Phosphorus (ug/L) Phosphorus Total 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13

Dissolved Organic Carbon (mg/L)

Figure 2, however, confirms that High DOC/High phosphorus lakes do not have high inputs from human sources. The total phosphorus loading from human sources does not exceed 50% for any lakes with TP > 20ug/L and DOC > 8 mg/L. In these cases, phosphorus concentrations naturally exceed the PWQO and are not a good trigger for human influence.

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Figure 2. Human phosphorus loading in DMM lakes with high TP and high DOC.

Total Phosphorus (ug/L) Phosphorus Total 8 Fox DI = 1.16 Perch DI=1.1 6 R² = 0.4552 Clark DI - 1.29 Buck HT DI = 1.19 4

2 0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 Dissolved Organic Carbon (mg/L)

We therefore recommend the following trigger

1. Whether or not the measured total phosphorus concentration exceeds the PWQO cap of 20 g/L for protection against nuisance algal and aquatic plant production in a lake in which DOC is < 8 mg/L. The lake would maintain its classification as derived through the two primary criteria but more investigation is warranted to determine the role of development in the phosphorus enrichment and to respond as required by amendments to policy.

2. A long-term trend in total phosphorus concentration may indicate a response to human phosphorus loads or other factors related to climate change and variability and so may not make a reliable input to planning policy. At least 10 years of data are also recommended to assess long term changes. While many DMM lakes have ~10 years of monitoring results, we recommend that trend analysis not be included as a criterion for lake classification at this stage.

The DMM should evaluate total phosphorus data for trends annually and, if an increasing upward trend is noted, the lake would maintain its classification but more investigation is warranted to evaluate the cause of the trend and to respond as required by amendments to policy.

3. 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 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, algal bloom activity can be triggered by factors other than elevated phosphorus concentrations resulting from human sources of phosphorus. For example, bluegreen algae are known to bloom

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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) 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.

Using the above criteria, lakes can be classified into three categories of protection for planning policies to manage shoreline development: “Enhanced”, “Strict” and “Standard” (Figure 3).

Figure 3. Management Classification Matrix for Planning Policies

P Load ≥BG+50% P Load < BG+50% High Sensitivity Enhanced Moderate Low Sensitivity Moderate Standard

“Enhanced” management is required for lakes that: a) have a phosphorus load that that could cause them to exceed the revised PWQO for total phosphorus concentration (i.e., P Load ≥BG +50%) and have high sensitivity to phosphorus loads.

These lakes have either been, or are likely to be impaired by phosphorus inputs from human sources. Additional phosphorus loads could further impair water quality in these lakes and should be avoided by, for example, freezing additional development or putting very strict site development requirements in place to minimize the potential for phosphorus inputs – this is a critical decision point .

“Moderate” management is required for lakes that: a) have a phosphorus load that that could cause them to exceed the revised PWQO for total phosphorus concentration (i.e., P Load ≥BG +50%) but have low sensitivity to phosphorus loads, or b) have a phosphorus load that would not cause them to exceed the revised PWQO for total phosphorus concentration (i.e., P Load

These lakes are unlikely to be impaired by phosphorus loads exceeding BG+50% as they are of low sensitivity or are of high sensitivity but their loads do not exceed BG+50% so that they have some capacity for additional loads. Additional development can be permitted but policies put in place so that the potential for additional phosphorus loads is minimized as much as possible to avoid degradation of water quality.

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“Standard” management is required for lakes that have a phosphorus load that would not cause them to exceed the revised PWQO for total phosphorus concentration (i.e., P Load

Lakes in which TP exceeded 20 ug/L and DOC was < 8mg/L Lakes in which an increasing trend was confirmed over ten years, or Lakes exhibiting a blue-green algal bloom

Would be subject to more investigation to evaluate the cause and to respond as required.

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Hutchinson Environmental Sciences Ltd.

1-5 Chancery Lane, Bracebridge, ON P1L 2E3 │ 705-645-0021 Suite 202 – 501 Krug Street, Kitchener, ON N2B 1L3 │ 519-576-1711 Memorandum

Date: April 29, 2015

To: Samantha Hastings and Christy Doyle

From: Neil Hutchinson and Tammy Karst-Riddoch

Re: J100059 – Lake System Health Program Modifications

We have prepared the following as a summary of our March 27, 2015 meeting at which we discussed a revised approach to the District of Muskoka Lake System Health Program. The recommended approach and revisions are outcomes of our review and update of the water quality model which has formed the technical foundation of the program since its inception. This memo is intended as a summary of the review process, its outcomes and resulting recommendations to inform discussion with District staff, Councillors and the public. Once we have agreed on this approach we will amend the December 19, 2013 technical report to add the necessary details supporting the recommendations in this memo.

Background

The District Municipality of Muskoka (DMM) uses their Water Quality Model (MWQM), a variant of MOE’s “Lakecap” Model, 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. 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 2005. The MOE promote the use of their “Lakecap” Model and Guidance document which was released in 2010 as their recommended means of Lakeshore Capacity Planning. Prior to 2010, the MOE encouraged use of this approach, although it had not yet been finaIized as formal Provincial Guidance.

The “Lakecap” approach is based on modelling the current phosphorus concentrations in a lake and then calculating the amount of shoreline development that the lake can sustain (or lake capacity) and remain below a modelled phosphorus concentration of “Background + 50%”. The MOE 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 MOE model was developed and calibrated on a set of small headwater lakes in Muskoka and Haliburton, the MOE advise that the model should be used in a watershed context – that is, any lake that is being modelled should incorporate all 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 MOE 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.

M01052015-J100059-LSHPrevisions.docx Results of 2005 Review

The model was last updated in 2005. 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 capacities1. We concluded that the model could not do this and the DMM implemented Lake System Health which moved away from planning policies that were based strictly on lakeshore capacity calculations to include policies that considered a) the ability of the model to predict phosphorus concentrations in lakes and b) lake sensitivity to additional development.

