Evaluating the Cost-Effectiveness of Wetland Restoration Scenarios in the Lynn River Subwatershed of Norfolk County, Southern Ontario

by Laura Hopkins

A Thesis presented to The University of Guelph

In partial fulfilment of requirements for the degree of Master of Science in Geography

Guelph, Ontario, Canada © Laura Hopkins, December, 2019

ABSTRACT EVALUATING THE COST-EFFECTIVENESS OF WETLAND RESTORATION SCENARIOS IN THE LYNN RIVER SUBWATERSHED OF NORFOLK COUNTY, SOUTHERN ONTARIO

Laura Hopkins Advisor: University of Guelph, 2019 Dr. Wanhong Yang

This research applies an integrated modelling approach, incorporating GIS, economic, and hydrologic modelling to evaluate the cost-effectiveness of wetland restoration scenarios in the 140 km2 Lynn River subwatershed in southern Ontario. Results from the Integrated

Modelling for Watershed Evaluation of BMPs-Wetland (IMWEBs-Wetland) model show that under the historical climate scenario, wetland restoration decreases peak flow at the subwatershed outlet by an average 1.23 percent, and that individual wetland restoration locations retain local flows by an average 85 percent. The effects of low flow increases at the subwatershed outlet are not pronounced. The annual costs of wetland restoration were estimated with average opportunity costs, amortized restoration administration and engineering costs, and average inconvenience costs. The spatial distribution of wetland restoration cost-effectiveness shows locations that should be targeted for wetland restoration mainly due to their large contribution to peak flow decreases at the subwatershed outlet as well as low economic costs.

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ACKNOWLEDGEMENTS I would like to express my deepest appreciation to my advisor Dr. Wanhong Yang, of the Department of Geography, Environment and Geomatics, who provided me with tremendous support and wise words of encouragement, exactly when I needed support and encouragement the most.

I would also like to thank my committee member, Dr. Glenn C Fox, of the Department of Food, Agriculture and Resource Economics, who greatly improved the contents of this thesis with his profound knowledge of natural resource economics.

I would also like to thank Dr. Yongbo Liu and Dr. Hui (Shawn) Shao of the Department of Geography, Environment and Geomatics, who patiently explained hydrologic modelling concepts and regularly met with me to improve my understanding of hydrologic modelling.

I would like to thank Adam Bonnycastle, of the Department of Geography, Environment and Geomatics for sharing his immense GIS knowledge with me. Moreover, I would like to thank Dr. John Lindsay, of the Department of Geography, Environment and Geomatics for thoroughly explaining complex remote sensing techniques to me. I would also like to thank Stephanie Drayer, the ALUS Norfolk Coordinator, who provided me with practical knowledge of wetland restoration in Norfolk County. I also want to thank my classmates, friends, and family for their understanding and support throughout the completion of my thesis. Furthermore, I could not have completed this thesis without the immense support of my dearest, Jany, who listened without judgement to my countless concerns and relentlessly encouraged me to continue writing.

Finally, I would like to express my deepest gratitude to the Social Sciences and Humanities Research Council, the University of Guelph, the Department of Geography, Environment and Geomatics, the Arthur D. Latornell Fund, and the Canadian Water Resources Association. Their generous funding has enabled me to attend and present at several conferences, travel to my study watershed, and finish my thesis without the worry of financial stress.

TABLE OF CONTENTS Abstract ...... ii Acknowledgments...... iii Table of Contents ...... iv List of Tables ...... vii List of Figures ...... ix List of Abbreviations and Acronyms ...... xi Chapter 1 Introduction ...... 1 1.1 Research Background ...... 1 1.2 Research Context ...... 3 1.3 Research Purpose and Objectives ...... 4 1.4 Thesis Outline ...... 5 Chapter 2 Literature Review ...... 6 2.1 Wetland Policies and Programs...... 6 2.1.1 Wetland Conservation Policies and Regulations ...... 6 2.1.2 Wetland Conservation Easements and Costs Share Programs ...... 8 2.2 Wetland Models ...... 11 2.2.1 Wetland Model Comparison ...... 11 2.2.2 Wetland Model Application ...... 14 2.3 Identifying Wetland Restoration Sites ...... 15 Chapter 3 Methods ...... 20 3.1 Research Overview ...... 20 3.2 Study Area ...... 20 3.2.1 Lynn River Black Creek Watershed ...... 20 3.2.2 Lynn River Subwatershed ...... 22 3.3 Wetland Restoration Site Identification ...... 23 3.3.1 Digital Elevation Model Creation ...... 23 3.3.2 Whitebox GAT Sink function ...... 24 3.3.3 Fill Depressions & Difference ...... 26 3.4 Data Preparation ...... 27 3.4.1 Data Preparation for IMWEBs Model ...... 27 3.4.2 Development of Climate Scenarios ...... 33

3.4.3 Data Preparation for Wetland Economic Model ...... 37 3.5 IMWEBs Model Setup, Calibration, and Validation ...... 42 3.6 Scenario Analysis ...... 45 3.6.1 Subwatershed Outlet Peak Flow Change...... 47 3.6.2 Subwatershed Outlet Low Flow Change ...... 52 3.6.3 Individual Wetlands Flow Retention ...... 53 3.6.4 Individual Wetlands Increase in Low Flow ...... 55 3.7 Wetland Economic Cost Estimation ...... 55 3.7.1 Opportunity Costs ...... 55 3.7.2 Wetland Restoration Administration and Engineering Costs ...... 57 3.7.3 Inconvenience Costs ...... 58 3.7.4 Annual Cost of Wetland Restoration ...... 59 3.8 Cost-Effectiveness Analysis ...... 62 3.9 Economic Sensitivity Analysis...... 64 Chapter 4 Results and Discussion ...... 65 4.1 IMWEBs-Wetland Calibration and Validation ...... 65 4.2 IMWEBs-Wetland Scenario Analysis ...... 68 4.2.1 Comparison of Streamflow under Historical Climate and Climate Change Scenarios 69 4.2.2 Subwatershed Outlet Peak Flow Change...... 71 4.2.3 Subwatershed Outlet Low Flow Change ...... 78 4.2.4 Individual Wetland Flow Retention ...... 80 4.2.5 Individual Wetland Low Flow Increase ...... 94 4.3 Cost-Effectiveness Analysis ...... 98 4.3.1 Annual Cost of Wetland Restoration ...... 99 4.3.2 Subwatershed Outlet Peak Flow Decrease Cost-Effectiveness Analysis ...... 101 4.3.3 Individual Wetland Peak Flow Decrease Cost-Effectiveness Analysis ...... 102 4.4 Economic Sensitivity Analysis...... 107 Chapter 5 Conclusion ...... 110 5.1 Limitations and Recommendations ...... 114 References ...... 116 Appendices ...... 125 Appendix A IMWEBs-Wetland Data Preprocessing ...... 125

A.1 DEM ...... 125 A.2 Landuse ...... 125 A.3 Crop Management ...... 126 A.4 Soil Data ...... 128 A.5 Streams ...... 131 A.6 Field Boundaries ...... 131 A.7 Existing Wetlands ...... 132 A.8 Climate Data ...... 133 A.9 Flow Data...... 135 A.11 Point Source Data ...... 136

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LIST OF TABLES

Table 3-1 The geospatial and observational data used in the IMWEBs-Wetland model ...... 28 Table 3-2 The 59 wetland restoration location identified in the Lynn River subwatershed with their contributing areas, maximum areas, maximum volumes, linear distance to the subwatershed outlet, and UTM NAD 17N coordinates ...... 29 Table 3-3 The 39 existing wetlands in the Lynn River subwatershed with their contributing areas, maximum areas, maximum volumes, and distance to the subwatershed outlet...... 32 Table 3-4 Climate scenarios for the Lynn River subwatershed IMWEBs-Wetland model ...... 33 Table 3-5 Mean precipitation, temperature, and wind speed of the two climate scenarios ...... 34 Table 3-6 Average percent of the Lynn River subwatershed covered by dominant crop types for years 2011-2017, expressed as a percentage of total land area ...... 39 Table 3-7 Data for the crop budget used to calculate average net return before land and overhead costs...... 42 Table 3-8 The six scenarios that will be analysed with the IMWEBs-Wetland model ...... 46 Table 3-9 Maximum flow at the subwatershed outlet for each year of simulation under the existing wetland scenario and the wetland restoration scenario for the historical climate scenario, and the difference and percent change between the maximum flow at the outlet under the two wetland scenarios ...... 49 Table 3-10 Maximum flow at the subwatershed outlet for each year of simulation under the existing wetland scenario and the wetland loss scenario for the historical climate scenario, and the difference and percent change between the maximum flow at the outlet under the two wetland scenarios ...... 50 Table 3-11 Probability distribution and return period preparation ...... 52 Table 3-12 Wetland restoration administration and engineering cost estimates for the Lynn River subwatershed ...... 57 Table 3-13 Ducks Unlimited Canada (2011) wetland restoration administration and engineering cost estimates for southern Ontario ...... 58 Table 3-14 Ten year average of 8 percent of machinery costs ...... 58 Table 3-15 Annual cost of wetland restoration for all 59 identified wetland restoration locations ...... 61 Table 4-1 IMWEBs-Wetland calibrated input parameters for the Lynn River subwatershed ...... 66 Table 4-2 Lynn River subwatershed IMWEBs-Wetland calibration and validation model bias, Nash-Sutcliffe coefficient, and coefficient of determination values ...... 66 Table 4-3 The six scenarios that will be analysed with the IMWEBs-Wetland model ...... 68 Table 4-4 Comparison of average, median, minimum, and maximum outlet daily streamflow under historical climate and climate change scenarios with existing wetlands ...... 70 Table 4-5 Average annual peak flow changes under historical climate scenario for existing wetlands, wetland restoration, and removal of all wetlands ...... 72 Table 4-6 Average annual peak flow reductions under climate change scenario for existing wetlands, wetland restoration, and removal of all wetlands ...... 74 Table 4-7 Historical climate scenario distribution of average peak flow decrease at the subwatershed outlet back to individual wetlands based on individual wetlands average flow retention values for wetland restoration locations ...... 82

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Table 4-8 Historical climate scenario distribution of average peak flow increase at subwatershed outlet back to individual wetlands based on wetlands average maximum flow retention values for existing wetlands ...... 84 Table 4-9 Climate change scenario distribution of average peak flow decrease at subwatershed outlet back to individual wetlands based on individual wetlands average flow retention values with wetland restoration locations ...... 89 Table 4-10 Climate Change scenario distribution of average peak flow increase at subwatershed outlet back to individual wetlands based on wetlands average maximum flow retention values with existing wetlands...... 91 Table 4-11 Average low flow increases during low flow periods (July-September) with historical climate scenario ...... 96 Table 4-12 Average flow increases during low flow periods (July-September) with climate change scenario ...... 98 Table 4-13 Subwatershed outlet peak flow decrease cost-effectiveness analysis with the average peak flow decrease, peak flow decrease with a 10 year return period, and the peak flow decrease with a 30 year return period, with the annual cost of restoring all 59 wetlands in the subwatershed ...... 102 Table 4-14 Individual wetland peak flow decrease cost-effectiveness analysis calculated with the average peak flow decrease at the subwatershed outlet distributed back to individual wetlands (expressed in m3/day) and annual cost of wetland restoration (expressed in $) under the historical climate and climate change scenarios ...... 104 Table 4-15 Sensitivity analysis of the four input costs for wetland restoration annual cost calculation ...... 108 Table 4-16 Sensitivity analysis of the interest rate and wetland lifespan used for calculating the annual cost of wetland restoration ...... 109 Table A-1 Lynn River subwatershed Landuse Lookup Preparation ...... 125 Table A-2 Crop management assumptions made for the Lynn River subwatershed IMWEBs- Wetland model ...... 127 Table A-3 Usersoil table created for the Lynn River subwatershed IMWEBs-Wetland model . 130 Table A-4 IMWEBs-Wetland required climate parameters and units, as well as ECCC stations used for each climate parameter and the dates with available data over the simulation period . 135 Table A-5 The ECCC climate stations used in the IMWEBs-Wetland model and their nearest ECCC climate station used to fill missing climate records ...... 135

LIST OF FIGURES Figure 3-1 The Lynn River Black Creek Watershed in Norfolk County of Southern Ontario, Canada...... 21 Figure 3-2 The Lynn River subwatershed landuse, streams, and calibration stations in Norfolk County, Ontario...... 22 Figure 3-3 The 59 identified wetland restoration locations in the Lynn River subwatershed ...... 26 Figure 3-4 Average annual precipitation of the two climate scenarios ...... 35 Figure 3-5 Average monthly precipitation of the two climate scenarios ...... 35 Figure 3-6 Average annual temperature of the two climate scenarios ...... 36 Figure 3-7 Average monthly temperature of the two climate scenarios ...... 36 Figure 3-8 Average annual wind speed of the two climate scenarios ...... 37 Figure 3-9 Average monthly wind speed of the two climate scenarios ...... 37 Figure 3-10 A representative wetland restoration location flow in and flow out under a storm event ...... 54 Figure 3-11 The Lynn River subwatershed average annual return before land and overhead costs ...... 56 Figure 4-1 Flow calibration daily comparison of measured flow to the calculated flow for 2011- 2017, along with daily precipitation and average air temperature ...... 67 Figure 4-2 Flow validation daily comparison of measured daily flow to the calculated flow for 2004-2010, along with daily precipitation and average air temperature ...... 67 Figure 4-3 Outlet daily streamflow with existing wetlands under historical climate scenario with average temperature and precipitation ...... 70 Figure 4-4 Outlet daily streamflow with existing wetlands under climate change scenario with average temperature and precipitation ...... 70 Figure 4-5 Annual peak flows under the historical climate scenario with no wetlands, with existing wetlands, and with wetland restoration at the subwatershed outlet ...... 73 Figure 4-6 Annual peak flows under climate change scenario with no wetlands, with existing wetlands, and with wetland restoration at the subwatershed outlet ...... 75 Figure 4-7 Frequency distribution of peak flow reductions at the Lynn River subwatershed outlet due to wetland restoration for the 31 year simulation under the historical climate scenario ...... 77 Figure 4-8 Frequency distribution of peak flow reductions at the Lynn River subwatershed outlet due to wetland restoration for the 31 year simulation under the climate change scenario ...... 77 Figure 4-9 Wetland restoration average annual flow retention with historical climate scenario . 85 Figure 4-10 Existing wetlands average annual flow retention with historical climate scenario .. 85 Figure 4-11 The Lynn River subwatershed existing wetlands and wetland restoration locations flow retention under historical climate scenario. Data are classified with Natural Breaks (Jenks) classification...... 87 Figure 4-12 Wetland restoration average annual flow retention with climate change scenario ... 92 Figure 4-13 Existing wetlands average annual flow retention with climate change scenario ...... 92 Figure 4-14 The Lynn River subwatershed existing wetlands and wetland restoration locations flow retention under climate change scenario. Data are classified with Natural Breaks (Jenks) classification...... 93