The 2005 Lake System Health Program was therefore implemented as a modification of the previous DMM approach. It included only one planning category (OverThreshold) that was based on modelled phosphorus concentrations. The remaining planning categories used the model to determine lake sensitivity2 to phosphorus loads and did not set lakeshore capacity limits based on modelled phosphorus concentrations. The resultant policies addressed the means to manage future development to minimize its impact on water quality by implementing a series of increasingly stringent study requirements and Best Management Practices for development with increasing lake sensitivity.

Results of 2013 Review

HESL revised the MWQM to incorporate the most recent MOE guidance (MOE 2010, Paterson et al. 2006). The revision and testing included the following changes from the previous version:

Revised atmospheric loading coefficients for phosphorus, Revised wetland phosphorus export equation for phosphorus, Incorporation of smaller lakes (8ha and greater) in the model, Revised GIS mapping of lake areas, watershed areas and wetland areas, Updated estimates of existing shoreline development (including developed and vacant lots) 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) Comparison of model output against the most recent 10 year record of total phosphorus measurements made in DMM lakes.

1The 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 any development beyond 128 lots is refused. Our review concluded that the model, for all lakes, overestimated phosphorus concentrations by 38%, underestimated them by 23% and that the 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. 2 Lake sensitivity was defined for each lake by based on its relative change in phosphorus concentration to a standard load of phosphorus (i.e., ‘responsiveness’) and the potential for septic system phosphorus to reach the lake (i.e., ‘mobility’)

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M01052015-J100059-LSHPrevisions.docx 2 were expressed by MOE scientists, based on their recent experience, when we presented our findings to them in a meeting with DMM in January of 20133.

We considered using the 2005 approach to similarly 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 to classify lakes that may be inaccurate or that can change over time in response to new scientific understanding or as a result of changing MOE 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, the approach remained focussed only on phosphorus and did not address other threats to the lakes.

Muskoka’s lakes are changing and are potentially threatened by a variety of stressors not related to shoreline development. The recent Canada Water Network Research Program in the Muskoka watershed, for example, showed that dissolved organic carbon levels are increasing, calcium levels are declining, invading species are populating an increasing number of lakes and the climate is changing with resultant changes in precipitation, temperature, runoff and evaporation that affect physical, chemical and biological conditions of lakes. Analysis of the DMM phosphorus record from 190 lakes for the period from 2000-2014 showed that there was no trend to increasing phosphorus in any of the lakes, but three lakes showed a statistically significant decline. Future changes in phosphorus concentration in lakes and other water quality issues (e.g., algal blooms) are possible in response to multiple stressors and should be considered in the management approach. Planning policy that is focussed solely on phosphorus sources is not protective in this environment.

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 the model results in recognition of the uncertainty that the modelling approach adds to the planning process,

b) Focusses planning policies on the excellent water quality monitoring program that has been in place for 15 years, and

c) Recognizes Best Management Practices and development standards that can effectively mitigate impacts of shoreline development and address a host of other environmental concerns.

3 Although MOE continues to recommend their 2010 “Lakecap” process they also recognize some of its weaknesses and are reconsidering their approach to managing shoreline development. In February of 2014, MOE awarded HESL a contract to complete a jurisdictional scan of fourteen jurisdictions located in Ontario, Nova Scotia, British Columbia and the USA to identify and describe technical and planning approaches to the management of shoreline development in order to guide future initiatives in the Province of Ontario. Common elements of the approach in each jurisdiction, including scientific tools and policy and regulatory approaches, were analyzed and evaluated in terms of their success and potential for application in Ontario.

Hutchinson Environmental Sciences Ltd.

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Recommended Approach

We recommend that the Lake System Health program be based on:

a) a minimum and enforced standard of protection for new development and redevelopment on all lakes, b) use of the District monitoring program to classify lakes based on observed water quality, and c) enhanced study and Best Management Practices for individual lakes based on observed water quality concerns, or ‘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. There is high confidence in measured data and the DMM water quality monitoring program collects data that can be used to assess lake status. The monitoring data that are routinely collected on Muskoka’s lakes can be used to inform the following triggers of lake sensitivity:

phosphorus concentrations exceeding 20 ug/L, increasing trends in phosphorus concentration and occurrence of bluegreen algal blooms

These are recommended for the management approach, as triggers for additional study and, if required, a management response.

Trigger 1 - Total Phosphorus > 20 g/L

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 at least 5 spring overturn phosphorus measurements in 10 years is required to assess long term concentration and records should be reviewed annually. For triggered lakes, a “Causation Study” would be required to determine the role of development in the phosphorus enrichment and “Enhanced BMPs” would be required for development or redevelopment.

Trigger 2 - Increasing Trend in Total Phosphorus

Trigger 3 - Documented Blue-Green Algal Bloom

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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, blue- green 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 concentration 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 sediments and then migrate into low phosphorus surface waters and bloom (Peninsula Lake in ~1995).

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, a “Causation Study” would be required to determine the role of development in the blue-green algae activity and “Enhanced BMPS” would be required for development or redevelopment.

Summary

In summary, we recommend that:

1. All lakes be afforded a high degree of protection by a requirement for a minimum set of Standard” BMPs for all new development or redevelopment. 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 and 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 and remedial actions if warranted. c. A “freeze” on new lot creation if the causation study determines that human phosphorus loading is the cause.

This approach would simplify policy implementation, provide a consistent and verifiable public and planning framework of lake status and be based on the DMM’s excellent record of lake water quality.

Hutchinson Environmental Sciences Ltd.

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The process is summarized in the following flow chart (Figure 1).

Figure 1. Proposed Lake System Health Planning Approach.