Figure 4-15 Average flow increase during low flow periods from July-September under historical climate scenario. Orange bars indicate wetland restoration locations, blue bars indicate existing wetlands...... 95 Figure 4-16 Average flow increase during low flow periods from July-September under climate change scenario. Orange bars indicate wetland restoration locations, blue bars indicate existing wetlands ...... 97 Figure 4-17 The Lynn River subwatershed annual wetland restoration costs, expressed in $/ha/year ...... 100 Figure 4-18 Individual wetlands peak flow decrease cost-effectiveness analysis calculated with the average peak flow decrease at the subwatershed outlet distributed back to individual wetlands (expressed in m3/day) and annual cost of wetland restoration (expressed in $) under the historical climate scenario. Data is classified with Natural Breaks (Jenks) classification...... 106 Figure 4-19 Individual wetlands peak flow decrease cost-effectiveness analysis calculated with the average peak flow decrease at the subwatershed outlet distributed back to individual wetlands (expressed in m3/day) and annual cost of wetland restoration (expressed in $) under the climate change scenario. Data is classified with Natural Breaks (Jenks) classification...... 107 Figure A-1 The Lynn River subwatershed soil types ...... 131 Figure A-2 Climate station locations for the Lynn River subwatershed IMWEBs-Wetland model ...... 134 Figure A-3 Flow station for the Lynn River subwatershed IMWEBs-Wetland model ...... 136 Figure A-4 Simcoe Municipal Wastewater Treatment Plant location in the Lynn River subwatershed ...... 137

LIST OF ABBREVIATIONS AND ACRONYMS

ALUS Alternative Land Use Services DEM Digital Elevation Model DUC Ducks Unlimited Canada ECCC Environment and Climate Change Canada GHG Greenhouse gas GIS Geographic Information System GM Genetically Modified HEW Hydrologic Equivalent Wetland IMWEBs Integrated Modelling for Watershed Evaluation of BMPs LiDAR Light Detection and Ranging LPRCA Long Point Region Conservation Authority NSE Nash-Sutcliffe Efficiency coefficient OMAFRA Ontario Ministry of Agriculture, Food and Rural Affairs PRECIS Providing Regional Climates for Impact Studies R2 Coefficient of determination RCP Representative Concentration Pathways SRES Special Report on Emissions Scenarios SUSTAIN System for Urban Stormwater Treatment and Analysis Integration SWAT Soil and Water Assessment Tool SWIM Soil and Water Integrated Model TIF Tagged Information File TIN Triangular irregular network

Chapter 1 Introduction

1.1 Research Background Canada has lost an estimated 70 percent of its wetlands since post-European settlement (Canadian Wetland Inventory, 2008). Most of the wetlands lost in Canada were drained during or shortly after European settlement to increase arable land available for agriculture and to reduce diseases (Government of Canada, 1991). Historically, wetlands were likely drained because the marginal benefit of increasing arable land available for agriculture and reducing diseases was much greater than the marginal benefit of the existing wetlands. However, as wetlands became scarcer, it is possible that the marginal benefits of the ecosystem services that wetlands provide became greater than the marginal benefit of increasing food production and reducing diseases. Wetlands provide important hydrologic functions in the landscape. Wetlands can reduce the incidence of local flooding by attenuating excess water until groundwater recharge can occur naturally (Javaheri et al. 2014). Moreover, wetlands can increase low flows by slowly releasing stored water (Bullock et al. 2003). Thus, wetlands act as a type of hydrologic buffer, decreasing peak flows under wet conditions, and increasing low flows under dry conditions. Decreasing peak flows is important during springtime snowmelt in Ontario, as this is typically when peak flows are highest throughout the province. Decreasing springtime peak flows can also decrease the length of time agricultural fields are spent flooded or fully saturated in the springtime (Haak, 2008). This can allow farmers with historically flooded or saturated fields to plant earlier. Increasing low flows is important during the summertime, as flows are typically lowest during the summer due to higher temperatures, evapotranspiration, and water use (Haak, 2008). Increasing low flows in streams is important ecologically for the passage of fish and other aquatic life. Moreover, increasing low flows is also important for water quality, as effluent and treated wastewater that is discharged to a river will become more dispersed if water in the river is flowing rather than stagnant (Kennedy et al. 1986). Wetlands provide a variety of ecosystem services in addition to decreasing peak flows and increasing low flows. Wetland plant species have been shown to absorb excess nutrients, resulting in improved downstream water quality (Darwiche-Criado et al. 2016). Wetlands adjacent to waterways and waterbodies can also reduce shoreline erosion due to the interlocking root systems that wetland plant species have, which stabilize soil at the shoreline, enhancing soil

accumulation through sediment trapping (Heimlich et al. 1998). Wetlands can reduce downstream sediment loading because wetland vegetation decreases the flow of water, causing sediments to be deposited within the wetland (Martinez-Martinez et al. 2015). Lastly, in recent years, the ability of wetlands to sequester carbon has been identified as a means of reducing atmospheric carbon dioxide concentrations associated with global climate change (Whiting et al. 2001). In addition to these ecosystem services, wetlands provide many other societal benefits, such as high biodiversity, important habitat for various fish and wildlife species, bird watching, fishing, aesthetics, and cultural benefits (Cortus et al. 2011). Heimlich et al. (1998) suggest that wetland ecosystem services are underproduced because private interests do not equal social interests. Typically, the private owner of a wetland would attain greater value from the wetland if it were drained and developed. Thus, wetland conservation policies and wetland restoration programs were implemented throughout Canada to protect and restore wetlands. Most wetland conservation policies in Canada aim for no net loss of wetlands, by disincentiving wetland conversion by requiring approvals for wetland drainage, and compensation when wetlands are lost or degraded (Austen and Hanson, 2007). This policy approach raises important ethical problems often referred to as regulatory takings because private property is designated for conservation by government regulations. A regulatory taking occurs when the government takes control of private property in order to control its development and no compensation is provided to the landowner (Coatsworth, 2012). Forced conservation designation can create perverse incentives for private property owners to drain existing wetlands on their property prior to receiving the designation because under this policy approach existing wetlands on private properties can be viewed as an environmental liability. To address the shortcomings of the regulatory takings approach, wetland restoration programs were implemented to encourage the creation or restoration of wetlands on private properties. Wetland restoration programs often provide financial compensation for farmers to restore and maintain wetlands on their property. Thus, wetland restoration programs turn what can be perceived as an environmental liability under the regulatory takings approach, into an environmental asset. For instance, the Alternative Land Use Services (ALUS), operating in Norfolk County, compensates farmers for the ecosystem services they have created due to the implementation of beneficial management practises (BMPs), such as wetland restoration (ALUS, 2019a).

ALUS is based on the club goods model. Buchanan (1965) defines club goods as goods whose benefits and costs are shared between members of a given sharing arrangement or association. Moreover, Cornes and Sandler (1986) define a club as a voluntary group that pools the resources of its members and provides excludable benefit to those members. Thus, club goods are traditionally defined as nonrival and excludable, such as cable television in which consumption by one member does not rival the consumption of another member and consumption is excludable to non-members. Coatsworth (2012) explains that in the club goods model, utility from consuming club goods is derived from the number of other individuals with whom consumption is shared. Fox (2008) extends the traditional club goods model to include voluntary associations that provide financial support for environmental goods and services, and this support generates benefits that are not exclusive to club members. Moreover, Coatsworth (2012) explains that the provision of club goods provides non-exclusive benefits to non-members while providing exclusive benefits to the membership base because full exclusion of club goods is costly or near impossible. Other examples of environmental goods and services provided under the club goods model include the programs Ducks Unlimited and Delta Waterfowl, which use club revenues to compensate landowners in exchange for the maintenance of wetland areas (Fox, 2008). 1.2 Research Context Norfolk County was one of the first counties in Ontario to participate in the ALUS pilot project in 2007 (ALUS, 2019a). The pilot was successful, and Norfolk County now has the longest continuously running ALUS program in Canada (ALUS, 2019a). Norfolk County is a predominantly agricultural region, located on the shoreline of Lake Erie in southern Ontario. It is estimated that 82 percent of the wetland area in Norfolk County has been lost since post- European settlement (DUC, 2010). Moreover, the Long Point Region Conservation Authority (LPRCA) states that the Lynn River Black Creek Watershed has “Poor” wetland cover (LPRCA, 2018). This determination was based on Ministry of Natural Resources and Forestry GIS data and Environment Canada’s recommendation that a healthy watershed in southern Ontario should contain at least 10 percent wetland cover (LPRCA, 2018). Thus, there is currently a need for wetland restoration in the Lynn River subwatershed. ALUS wetland restoration project enrollment is voluntary, and open to any farmer that wants to improve their environmental stewardship (ALUS, 2019a). However, not all restored

wetlands will provide equal magnitudes of ecosystem services, due to differences in wetland drainage area characteristics such as slope, soil, and land use, as well as wetland features such as size, volume, and position relative to the stream network (Yang et al. 2016a). Similarly, the economic costs of wetland restoration, such as the opportunity costs of the forgone cropping return, vary between farmers due to differences in land productivity (Yang et al. 2016a). Due to this spatial heterogeneity, wetland restoration efforts that are spatially targeted to specific agricultural locations with high environmental outcomes relative to economic costs will be more cost-effective than non-targeted wetland restoration efforts. Evaluating the cost-effectiveness of wetland restoration programs often requires an integrated modelling approach. The integrated modelling approach, incorporating GIS, economic, and hydrologic modelling, has been successfully employed to evaluate the cost- effectiveness of wetland restoration scenarios in a prairie watershed in Manitoba (Yang et al. 2016a). This research applies the integrated modelling approach, to model the spatial heterogeneity of both the flow outcomes of wetland restoration, and the economic costs of wetland restoration, to evaluate the cost-effectiveness of wetland restoration scenarios in the Lynn River subwatershed in Norfolk County of southern Ontario. 1.3 Research Purpose and Objectives The purpose of this research is to evaluate the cost-effectiveness of wetland restoration scenarios in the Lynn River subwatershed of Norfolk County in southern Ontario. Specifically, this research has 3 related objectives: 1. Estimate the flow outcomes of wetland restoration in the Lynn River subwatershed in terms of peak flow decreases and low flow increases. 2. Estimate the economic costs of wetland restoration scenarios in the Lynn River subwatershed. 3. Evaluate the cost-effectiveness of wetland restoration scenarios in the Lynn River subwatershed.

1.4 Thesis Outline This thesis consists of five chapters. Chapter one contains the introduction and provides background on historical wetland loss and the wetland restoration program operating in Norfolk County, as well as the research purpose and objectives. Chapter two provides a literature review of wetland conservation policies and wetland restoration programs, hydrologic models used for simulating the impact of wetland restoration and/or wetland loss, and techniques for identifying potential wetland restoration locations. Chapter three provides the methods used in this research, including an overview of the Lynn River subwatershed, methods used for calculating peak flow decreases and low flow increases due to wetland restoration, and methods used for calculating the cost-effectiveness of wetland restoration. Chapter four contains the model calibration and validation results, wetland scenario simulation results, and cost-effectiveness analysis results. Chapter five presents a summary of the thesis as well as limitations and recommendations for future research opportunities.

Chapter 2 Literature Review

2.1 Wetland Policies and Programs Historically, policies were first implemented to promote the drainage of wetlands, and later to promote the conservation of wetlands. The crown promoted the drainage of wetlands to increase westward settler expansion throughout the European settlement of North America (Heimlich et al. 1998). Moreover, in the United States, the Swamp Land Act of 1850 allowed states to gain ownership of previously federally owned swampland, contingent on the agreement that states would drain the land to establish productive agricultural land (Bogue, 1951). Historically, wetlands were likely drained because the marginal benefit of increasing arable land available for agriculture and reducing diseases was much greater than the marginal benefit of the existing wetlands. However, as wetlands became scarcer, it is possible that the marginal benefits of the ecosystem services that wetlands provide became greater than the marginal benefit of increasing food production and reducing diseases. Thus, public choice theory policies were implemented to decrease the net returns derived from wetland conversion, by disincentivizing conversion or removing previous incentives for wetland conversion (Hansen et al. 2015). 2.1.1 Wetland Conservation Policies and Regulations Most wetland conservation policies are based on the no net loss objective, with the stated goal of reaching an overall national equilibrium between wetland losses and gains (Heimlich et al. 1998). Relatively few of the marginal benefits of wetland conservation can be captured by the individual landowner (Hansen et al. 2015). Therefore, the marginal benefit individual landowners acquire from converting a hectare of wetland to cropland may be relatively high compared to the marginal benefit of wetland conservation, leading to the loss of privately-owned wetlands (Hansen at al. 2015). To address the discrepancies between private and social interests, wetland conservation policies and wetland restoration programs have been implemented throughout Canada. 2.1.1.1 Examples of Wetland Conservation Policies The Atlantic Provinces in Canada have all implemented wetland conservation policies with the stated goal of no net loss of wetlands and wetland functions. The Nova Scotia Wetland Conservation Policy requires approval for wetland alteration or drainage, and wetland

compensation is required where wetlands have been altered or drained to ensure the stated goal of no net loss is achieved (Government of Nova Scotia, 2011). Similarly, the stated aim of the New Brunswick Wetland Conservation Policy is no net loss of wetlands by requiring approval for wetland alteration, and wetland compensation measures, such as restoring, creating, or enhancing wetlands elsewhere, when wetland alteration measures must be taken (Government of New Brunswick, 2012). The stated aim of the Wetland Conservation Policy for Prince Edward Island is also a no net loss objective, where wetlands should be avoided unless the project is deemed to be in the greater public interest (Government of Prince Edward Island, 2007). The policy states that if a project is deemed to be in the greater public interest, the proponent is required to provide funding or conduct work to replace the lost wetland (Government of Prince Edward Island, 2007). Lastly, the Newfoundland and Labrador Policy for Development in Wetlands employs four policy classifications to outline which types of wetlands can be developed. These classifications include, developments not permitted, developments requiring written permission, implementation of mitigative measures, and restoration measures (Newfoundland and Labrador Municipal Affairs and Environment, 2017). The stated objective of these classifications is to allow development in wetlands that will not adversely affect the surrounding water quality and quantity (Newfoundland and Labrador Municipal Affairs and Environment, 2017). The stated aim of the Alberta Wetland Policy is to determine wetland values based on the ecological goods and services the wetlands provide, aiming to ensure that the highest valued wetlands are protected (Government of Alberta, 2013). When wetlands must be altered or destroyed, wetland replacement projects must be undertaken by the approval holder. Replacement can include either in-lieu fee payment, whereby the approval holder pays financial restitution for wetland loss, or permittee-responsible replacement, whereby the approval holder engages in active replacement projects (Government of Alberta, 2013). The Alberta Wetland Policy recognises the spatiality of wetland ecological goods and services and encourages restorative efforts to be local. Currently, wetland stewardship is promoted through voluntary activities, however a range of initiatives are under development to further encourage wetland stewardship (Government of Alberta, 2013). In Québec, Bill 132 An Act respecting the conservation of wetlands and bodies of water was implemented in June of 2017 (National Assembly of Québec, 2017). The Act is the first of