All DMM Lakes

Standard BMPs for New Development and ReDevelopment

Sample Lakes and Review Data Annually

TP > 20 ug/L No Increasing TP Trend Documented Blue-Green Algal Bloom

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

No Development Related TP as Cause?

Yes

Limit Lot Creation Remedial Action Plan

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Proposed “Standard” and “Enhanced” BMPs are presented in the Table 1. These will be further elaborated in the technical report and in Schedules to the Official Plan.

Table 1. 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// X Full Servicing Limits to Growth Assessment X Causation Study X Limit Lot Creation X Remedial Action Plan X

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Hutchinson Environmental Sciences Ltd.

1-5 Chancery Lane, Bracebridge, ON P1L 2E3 │ 705-645-0021 Suite 202 – 501 Krug Street, Kitchener, ON N2B 1L3 │ 519-576-1711 Memorandum

Date: August 28, 2015

To: Samantha Hastings and Christy Doyle

From: Neil Hutchinson

Re: J150074 – Lake System Health Program Modifications

We have prepared the following summary of a revised approach to the District of Muskoka Lake System Health Program. The approach originated in a March 27 meeting between HESL (Neil Hutchinson) and the District Municipality of Muskoka (DMM: Christy Doyle and Samantha Hastings) which was presented in a memo (HESL to DMM April 29 2015) and comments and clarifications received from the DMM on June 29, 2015.

The recommended approach and revisions are outcomes of our review and update of the water quality model which has formed the technical foundation of the program since its inception. This memo is intended as a summary of the review process, its outcomes and resulting recommendations to inform discussion with District staff, Councillors and the public. Once we have agreed on this approach we will amend the December 19, 2013 technical report to add the necessary details supporting the recommendations in this memo.

Background

The District Municipality of Muskoka (DMM) uses their Water Quality Model (MWQM), a variant of the Province’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. 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 2005. The Ministry of the Environment and Climate Change (MOECC) promotes the use of the “Lakecap” model and guidance document which was released in 2010 as the recommended means of Lakeshore Capacity Assessment1. Prior to 2010, the MOECC encouraged use of this approach, although it had not yet been finaIized as formal Provincial guidance.

The “Lakecap” approach is based on modelling the current phosphorus concentrations in a lake resulting from natural and human inputs and then calculating the amount of phosphorus from shoreline development that the lake can sustain while remaining below a modelled phosphorus concentration of “Background + 50%” (the “lake capacity”) to a maximum concentration of 20 g/L. The MOECC approach requires that the

1Province of Ontario. 2010. 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 2010. PIBS 7642e © 2010, Queen’s Printer for Ontario.

M01092015-J150074-LSHPrevisions.docx 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 Lakecap model was developed and calibrated on a set of small headwater lakes in Muskoka and Haliburton, the MOECC advises that the model should be used in a watershed context – that is, any lake that is being modelled should incorporate all lakes in its watershed. (p. 29, Province of Ontario 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 Lakecap model, as it did in the previous versions. The MOECC recognizes this in the 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 MWQM was last updated in 2005. 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 capacities2. We concluded that the model could not set defensible lot development capacities and, as a result, the DMM implemented “Lake System Health” which moved away from planning policies that were based strictly on lakeshore capacity calculations to include policies that considered a) the ability of the model to predict phosphorus concentrations in lakes, and b) lake sensitivity to additional development.

The 2005 Lake System Health Program was therefore implemented as a modification of the previous DMM approach. Primary modifications included:

One planning category (“Over Threshold”) that was based on modelled phosphorus concentrations. The remaining planning categories used the model to determine lake sensitivity3 to phosphorus loads and 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 with increasing lake sensitivity.

2The 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 the revised model produced estimated phosphorus concentrations often differed from measured values and the 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. The Province advises that the modelled phosphorus concentration should be accurate to within 20% of the measured value. 3 Lake sensitivity was defined for each lake by based on its relative change in phosphorus concentration to a standard load of phosphorus (i.e. ‘responsiveness’) and the potential for septic system phosphorus to reach the lake (i.e., ‘mobility’).

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Results of 2013 Review

The most recent review began in 2010 and HESL revised the 2005 version of the MWQM to incorporate the most recent MOECC guidance (Province of Ontario 2010, Paterson et al. 20064). The revision and testing included the following changes from the previous version:

Revised atmospheric loading coefficients for phosphorus, Revised wetland phosphorus export equation for phosphorus, Incorporation of smaller lakes (8 ha and greater) in the model, Revised GIS mapping of lake areas, watershed areas and wetland areas, Updated estimates of existing shoreline development (including developed and vacant lots) Removal of the model factors that accounted for attenuation of septic system phosphorus (soil classification and staged attenuation of septic system phosphorus in 100-m increments from the lakeshore to 300 m) and, Comparison of model output against the most recent 10-year record of total phosphorus measurements made in DMM lakes.

We considered continued use of 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 as a result of 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, the approach remained focussed only on phosphorus and did not address other threats to water quality in the lakes. In addition, the inability of the model to predict accurate development capacities and the emergence of P reduction technologies over the past 15 years resulted in OMB decisions allowing development despite modelling results. OMB hearings can be costly for Municipalities and reduce public acceptance of planning policies.

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

5 Although MOECC continues to recommend the 2010 “Lakecap” process, they also recognize some of its weaknesses and are reconsidering their approach to managing shoreline development. In February of 2014, MOECC awarded HESL a contract to complete a jurisdictional scan of fourteen jurisdictions located in Ontario, Nova Scotia, British Columbia 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. Common elements of the approach in each jurisdiction, including scientific tools and policy and regulatory approaches, were analyzed and evaluated in terms of their success and potential for application in Ontario. The MOECC is currently considering the results of this review.