its kind in Canada and has the stated objective of no net loss of both wetlands and bodies of water. The Act requires that regional wetlands and bodies of water plans be created, in order to guide development and conservation within that region to achieve no net loss (National Assembly of Québec, 2017). Moreover, under the Act, the Minister will implement programs to promote the restoration, or creation, of wetlands and bodies of water. Lastly, the requirement for reporting is included to increase accountability and ensure the stated objective of no net loss is being achieved (National Assembly of Québec, 2017). The Canadian territories either do not have or have underdeveloped wetland policies compared to the provinces. Neither Yukon nor Nunavut currently have wetland conservation policies (Ducks Unlimited Canada, 2017). However, the Northwest Territory Water Stewardship Strategy was released in 2010, promoting the stewardship of water within the territory (Government of Northwest Territories, 2016). The Strategy’s stated objectives are to maintain water quantity, quality, and rates of flow, ensure clean, safe drinking water for all residents, and ensure aquatic ecosystems are healthy and diverse (Government of Northwest Territories, 2016). Though wetlands are not explicitly mentioned in the strategy, water stewardship efforts will inevitably include some level of wetland conservation. The policies and regulations outlined thus far aim to protect wetlands prior to their destruction. However, relying strictly on policy and regulation, no incentive is created for individuals or firms to adopt additional wetland conservation stewardship activities (EPA, 2017). Financial incentives in the form of compensation can be used to encourage the restoration or creation of new wetlands, instead of merely discouraging the destruction of existing wetlands, as is the goal with policies and regulations. The next category of wetland conservation programs, conservation easements and cost shares, employ financial compensation to promote the conservation and restoration of wetlands. 2.1.2 Wetland Conservation Easements and Costs Share Programs Compensation based programs have been implemented to enhance the returns that landowners receive from wetland conservation and restoration efforts (Guerra, 2010). For instance, cost shares provide financial assistance to landowners for their wetland restoration projects (OMAFRA, 2016a). Moreover, landowners can enter a conservation easement with a conservation body that limits certain types of land uses on that property (OMAFRA, 2016b). These voluntary programs offer wetland landowners compensation to conserve or restore

wetlands on their property. The economic theory behind compensation based programs requires the compensation to be sufficient enough for the farmer to agree to sustain any necessary sacrifices, inclusive of the costs of conversion. Differences in easement contract lengths may affect the types of environmental benefits created because wetland ecosystems can take years to fully recover (Hansen et al. 2015). Thus, the annual ecosystem services provided by a newly restored wetland may increase over time as the wetland reaches full maturity. Moreover, competitive program enrollment can further increase the cost-effectiveness of wetland conservation programs (Hansen et al. 2015). Lastly, programs designed to spatially target locations for wetland restoration with high environmental benefits relative to economic costs lead to increased cost-effectiveness (Hansen et al. 2015). 2.1.2.1 Examples of Compensation Programs The Saskatchewan Wetland Policy is led by the Saskatchewan Wetland Conservation Corporation with the stated objective of ensuring government departments adjust their policies and programs to promote wetland conservation (Stewart et al. 1999). The government of Saskatchewan has promoted conservation easements as a tool for wetland conservation and stewardship. The Canada-Ontario Environmental Farm Plan is composed of voluntary assessments which the farmer prepares to determine environmental strengths and weaknesses, which is then used to create a plan with the stated goal to improve environmental conditions (OMAFRA, 2016a). Upon completion of an Environmental Farm Plan, government funded cost share programs are available to assist in project implementation (OMAFRA, 2016a). Additionally, the Government of Ontario also promotes the use of conservation easements to further conserve wetland ecosystems in agriculture (OMAFRA, 2016b). Similarly, the stated aim of the Canada- British Columbia Environmental Farm Plan is to use environmental risk assessments to create recommendations to improve farm sustainability (Government of British Columbia, 2010). Funding is available to aid the farmer in implementing these recommendations through the Beneficial Management Practices Program. The Manitoba Riparian Tax Credit is the first of this kind in Canada, and provides a financial incentive, in the form of a tax credit, for the stated goal of helping agricultural landowners to improve their management of rivers and stream banks (Government of Manitoba, 2017a). Moreover, the Government of Manitoba (2017b) aims to work co-operatively with

various non-governmental organizations, such as Ducks Unlimited Canada, to establish Manitoba’s Protected Areas Initiative. Under this initiative, landowners are encouraged to enter conservation easements in order to increase the area of protected wetlands throughout Manitoba (Government of Manitoba, 2017b). The United States Federal Wetland Reserve Program is a voluntary program with the sated goal of allowing landowners to sell a conservation easement or enter into a cost share restoration agreement with the United States Department of Agriculture to restore and protect wetlands on their property (USDA, 2017). Four incentive structures are offered; permanent easement, 30-year easement, restoration cost share agreement, and a 30-year contract available for tribal lands (USDA, 2017). The stated aim of having four different incentive structures is to allow the Wetland Reserve Program to be used by a broad range of landowners across the United States. 2.1.2.2 Alternative Land Use Services (ALUS) Canada The Alternative Land Use Services (ALUS) is a compensation based program, designed to encourage the production of ecosystem goods and services on private farmlands. ALUS is based on the belief that farmers and ranchers are the stewards of the land, and this puts farmers and ranchers in the ideal position to produce ecosystem goods and services from their farmlands (ALUS, 2019b). ALUS founders believe that environmental stewardship should be a cost share activity, because many of the ecosystem goods and services that benefit the public are found on private lands (Rosenburg, 2010). ALUS uses scientific principles to produce measurable ecosystem goods and services and uses independent monitoring and auditing for verification (ALUS, 2019b). ALUS aims to be transparent and accountable, while complimenting existing agri-environmental policies, such as the Environmental Farm Plan (Rosenburg, 2010). ALUS was established in 2006 and is currently operating in six Canadian provinces, with the longest running ALUS project in Norfolk County (ALUS, 2019a). Norfolk County has over 160 farms and more than 525 hectares of land enrolled in the ALUS program (ALUS, 2019a). Norfolk County was one of the first counties in Ontario to participate in the ALUS pilot project in 2007 (ALUS, 2019a). Some examples of the ALUS projects that have been undertaken in Norfolk County include creation and enhancement of wetlands, riparian buffer establishment, creation of Tallgrass Prairie and Oak Savannah, reforestation of native Carolinian species, pollinator hedgerows, and grassed windbreaks (Rosenburg, 2010).

Originally, the objective of the Norfolk ALUS pilot project was to showcase the ALUS approach, in hopes that it would be adopted as provincial policy backed with taxpayer funding (Rosenburg, 2010). Initially, ALUS was envisioned as a single payer policy, with funding coming from government. However, due to economic downturn (in 2008) it was difficult to provincially implement a new and costly program. Thus, diverse funding sources were developed, mostly from non-governmental organizations (NGOs), such as Delta Waterfowl, government grants, private organizations, and individuals (Rosenburg, 2010). ALUS pays farmers for the ecosystem goods and services they have produced due to the implementation of BMPs, such as wetland restoration (ALUS, 2019a). ALUS operates with two financial incentive structures. First, grants are given to implement all restoration projects as ALUS pays for the materials, implementation, and labour (Guerra, 2010). Second, landowners receive an annual payment, which recognizes the opportunity cost of removing lands from agricultural production (Guerra, 2010). Landowners typically receive annual payments of $150/acre/year, which is based on average annual rental rates of agricultural land in Norfolk County (Guerra, 2010). ALUS enrollment is voluntary and open to any farmer that wants to improve their environmental stewardship (ALUS, 2019a). Farmers interested in participating in the ALUS program must fill in a one-page expression of interest form. Their application then goes to the approval committee, where they ensure that all requirements are met, such as the capping requirement that a maximum of 20 percent of a farmer’s land can be signed up for the program (Rosenburg, 2010). The program coordinator then does an onsite visit, and together they draft a plan for the farm (Rosenburg, 2010). Farmers who submit expression of interest in the program are selected on a first come first served basis until the maximum potential participation is reached. ALUS coordinators have used both targeted approaches for selecting participants, as well as hosting workshops and allowing interested participants to approach them (Rosenburg, 2010). 2.2 Wetland Models 2.2.1 Wetland Model Comparison Hydrologic models are used to assess the effects of wetland restoration scenarios and to predict the impact wetland conservation efforts will have on the watershed. Various hydrologic models are used to characterize wetland processes and evaluate wetland effects. Most of the

wetland models currently in use employ a semi-distributed model structure, which clumps wetlands together for parametrization (Golden et al. 2014). However, due to the heterogeneity of wetland functions, it is unrealistic to characterize wetlands using a semi-distributed model that clumps wetlands together. Various attempts have been made to address this problem to improve the characterization of wetlands within hydrologic models. The Soil and Water Assessment Tool (SWAT) is widely used in literature to assess the impacts of land management practices on water supplies and nonpoint sources of pollution in watersheds (Arnold et al. 2005). SWAT is best suited for larger scale analysis, such as regional scale analysis spanning several decades (Yang et al. 2018). The concept of a Hydrologic Equivalent Wetland (HEW) was introduced in SWAT to characterize multiple wetlands within a watershed (Wang et al. 2008). The HEW is defined as the combined area and volumetric capacity of all wetlands within the hydrologic response unit (Wang et al. 2008). Wang et al. (2010) successfully employed the HEW concept in SWAT to a watershed in Manitoba, to assess the effects of wetland restoration scenarios on peak discharge and sediment loading, and total phosphorus and total nitrogen loading. Wang et al. (2010) found that the HEW concept allows for the nonlinear functional relations between watershed processes and wetland characteristics, such as size and morphology, to be accurately represented. Second, a probability distribution approach was developed in SWAT to characterize multiple wetlands within a subbasin. Mekonnen et al. (2016a) developed a probability distribution model for depressional storage in the SWAT model to better characterize landscape storage heterogeneity. Mekonnen et al. (2016a) used topographic characteristics to develop a probability density function that describes the spatial heterogeneity of landscape depression storages. Mekonnen et al. (2016b) successfully employed the probability distribution approach in SWAT to a watershed in Canada’s Prairie region. Mekonnen et al. (2016b) explained that traditional SWAT models poorly represent sediment mobilization in Prairie regions due to the numerous, dynamically-connected landscape depressions that highly influence the hydrology of Prairie regions. Mekonnen et al. (2016b) proposed that the probability distribution function is used to represent the variation in storage capacity of the numerous depressions. Mekonnen et al. (2016b) incorporated the use of a seasonally varied soil erodibility factor in order to account for changes in erosion as the soil freezes and thaws. Mekonnen et al. (2016b) results highlighted an

improvement in sediment export predictions for cold-climate regions with numerous landscape depressions exhibiting variable storage capacities, such as Canada’s Prairie region. Third, a Puddle Delineation algorithm was developed in SWAT to characterize multiple wetlands within a subbasin. Nasab et al. (2017) explained that most hydrologic models fail to account for the fact that puddles are spatially distributed throughout a watershed, and that their sizes, storages and interactions vary over time. They developed the Puddle Delineation algorithm to quantify topographic characteristics in terms of distribution and hierarchical relationships between depressions, which was then coupled into SWAT at the hydrologic response unit scale. Nasab et al. (2017) successfully coupled the Puddle Delineation algorithm with SWAT for flows throughout a large watershed in North Dakota. Lastly, Liu et al. (2016) developed the Integrated Modelling for Watershed Evaluation of BMPs (IMWEBs) model for the spatially explicit evaluation of agricultural best management practises (BMPs). IMWEBs is designed to evaluate the water quantity and water quality effects of individual or multiple BMPs at the site, field, farm, and watershed scales (Liu et al. 2016). IMWEBs is a cell-based, fully distributed model, which characterizes individual pixels within a watershed based on differences in land use/land cover, topography, climate, management practises, and soil type (Liu et al. 2016). In contrast, the HEW concept within SWAT groups wetlands together based on similarities in land use/land cover, topography, climate, management practises, and soil type (Liu et al. 2016). Thus, IMWEBs is more suitable for assessing the effects of individual wetlands within a watershed. IMWEBs-Wetland can be used to assess the effects of restoring individual wetlands within a watershed. For instance, Liu et al. (2018) employed the IMWEBs-Wetland model to simulate and assess the water quantity and water quality effects of individual wetlands at the site and watershed scales in a subwatershed in southern Manitoba. They found that the IMWEBs- Wetland model is capable of simulating wetland processes in a complex watershed. Liu et al. (2018) state that the IMWEBs-Wetland model is novel in simulating the water quantity and water quality effects of individual wetlands, which enables the examination of location-specific targeting for wetland restoration and wetland retention.

2.2.2 Wetland Model Application Various wetland modules and model integrations have been developed to address the limitations in wetland characterization. The original SWAT had no characterization of riparian wetlands. Liu et al. (2008) developed a SWAT extension module to simulate riparian wetland hydrologic processes. Liu et al. (2008) found that the riparian wetland module extension predicted flow and sediment loads at the outlet of the watershed more accurately. Rahman et al. (2016) extended the riparian wetland module to quantify the hydraulic interactions between riparian depressional wetlands, rivers and aquifers. Rahman et al. (2016) employed a more robust bidirectional approach instead of the traditional unidirectional approach that is often used to characterize the interactions between a wetland and a river or aquifer. They successfully employed this technique to a river basin in Bangladesh and India, and found that the bidirectional approach outperformed the traditional approach in reproducing river stages. Another approach to improving SWAT characterization is to couple SWAT with the field scale model, SUSTAIN (System for Urban Stormwater Treatment and Analysis IntegratioN), which characterizes drained water from upstream flow (Martinez-Martinez et al. 2015). Similarly, the eco-hydrological, semi-distributed Soil and Water Integrated Model (SWIM) has also been improved upon by incorporating wetland flow and nutrient fluxes, particularly in relation to the groundwater dynamics characterizing riparian wetlands (Hattermann, et al. 2006; Hattermann, et al. 2008). Additionally, a distributed hydrological model, HYDROTEL, has also been improved upon by the integration of modules for isolated wetlands and riparian wetlands (Fossey et al. 2015). Fossey et al. (2016a) successfully employed HYDROTEL to investigate the role of isolated wetlands and riparian wetlands on stream flows in a watershed in Quebec. Their results suggested that both isolated wetlands and riparian wetlands impact landscape hydrology. These wetland model improvements have contributed to increased understanding of wetland functions and distributions at the watershed scale. Martinez-Martinez et al. (2014) found that wetland area had a greater impact on average daily streamflow than wetland depth. Moreover, Martinez-Martinez et al. (2015) evaluated restoration scenarios impacts on sediment loading and found that clusters of wetlands installed at a distance of 150-200 km from the outlet performed better than clusters of wetlands at either closer or farther distances. Additionally, Fossey et al. (2016b) used HYDROTEL to assess the impact of climate change on stream flows to a watershed in Quebec. They found that conservation of existing wetlands within the