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M01092015-J150074-LSHPrevisions.docx 3 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.6 Recent research by the MOECC shows increasing reports of nuisance algal blooms across Ontario as a possible response to changing climate7.

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 2002-2014 showed that there was no trend to increasing phosphorus in any of the lakes, but 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 Best Management Practices for control of phosphorus and the other stressors acting in Muskoka’s lakes.

a) Eliminates the classification of lakes based on modelled estimates of phosphorus in recognition of the uncertainty that the modelling approach adds to the planning process,

b) Focusses planning policies on the excellent water quality monitoring program that has been in place for 15 years, and

c) Recognizes Best Management Practices and development standards that can effectively mitigate impacts of shoreline development on phosphorus, but also address a host of other environmental concerns.

Recommended Approach

We therefore recommend that the Lake System Health program be based on:

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

6 See, for example, Palmer, M.E., N.D. Yan, A.M. Paterson and R.E. Girard. 2011. 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: 1038- 1049.

7 Winter, J.G., A.M. DeSellas, R. Fletcher, L. Heintsch, A. Morley, L. Nakamoto and K. Utsumi. 2011. Algal blooms in Ontario: Increases in reports since 1994. Lake and Reservoir Management 27:107-114.

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3. Implementation of enhanced planning requirements and Best Management Practices for individual lakes based on observed water quality concerns, or ‘triggers’. These could include implementation of “causation studies” on individual lakes and focussed use of the existing model8.

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 increase allowed under “Lakecap”), A statistically significant increasing trend in phosphorus concentration, based on evaluation of the phosphorus concentration record measured by the DMM monitoring program since 2002, and Occurrence of bluegreen algal (cyanobacterial) blooms as confirmed by 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

A measured total phosphorus concentration that exceeds the PWQO of 20 g/L for protection against nuisance algal and aquatic plant production may warrant investigation and enhanced protection. A data record of the most recent 5 spring overturn phosphorus measurements in 10 years is required to assess long term concentration and records should be reviewed annually. Most Muskoka lakes are sampled every two years and have sufficient data to assess the long term concentration. For lakes with a less frequent record (e.g., every 3 years) then the most recent five measurements would be used.

For triggered lakes, 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, while management recommendations such as “Enhanced BMPs” would be developed to protect water quality from any new development or redevelopment of shoreline properties. Enhanced BMPs would be elaborated in, and drawn from, a schedule in the District’s Official Plan.

Trigger 2 - Increasing Trend in Total Phosphorus

8 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 accuracy- such 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.

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M01092015-J150074-LSHPrevisions.docx 5 increase must be statistically significant at p<0.1. We recommend that trends be assessed using data beginning in 2002, when sampling and laboratory methodologies improved and high quality phosphorus measurements were available for the DMM program. The trend would be reassessed as more measurements were obtained but the starting point would remain at 2001. 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, a “Causation Study” would be required to determine the role of shoreline development or other human factors in the phosphorus enrichment, while management recommendations such as “Enhanced BMPs” would be developed to protect water quality from any new development or redevelopment of shoreline properties. Enhanced BMPs would be elaborated in, and drawn from, a schedule in the District’s Official Plan.

Trigger 3 - Documented Blue-Green Algal Bloom

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, blue- green 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 concentration in the hypolimnion due to internal loading from anoxia (e.g., Three Mile Lake) may be susceptible to blue-green blooms. In other lakes, blue-green algae may take phosphorus from lake sediments and then migrate into low phosphorus surface waters and bloom (e.g., Peninsula Lake in ~1995). Blooms in these lakes may therefore reflect natural conditions and not be related to shoreline development or other human sources.

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, a “Causation Study” would be required to determine the role of shoreline development or other human factors in the cyanobacterial bloom, while management recommendations such as “Enhanced BMPs” would be developed to protect water quality from any new development or redevelopment of shoreline properties. Enhanced BMPs would be elaborated in, and drawn from, a schedule in the District’s Official Plan.

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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 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. The need for, and nature of, lake management must, however, be based on an understanding of the factors that caused a) phosphorus concentrations to exceed 20 µg/L, b) a significant increasing trend in phosphorus concentrations, or c) a cyanobacterial bloom.

Causation studies are therefore recommended for “triggered” lakes to determine a) the cause of the trigger, b) the role of shoreline development in the observed trigger, and c) 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, dissolved oxygen (DO) and dissolved organic carbon (DOC) measurements), Collection of additional water quality data (e.g., hypolimnetic samples to assess internal load), Detailed and lake-specific application of the model to consider more accurate counts of shoreline development and usage (seasonal vs permanent), land use in the watershed and catchment soil types and depth to assess any phosphorus attenuation by soils, Site-specific investigations of hydrology and inflows to assess any flooding or localized changes in the catchment from road construction, beaver dams, or other factors that may alter water loads and hence phosphorus dynamics, A septic system inspection program to identify any potential issues, A survey of shoreline disturbance (i.e., shoreline erosion, areas of poor vegetative buffers) A “Limits to Growth” assessment 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. .

We would propose that the DMM be responsible for conducting the Causation Studies and that the results of these be posted in a Schedule to the District OP to show the results and resultant requirements for development or redevelopment of shoreline properties.

Development and redevelopment of shoreline properties on lakes which were not triggered could proceed under standard planning requirements using the “Standard” BMPs listed below to protect water quality, Lakes which were “triggered” would undergo a “Causation Study” o If the Causation study concluded that shoreline development was not likely responsible for the trigger then development or redevelopment could proceed but with the use of the “Enhanced” BMPs in addition to the ”Standard” BMPs to recognize that these lakes may be susceptible to additional phosphorus loading. o If the Causation study concluded that shoreline development was likely responsible for the trigger then development of shoreline properties would not be permitted. Redevelopment could be considered if it included improvements to reduce nutrient loading from the site.