watershed would mitigate the increase in peaks flows that are predicted to occur under climate change conditions. Wetland models have been used to quantify the impacts of wetland restoration scenarios. Yang et al. (2010) found that if wetlands within a Manitoba watershed were restored to 1968 levels, the peak discharge and average sediment loading would be reduced by 23.4 and 16.9 percent, respectively. Similarly, Javaheri et al. (2014) found that wetland restoration scenarios applied to a watershed in Indiana can reduce peak flow by up to 42 percent and reduce flood areas by up to 55 percent. Additionally, Darwiche-Criado et al. (2016) evaluated the ability of restored wetlands in an agriculturally intensive region of Spain to remove nitrate and found that wetland buffer zones can effectively remove nitrates. Yang et al. (2016b) found that sediment, total nitrogen, and total phosphorus loadings decrease by 34.5 percent, 28.3 percent, and 37 percent, respectively, under a 100 percent riparian wetland restoration scenario in a southern Ontario watershed. Few hydrologic studies have incorporated additional criteria, such as economic costs, when evaluating wetland restoration scenarios. Yang et al. (2016a) developed an integrated modelling approach, incorporating economic and hydrologic modelling to examine the cost- effectiveness of wetland restoration scenarios in a Canadian prairie watershed. The wetland restoration outcomes are spatially variable and related to wetland drainage characteristics and wetland features (Yang et al. 2016a). Moreover, Yang et al. (2016a) found that wetland restoration costs are driven by forgone cropping returns. Cost-effectiveness ratios were used to identify specific locations for wetland restoration. Their results showed that the cost- effectiveness approach, incorporating economic cost to environmental benefit ratios, is superior to the cost minimization approach, in terms of total phosphorus reductions. Mallon (2017) used IMWEBs to evaluate the cost-effectiveness of nutrient management schemes in a southern Ontario watershed, and found that the most effective scheme was targeting by outlet abatement. 2.3 Identifying Wetland Restoration Sites Wetland loss has been substantial on agricultural landscapes, thus automated techniques to identify locations for wetland restoration are important to protect and enhance the ecosystem services that wetlands provide (Waz and Creed, 2017). Most automated techniques to identify locations for wetland restoration use Digital Elevation Models (DEMs) to identify topographic depressions in the landscape. Topographic depressions are often assumed to be associated with

wetlands because water can pool in these depressions. Additionally, soil and landuse maps are often used in conjunction with the identified topographic depressions to locate wetland restoration sites. Two methods for identifying locations for wetland restoration will be discussed, Whitebox GAT Sink tool, and Whitebox Tools Stochastic Depression Analysis. Whitebox GAT Sink tool employs flow-routing algorithms and depression filling algorithms to identify topographic depression in a raster DEM. A Sink, or topographic depression, can be identified by flow-routing algorithms at cells where flow may enter, but cannot be directed any further due to the absence of adjacent downslope neighbors (Wilson and Gallant, 2000). Whitebox GAT Sink tool employs the D8 flow-routing algorithm to identify depressions. Flow routing algorithms direct the flow from a cell to one or numerous of its adjacent eight neighbours in a three-by-three moving window (Wilson and Gallant, 2000). First, the direction of flow from a cell is determined, and then flow is routed from that cell to its lower elevation neighbours (Wilson et al., 2007; Wilson et al., 2008). Flow-routing algorithms can be divided into single-flow direction algorithms that route flow only to one cell in the window, and multiple-flow direction algorithms that disperse flow in multiple directions among neighbours in the window (Wu et al. 2008). The Sink tool is often used for mapping existing wetlands and locating drained/lost wetlands, as most commercial GIS platforms contain a Sink tool. For instance, Martin and Kirkman (2012) used the Sink tool in ArcGIS to identify depressions, which were then converted to vector polygons and intersected with a hydric soil map to identify isolated wetlands. An existing wetland inventory was also added to the model. The combined model was compared to one that only used existing wetland inventory data and hydric soil map layer, and the results indicated that incorporating depression identification with the Sink tool significantly improved the accuracy of the model (Martin and Kirkman, 2012). Similarly, McCauley and Jenkins (2005) used the Sink tool to identify depressions in a DEM and intersected the digitized depression polygons with hydric soil maps to identify drained/lost wetlands in the landscape. Depression-filling algorithms can also be used to identify closed depressions in a DEM. Depression-filling algorithms are typically used for flow-enforcement to remove depressions in a DEM. Depressions in a DEM often need to be removed for hydrologic modelling purposes, because flow path modelling requires a continuous surface without disruptions. Flow- enforcement can be categorized by filling, breaching, and hybrid techniques.

Filling methods involve raising the elevation of a depression to its lowest spill point (Lindsay, 2016). Breaching involves carving a channel through the depression in the direction of a downslope neighbour by lowering the elevations of higher cells (Reiger, 1998; Lindsay, 2016). Breaching methods are not typically used for depression identification. In contrast, most filling methods locate depressions as well as their spill points before the filling operation (Wang and Liu, 2006). Thus, depression filling algorithms can be used to identify the distribution of depressions within a DEM, which can be used for wetland mapping. For instance, Creed et al. (2003) used the depth-to-sink approach to identify depressions from a DEM used for wetland mapping. The depth-to-sink method involves computing the difference between the DEM with and without depressions filled. The automatic depth-to-sink method was compared to manual delineation of wetlands, and the results indicated strong agreement between the two methods. Yang et al. (2009) used a depression filling technique in a Manitoba watershed, utilizing LiDAR derived DEM and land use data. The LiDAR DEM was used to identify depressions by comparing the DEM before and after filling the depressions. The results were then converted to points and buffered to generate polygons within realistic wetland area ranges. The land use data was then used to remove depressions located in streams, roads, and forests, as wetlands in agricultural areas were the focus of the study. The previous techniques for wetland identification assume that all depressions in the DEM exist in the real world. However, artifact depressions will exist in the DEM due to data measurement errors, pre-processing errors, and the limited spatial resolution of the DEM (Lindsay and Creed, 2006). Measurement errors can occur during data acquisition, and data producers often report the accuracy of their data with the Root Mean Square Error (Wechsler, 2007). Pre-processing errors can be introduced while creating the bare-earth DEM and when interpolating the data into a continuous grid surface (Sithole and Vosselman, 2004; Arun, 2013). Artefact depressions can also be introduced when grid spacing is too coarse to capture important features in the landscape (Lindsay and Creed, 2005). Lindsay and Creed (2006) summarized five approaches for distinguishing artefact depressions from real depressions; ground inspection, analyzing source data, knowledge-based approach, classification methods, and Monte Carlo simulation. Moreover, Lindsay and Creed (2006) compared four methods for distinguishing artefact depressions from real depressions; discriminatory analysis classification, developing simple and complex heuristic rules, and Monte

Carlo simulation. Lindsay and Creed (2006) found that the Monte Carlo method performed slightly better overall compared to the other methods. Lindsay and Creed (2006) suggested that the Monte Carlo method, coupled with ground inspection, is a promising method for discriminating artefact from real depressions. The Monte Carlo method employs stochastic simulation to represent depressions, by providing a range of probability that a depression (Pdep) exists in the landscape (Lindsay and Creed, 2006). A user specified threshold is applied to the Pdep to determine which depressions represent real features. Quantifying the range of probability involves adding a randomly selected error from the normal distribution to every cell, then filling all depressions within the DEM (Lindsay and Creed, 2006). The filled depressions are then flagged. This process continues by adding a different error from the normal distribution to each iteration. The probability of a real depression belonging to the landscape is determined by dividing the number of depressions that have been flagged by the total number of iterations used to perform the simulation (Lindsay and Creed, 2006). Whitebox Tools Monte Carlo method for identifying depressions in a DEM is called Stochastic Depression Analysis. The Stochastic Depression Analysis method for identifying depressions in a landscape has been used for several applications, including wetland identification. For instance, Serran and Creed (2016) created a Pdep layer and used it in object based classification for identifying wetlands on prairie landscapes. They found that the classification of Pdep decreased the error of commission by 5 percent compared to simply applying a threshold to the Pdep layer. Furthermore, Waz and Creed (2017) employed Stochastic Depression Analysis coupled with statistical power law techniques to categorize and quantify permanent wetland loss, temporary wetland loss, and restorable wetland loss in a Prairie watershed in Canada. They found this automated method for quantifying wetlands to be simple, transparent, and reproducible, without using historical data or laborious aerial photography or satellite imagery interpretation. Lastly, Wu et al. (2014) employed stochastic depression analysis to identify woodland vernal pools in Massachusetts. They found that the error of commission ranges from 2.5 percent to 6.0 percent, which is much lower than the reported errors of previous vernal pool studies in the United States. Wu et al. (2014) concluded that their semi-automated approach for vernal pool identification may reduce inconsistencies associated with manual aerial photography interpretation methods.