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Summary

In summary, we recommend that:

1. All lakes be afforded a high degree of protection by a requirement for a minimum set of “Standard” BMPs for all new development or redevelopment of shoreline lots. Examples are shown jn Table 1 as a starting point for discussion. 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 and documented occurrence of a blue-green algal bloom. 3. That triggered lakes 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 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 and hydrology 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 was 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 monitoring record of lake water quality. The process is summarized in the following flow chart (Figure 1).

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Figure 1. 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 No Increasing TP Trend Documented Blue-Green Algal Bloom

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

No Development Related TP as Cause?

Yes

Limit Lot Creation Remedial Action Plan

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Proposed “Standard” and “Enhanced” BMPs are presented in the Table 1. These will be further elaborated in the technical report and in Schedules to the Official Plan.

Table 1. 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// X Full Servicing Slope Dependent Setback X Enhanced Building Setback X Causation Study X Limit Lot Creation X Remedial Action Plan X

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Hutchinson Environmental Sciences Ltd.

1-5 Chancery Lane, Bracebridge, ON P1L 2E3 │ 705-645-0021 Memorandum

Date: January 4, 2016

To: Samantha Hastings and Christy Doyle - District Municipality of Muskoka

From: Neil Hutchinson and Brent Parsons

Re: J150074 – Lake System Health Program Modifications

This memo summarizes our recommendations for a revised approach to the District of Muskoka Lake System Health Program. Our recommendations arise from our review and update of the District’s water quality model which has formed the technical foundation of recreational water quality management in Muskoka for the past 25 years. The revised approach originated in a March 27, 2015 meeting between HESL (Neil Hutchinson) and the District Municipality of Muskoka (DMM: Christy Doyle and Samantha Hastings). We presented the approach in a memo (HESL to DMM April 29 2015) and comments and clarifications were received from the DMM on June 29, 2015. Since that time we have had several discussions, presented the proposed revisions to the MOECC and have added the resultant clarifications and requests for additional analysis to our original memo.

This memo summarizes the review process, its outcomes and resulting recommendations to inform discussion with District staff, Councillors and the public. We are also amending the December 19, 2013 technical report to add the necessary details supporting the recommendations in this memo.

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. 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 2005. The MOECC released their “Lakecap” Model and guidance document in 2010 as their recommended means of Lakeshore Capacity Planning1. Prior to 2010, the MOECC encouraged use of this approach, although it had not yet been finaIized as formal Provincial guidance.

1 Ontario Ministry of the Environment. 2010. 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 2010. PIBS 7642e © 2010, Queen’s Printer for Ontario.

M04012016-J150074-LSHPrevisions.docx 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.

Results of 2005 Review

The model was last updated in 2005. 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 capacities2. 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 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. The remaining planning categories (“Low”, “Moderate” and “High” Sensitivity) used the model to determine lake sensitivity3 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.

2The 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. 3 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’).

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Results of 2013 Review

The most recent review began in 2010. HESL revised the 2005 version of the MWQM to incorporate the most recent MOECC guidance (MOE 2010, Paterson et al. 20064). These revisions included:

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, 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.

4 Paterson, A.M., P.J. Dillon, N.J. Hutchinson, M.N. Futter, B.J. Clark, R.B. Mills, R.A. Reid and W.A. Scheider. 2006. A review of the components, coefficients, and technical assumptions of Ontario’s Lakeshore Capacity Model. Lake and Reservoir Management. 22(1):7-18. 5 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 February of 2014, MOECC awarded HESL a contract to complete a jurisdictional scan of fourteen jurisdictions located in Ontario, Nova Scotia, British Columbia 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. Common elements of the approach in each jurisdiction, including scientific tools and policy and regulatory approaches, were analyzed and evaluated in terms of their success and potential for application in Ontario. The MOECC are currently considering the results of this review.

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M04012016-J150074-LSHPrevisions.docx 3 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.6 Recent research by the MOECC also shows increasing reports of nuisance algal blooms across Ontario, a possible response to changing climate7.

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:

a) A minimum and enforced standard of protection and Best Management Practices for new development and redevelopment on all lakes,

b) Use of the District monitoring program to track phosphorus on DMM lakes and classify them according to measured changes and observed quality, and

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c) 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 model8 in response to the monitoring triggers.

Lake Planning and Management Triggers

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

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 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

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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

For triggered lakes:

Trigger 3 - Documented Blue-Green Algal Bloom

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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.

Causation Studies

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, 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)

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A “Limits to Growth” assessment based on the present shoreline characteristics (see http://www.muskokawaterweb.ca/lake-data/muskoka-data/shoreline-land-use-maps) 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.

Our analysis was done using monitoring results for 2000-20149. Five lakes had 10 year (2005-2014) average TP concentrations exceeding >20 µg/L. These were Ada Lake, Atkins Lake, Barrons Lake, Bass Lake (Gravenhurst) and Three Mile Lake Main. Three of 190 lakes (Clark Lake, Mirror Lake, Tackaberry Lake) exhibited statistically significant decreasing TP concentrations from 2000-2014 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.

Conceptual Causation Study tasks are presented below for Bass Lake, Bruce Lake and Three Mile Lake.

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 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

9 Memo: HESL (Karst-Riddoch) to DMM (Brouse) Sept. 23, 2014

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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. (Figure 1 below shows the relationship for 2002-2011 as an example. This data needs to be reviewed and reanalysed).

Figure 1. TP vs DOC for Muskoka lakes (2002-2011)

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Bruce Lake

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.

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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 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.

Three Mile Lake

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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

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” 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.

Proponents of development or redevelopment would be responsible for the costs associated with implementation of standard or enhanced BMPs.

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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. 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. 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 and documented presence of a blue-green algal bloom. 3. That triggered lakes be subject to: a. Enhanced BMPs (Table 1) 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 2). Proposed “Standard” and “Enhanced” BMPs are presented in Table 1.

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Table 1. Proposed BMPs for “Standard” and “Enhanced” Lake Classifications.

Standard Enhanced Vegetated Buffers X X

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Figure 2. 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 No Increasing TP Trend Documented Blue-Green Algal Bloom

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

No Development Related TP as Cause? Yes

Limit Lot Creation Remedial Action Plan

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

Appendix E. 2014 Total Phosphorus Update

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Suite 202 – 501 Krug Street, Kitchener, ON N2B 1L3 │ 519-576-1711 Memorandum

Date: September 23, 2014

To: Judi Brouse From: Tammy Karst-Riddoch

Re: DMM Lake System Health – 10-year ave. TP and TP trends update

As you requested, I have updated the calculations of the 10-year average spring total phosphorus concentration and trends assessment with the data collected in 2013 and 2014 for the Lake System Health Program. All data collected since 2000 were re-evaluated for a) bad splits between sample duplicates and b) outliers, prior to computing the new 10-year average and trend statistics. The procedures for identifying bad splits and outliers followed GLL (2008) with a few minor exceptions, described in the following sections.

All data and results of statistical analysis are summarized in Tables and Figures in this memo, but are also provided as digital Excel files for your convenience (attached).

1. Bad Splits

Duplicate TP samples have been collected for each monitoring site since 2002. In previous assessments of the TP data, bad splits in duplicate sample pairs were assessed using an outlier approach as described by GLL (2008). Briefly, in this approach, the two measurements were added to the formerly available data from the same lake as two individual data points and then outlier tests (Dixon and Grubb’s tests) were applied to identify whether either of the two measurements were statistical outliers.

In this assessment, duplicate TP samples were evaluated following the approach that is now recommended by the MOE for the Lake Partner Program data. If the variance between sample pairs is >25%, the larger of the two values is considered to be contaminated and is discarded from the dataset. A total of 110 bad splits were identified following the MOE approach (Table 1). This represents 8% of the total sample pairs (1304) collected since 2002. Note that three duplicates identified as having bad splits had one value that was very low (<0.5 g/L) and was discarded; the higher value was maintained in these cases.

2. Outliers

Outliers were assessed following the procedures outlined by GLL (2008). If 6 or more data years were available, only the Grubb’s test was used to identify outliers at p<0.05, otherwise both the Grubbs and Dixon tests were used. The Grubbs and Dixon tests gave opposite results for one site, Lake Huron-Cognashene Bay. The outlier test was re-run for this site, therefore, by including the sample pairs for the outlier in question as individual data points to increase power of the test. No outlier was detected by this method.

Sites in Lake Joseph (and Little Lake Joseph) have been monitored at approximately biweekly intervals from spring to the end of August since 2008. The outlier tests were performed using all values from each sampling event in all sample years (and not the mean phosphorus concentrations measured each year).

M250914_J100059_TP update A total of 54 outliers were detected in the 2000-2014 data set, including 18 outliers from sites in Lake Joseph (Table 2).

Table 1. Bad Splits in TP Sample Duplicates (2000-2014)

Sample TP1 TP2 % Mean TP Site Name Date Year (g/L) (g/L) Variance (g/L) Little Lake Joseph 24-Jul-14 2014 5.0 7.2 25.5 5.0 McCrae Lake 1-Jun-10 2010 15.0 10.4 25.6 10.4 Loon Lake 1-Jun-10 2010 66.8 96.6 25.8 66.8 Penfold Lake 27-May-02 2002 19.4 13.4 25.9 13.4 Walker Lake 18-May-06 2006 8.4 5.8 25.9 5.8 Camp Lake 4-May-07 2007 4.0 5.8 26.0 4.0 Lake Joseph-Cox Bay 8-Jun-09 2009 5.8 4 26.0 4.0 Lake Joseph-Hamer Bay 4-Jun-08 2008 5.8 4 26.0 4.0 Lake Joseph-Cox Bay 23-Jun-09 2009 4.4 6.4 26.2 4.4 Lake Rosseau-Brackenrig Bay 21-May-03 2003 7.0 10.2 26.3 7.0 Lake of Bays - South Portage Bay 2-Jun-05 2005 10.8 7.4 26.4 7.4 Oudaze Lake 13-May-04 2004 10.0 14.6 26.4 10.0 Six Mile Lake - Main 29-May-07 2007 11.4 7.8 26.5 7.8 Lake Joseph-North 25-May-09 2009 11.6 17 26.7 11.6 Silversands Lake 25-May-10 2010 6.8 10 26.9 6.8 Tucker Lake 8-Jun-09 2009 6.2 4.2 27.2 4.2 Little Lake Joseph 4-Aug-09 2009 13.0 8.8 27.2 8.8 Lake Joseph-North 22-May-08 2008 7.4 5 27.4 5.0 Pigeon Lake 29-Apr-10 2010 8.0 5.4 27.4 5.4 Tackaberry Lake 11-May-05 2005 8.6 5.8 27.5 5.8 Brooks Lake 25-May-11 2011 12.2 8.2 27.7 8.2 Lake Joseph-Main 10-Aug-10 2010 2.4 3.6 28.3 2.4 Lake Joseph-Cox Bay 5-Jun-13 2013 7.2 4.8 28.3 4.8 Lake Joseph-North 14-Jun-10 2010 2.4 3.6 28.3 2.4 Lake Waseosa 15-May-03 2003 14.4 9.6 28.3 9.6 Lake of Bays - Ten Mile Bay 12-May-09 2009 7.8 5.2 28.3 5.2 Little Lake Joseph 6-Jul-09 2009 6.0 4 28.3 4.0 Longs Lake 11-May-11 2011 15.4 10.2 28.7 10.2 Pine Lake BB 22-May-03 2003 7.0 4.6 29.3 4.6 Young Lake 27-May-08 2008 10.4 6.8 29.6 6.8 Lake Joseph-Cox Bay 27-May-14 2014 8.4 5.4 30.7 5.4 Lake Joseph-Main 6-Jul-09 2009 3.6 5.6 30.7 3.6 Lake Joseph-Cox Bay 4-Jun-08 2008 4.6 7.2 31.2 4.6 Wood Lake 29-Apr-11 2011 7.2 11.4 31.9 7.2 Bonnie Lake 9-May-13 2013 9.2 5.8 32.1 5.8 High Lake 21-May-08 2008 5.8 9.2 32.1 5.8 Lake Joseph-Main 27-Aug-13 2013 5.4 3.4 32.1 3.4 Go Home Lake 2-Jun-08 2008 11.8 7.4 32.4 7.4 Lake Huron-Cognashene Bay 28-May-14 2014 5.0 8 32.6 5.0 Grandview Lake 8-May-02 2002 12.8 8 32.6 8.0 Paint Lake 26-May-11 2011 6.0 9.6 32.6 6.0 Silver Lake ML 16-May-11 2011 37.2 23.2 32.8 23.2 Pigeon Lake 24-May-05 2005 22.2 13.8 33.0 13.8

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Sample TP1 TP2 % Mean TP Site Name Date Year (g/L) (g/L) Variance (g/L) Lake Joseph-North 29-Jul-10 2010 4.2 2.6 33.3 2.6 Lake Joseph-South 26-Aug-09 2009 17.8 11 33.4 11.0 Lake Joseph-North 14-May-14 2014 6.8 4.2 33.4 4.2 Tucker Lake 3-Jun-11 2011 5.6 9.2 34.4 5.6 Gullfeather Lake 3-Jun-11 2011 9.8 16.2 34.8 9.8 Otter Lake 21-May-10 2010 10.0 6 35.4 6.0 Little Lake Joseph 29-Jul-10 2010 4.2 7 35.4 4.2 Lake Joseph-North 15-Jul-08 2008 3.2 5.4 36.2 3.2 Fifteen Mile Lake 2-Jun-05 2005 10.2 6 36.7 6.0 Gilleach Lake 25-May-07 2007 8.8 15 36.8 8.8 Deer Lake 1-Jun-11 2011 14.0 8.2 36.9 8.2 Lake Joseph-Main 4-Aug-09 2009 16.4 9.6 37.0 9.6 Tucker Lake 23-May-07 2007 7.2 4.2 37.2 4.2 Cornall Lake 23-May-02 2002 14.4 24.8 37.5 14.4 Lake Joseph-Main 27-Aug-10 2010 3.8 2.2 37.7 2.2 Lake Joseph-Cox Bay 8-May-08 2008 11.4 6.6 37.7 6.6 Lake Joseph-Main 18-Jun-08 2008 6.6 3.8 38.1 3.8 Lake Joseph-Cox Bay 18-Jun-08 2008 13.4 7.6 39.1 7.6 Lake Joseph-Cox Bay 27-Aug-08 2008 10.6 6 39.2 6.0 Myers Lake 24-May-11 2011 9.6 17 39.3 9.6 Lake Joseph-South 8-May-13 2013 6.4 3.6 39.6 3.6 Fairy Lake - Main 19-May-06 2006 14.6 8.2 39.7 8.2 Lake of Bays - South Portage Bay 18-May-11 2011 10.4 5.8 40.2 5.8 Skeleton Lake 17-May-06 2006 3.0 5.4 40.4 3.0 Clearwater Lake GR 29-Apr-10 2010 4.4 8 41.1 4.4 Little Lake Joseph 26-Jun-14 2014 8.4 4.6 41.3 4.6 Lake Joseph-North 27-Aug-08 2008 4.4 2.4 41.6 2.4 Sixteen Mile Lake 5-Jun-14 2014 9.6 5.2 42.0 5.2 Nutt Lake 11-May-11 2011 15.0 8 43.0 8.0 Little Lake Joseph 23-Jul-09 2009 4.0 7.6 43.9 4.0 Clearwater Lake HT 12-May-03 2003 10.0 19 43.9 10.0 Lake Joseph-Main 17-Aug-09 2009 11.4 21.8 44.3 11.4 Lake Joseph-Hamer Bay 14-Aug-08 2008 11.2 5.8 44.9 5.8 Ril Lake 12-May-08 2008 15.6 8 45.5 8.0 Joseph River 24-May-05 2005 12.6 6.4 46.1 6.4 Lake Joseph-Main 26-Aug-09 2009 3.2 6.4 47.1 3.2 Lake Joseph-North 23-Jul-09 2009 4.4 9 48.5 4.4 Lake Joseph-Main 8-Jun-09 2009 3.2 6.6 49.1 3.2 Wolfkin Lake 9-May-03 2003 7.6 3.6 50.5 3.6 Lake Joseph-Cox Bay 4-Aug-09 2009 17.0 7.6 54.0 7.6 Mary Lake 23-May-07 2007 22.0 9.2 58.0 9.2 Longline Lake 20-May-11 2011 15.4 6.2 60.2 6.2 Lake Joseph-Main 14-Aug-08 2008 4.0 10.2 61.7 4.0 Tasso Lake 25-May-11 2011 6.2 18 69.0 6.2 Lake Joseph-South 17-Aug-09 2009 47.4 16.2 69.4 16.2 Lake Joseph-Hamer Bay 29-Jul-10 2010 8.2 2.8 69.4 2.8 Little Lake Joseph 26-Aug-09 2009 23.4 7.6 72.1 7.6 Joseph River 27-Aug-10 2010 69.8 22.2 73.2 22.2

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Sample TP1 TP2 % Mean TP Site Name Date Year (g/L) (g/L) Variance (g/L) Little Lake Joseph 17-Aug-09 2009 6.0 19.8 75.6 6.0 Lake Joseph-South 4-Aug-09 2009 16.0 54 76.8 16.0 Lake Joseph-South 15-Jul-08 2008 18.8 5 82.0 5.0 Lake Joseph-Hamer Bay 4-Aug-09 2009 22.4 5.8 83.2 5.8 Lake Joseph-South 8-Jun-09 2009 11.2 2.8 84.9 2.8 Lake Joseph-North 26-Aug-09 2009 15.6 3.8 86.0 3.8 Lake Joseph-Cox Bay 26-Aug-09 2009 4.4 19.6 89.6 4.4 Bruce Lake 24-May-05 2005 9.8 59.2 101.2 9.8 Lake Joseph-South 6-Jul-09 2009 7.6 51.6 105.1 7.6 Little Lake Joseph 14-Aug-13 2013 5.2 36 105.7 5.2 Little Lake Joseph 11-Jun-14 2014 68.0 7.6 113.0 7.6 Lake Joseph-North 11-Jun-14 2014 50.8 4.6 117.9 4.6 Lake Joseph-Cox Bay 2-Jun-10 2010 2.4 31.6 121.5 2.4 Joseph River 6-Aug-14 2014 130.0 8.6 123.9 8.6 Solitaire Lake 7-May-13 2013 0.2 4.6 129.6 4.6 Lake Joseph-North 6-Aug-14 2014 107.0 3.8 131.7 3.8 Lake Joseph-Cox Bay 6-Aug-14 2014 166.0 4.8 133.5 4.8 Brooks Lake 7-May-13 2013 15.6 0.4 134.4 15.6 Lake Joseph-Cox Bay 27-Aug-10 2010 0.2 31.8 139.7 31.8 Notes: Purple highlight represents the contaminated sample. Suspect low values are highlighted in orange and were removed from further analysis.

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Table 2. List of Outliers (2000-2014)

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3. Average TP Concentration and Trends

The annual, 10-year (2005-2014) and long term (2000-2014) spring TP are presented in Table 3 for each sampling site (n=196). These values represent spring concentration for all sites except those sampled in Lake Joseph and Little Lake Joseph since 2008, which represent spring/summer averages.

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 2000-2014. 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.

There was a statistically significant (p<0.10) decreasing trend in total phosphorus concentrations in three of the 190 lakes tested (Clark Lake, Mirror Lake and Tackaberry Lake, Figure 1). None of the lakes displayed a significant increasing trend (p<0.10). These results contrast those reported in HESL (2013) where phosphorus concentrations (2000-2012) 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 2000-2012 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 ouliers that can have an undue influence on trend analysis based on such few data points.

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Table 3. Total Phosphorus Concentration in District of Muskoka Lakes (n=196)

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Figure 1. Total phosphorus concentrations of lakes with a significant (p<0.10) decreasing phosphorus trend (2000-2014).

TKR Attach.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

Appendix F. List of MNR Lake Trout Lakes Within the District Municipality of Muskoka.

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J150074 , District Municipality of Muskoka Revised Water Quality Mode l and Lake System Health Program

ManagementType

AtCapacity?

Municipality

Lake name Lake Designation

Township Comment

Bella Lake Sinclair Lake of Bays Natural Yes Bigwind Lake Oakley Bracebridge PGT Yes Blue Chalk Lake Ridout Lake of Bays Natural Yes Bonnie Lake Macaulay Bracebridge Natural Yes 2012 list indicated possible candidate for future removal from list; no change being considered currently Buck Lake (Green) Sinclair Lake of Bays Natural Yes Camp Lake Finlayson Lake of Bays Natural No Clear Lake (Oakley Twp.) Oakley Bracebridge Natural Yes Clearwater Lake Morrison Gravenhurst Natural Yes Dotty Lake (Long) Finlayson Lake of Bays PGT Yes Fairy Lake Brunel Huntsville PGT No Fifteen Mile Lake Franklin Lake of Bays Natural Yes Harp Lake Chaffey Huntsville PGT Yes Jerry Lake Sinclair Lake of Bays Natural Yes Joseph, Lake Medora Muskoka Lakes Natural No Lake Of Bays Franklin Lake of Bays, Huntsville Natural No Margaret Lake Ridout Lake of Bays Natural Yes Mary Lake Stephenson Huntsville PGT No Muskoka, Lake Muskoka Muskoka Lakes, Bracebridge, Gravenhurst Natural No Oxbow Lake Finlayson Lake of Bays PGT Yes Peninsula Lake Franklin Lake of Bays, Huntsville PGT Yes Pine Lake Oakley Bracebridge Natural Yes Raven Lake Ridout Lake of Bays Natural No Lake located mostly in Haliburton County and is administered by Bancroft District MNRF Rebecca Lake Sinclair Lake of Bays PGT Yes Red Chalk Lake Ridout Lake of Bays Natural Yes Rosseau, Lake Cardwell Muskoka Lakes Natural No Shoe Lake Ridout Lake of Bays PGT Yes 2015 ER decision to retain designation Skeleton Lake Watt Muskoka Lakes, Huntsville Natural No Solitaire Lake (Clear) Sinclair Lake of Bays Natural Yes South Tasso Lake (Blue) Finlayson Lake of Bays Natural Yes Tasso Lake (North Tasso) Finlayson Lake of Bays Natural Yes 2012 list indicated possible candidate for future removal from list; no change being considered currently Vernon, Lake Stisted Huntsville PGT No Young Lake Watt Muskoka Lakes PGT Yes Updated January 2016 Updates were provided by MNRF and include: Removal of (Long) Cardwell Lake in response to ER submission and addition of Raven Lake which had been inadvertently omitted from previous lists. (email: Steve Scholten, Management Biologist, MNRF High Falls, January 14, 2016)

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