The Sink tool and Stochastic Depression Analysis have both been used successfully to identify wetland restoration locations. The Sink tool, in conjunction with depression filling algorithms, can be used to simply and easily estimate the surface area and volume of wetland restoration locations. Wetland surface area and wetland volume are important parameters for wetland hydrologic modelling. In contrast, the Stochastic Depression Analysis method is more complex, as the spatial autocorrelation of the DEM must be estimated, and the Pdep threshold value must be determined. Moreover, wetland volume cannot be easily estimated with Stochastic Depression Analysis. Lastly, the Monte Carlo simulation used in Stochastic Depression Analysis requires substantially more computer processing time than the Sink tool.

Chapter 3 Methods

3.1 Research Overview This research builds upon research done by Yang et al. (2016a), and others, by employing an integrated modelling approach to evaluate the cost-effectiveness of wetland restoration scenarios in an agricultural watershed. Specifically, this research identified potential geographic locations for restoring wetlands using GIS-based techniques. The water quantity outcomes of wetland restoration, in terms of peak flow decreases and low flow increases, were then modelled with the Integrated Modelling for Watershed Evaluation of BMPs-Wetland (IMWEBs-Wetland) model. A wetland economic model was developed to estimate the total economic costs of the wetland restoration locations in terms of the opportunity costs of the forgone cropping returns, the wetland restoration administration and engineering costs, and the inconvenience costs. Lastly, cost-effectiveness analysis was used to identify wetlands that have low economic costs and large peak flow decreases. The study period covered January 1, 1984, to December 31, 2045, which included a historical period (1984-2014) and a future period (2015-2045). This time frame was selected based on the availability of crop inventory data, historical and projected climate data, and flow monitoring data available for model calibration and validation. 3.2 Study Area 3.2.1 Lynn River Black Creek Watershed The Lynn River is approximately 30 km long and flows from its headwaters in Windham Centre, Ontario, south to Port Dover, Ontario, where it combines with Black Creek, approximately 700 m from the Lake Erie outlet. The combined water from Lynn River and Black Creek drains into Lake Erie. The Lynn River Black Creek watershed has a drainage area of nearly 300 km2. Tributaries to the Lynn River Black Creek watershed include Catfish Creek, Kent Creek, and Patterson Creek, among others. Figure 3-1 shows the location of the Lynn River Black Creek watershed, in Norfolk County, to the south-west of Hamilton, Ontario (LPRCA, 2018).

Figure 3-1 The Lynn River Black Creek Watershed in Norfolk County of Southern Ontario, Canada The watershed is comprised mainly of agricultural land with forest cover increasing to the west. Wetland cover has dropped in Norfolk County from an estimated 30 percent prior to European settlement in the 1800s, to an estimated 5.4 percent in 2002 (Ducks Unlimited Canada, 2010). There are two towns in the watershed, the Town of Simcoe near the center of the watershed, and the Town of Port Dover at the outlet to Lake Erie. The Town of Simcoe has a population of 13,922 and the Town of Port Dover has a population of 6,161 (Statistics Canada, 2016). The Lynn River Black Creek watershed was selected as the study area for several reasons. First, it is a typical agricultural watershed that has experienced historical wetland loss. Moreover, the Alternative Land Use Services (ALUS) operates in Norfolk County, where the Lynn River Black Creek watershed is located. ALUS is a compensation based program that pays farmers for the ecosystem goods and services they have produced due to the implementation of BMPs, such as wetland restoration (ALUS, 2019a).

3.2.2 Lynn River Subwatershed Unfortunately, Black Creek is ungauged and does not contain any flow stations for model calibration and validation. Moreover, the outlet to Lake Erie, where both the Lynn River and Black Creek drain, is also ungauged. Fortunately, the Lynn River has one flow station downstream of the Town of Simcoe, as shown in Figure 3-2. Therefore, the Lynn River subwatershed was chosen instead of the entire Lynn River Black Creek watershed due to the lack of available flow data throughout the rest of the watershed. The Lynn River subwatershed is approximately 140 km2 and is in the north-west portion of the Lynn River Black Creek watershed.

Figure 3-2 The Lynn River subwatershed landuse, streams, and calibration stations in Norfolk County, Ontario The subwatershed is also comprised predominantly of agricultural land, which makes up approximately 65 percent of the subwatershed area, while 23 percent is forest, 6 percent is developed urban areas, and less than 1 percent is open water or wetlands. Agricultural production in the subwatershed is mainly comprised of field crops, such as corn, soybean, winter wheat, and hay. However, some specialty crops, such as ginseng and tobacco, are also grown in the subwatershed.

3.3 Wetland Restoration Site Identification The IMWEBs-Wetland model requires wetland volume and wetland surface area estimates for all modelled wetlands. Wetland surface area is easier to estimate than wetland volume using GIS-based techniques, as wetland surface area is typically assumed to be equal to the area of the wetland polygon or number of wetland raster cells on a map. Moreover, the IMWEBs-Wetland model can use empirical equations to estimate wetland volume from wetland surface area if wetland volume estimates are not available. This section details the process of identifying the wetland restoration locations using GIS-based techniques. Briefly, a 1 m Digital Elevation Model (DEM) was created from available LiDAR data, Whitebox GAT Sink tool was used to identify depressions in the 1 m DEM, spatial analysis techniques were used to select those depressions that were within poorly drained soils, isolated from streams, within agricultural landuse, and did not have tile drains below. The difference between the filled depressions DEM and the non-filled DEM was used to estimate wetland volumes. 3.3.1 Digital Elevation Model Creation The DEM was created from Land Information Ontario (2019) Lake Erie LiDAR Classified Point Cloud 2017-18. First, the Select Tiles by Polygon tool in Whitebox Tools was used to select all the one-square-kilometer tiles that intersect the Lynn River subwatershed polygon. The LiDAR Ground Point Filter tool in Whitebox Tools was used to filter ground points, using a 2.0 search radius, 0.0 minimum number of neighbours, 45 degree inter-point slope threshold, 0.25 meter off terrain point height threshold, and specifying that points are not classified. The LiDAR Remove Outliers tool in Whitebox Tools was used to remove outliers, using a 2.0 search radius, and a maximum elevation difference of 30. The LiDAR TIN Gridding tool in Whitebox Tools was used to interpolate the LiDAR points to a continuous elevation surface using a Delaunay triangular irregular network (TIN), with a 1.0 meter resolution, specifying last returns, and excluding classes 7 (low noise), 9 (water), 10 (ignored ground, near a break line), 17 (bridge decks), and 18 (High noise). The Mosaic tool in Whitebox Tools was used to mosaic the resultant tiles together into one raster file, using nearest neighbour as the resampling method. The nearest neighbor resampling method was chosen because it minimizes changes to pixel values and does not cause smoothing of the data, as bilinear interpolation and cubic convolution can. The Clip tool in Whitebox Tools was used to clip the raster file to the

Lynn River subwatershed polygon. The Remove Off Terrain Objects tool in Whitebox Tools was used to remove off terrain objects, using a filter dimension of 150 meters and a slope threshold of 25 degrees. The Fill Missing Data tool in Whitebox Tools was used to fill missing data, with a filter dimension of 100 meters and an inverse distance weight value of 2.0. Lastly, the resultant raster DEM was projected to Universal Transverse Mercator North American 1983 Zone 17N using the Project Raster tool in ArcGIS. 3.3.2 Whitebox GAT Sink function Whitebox Tools Sink function was used to locate the sinks in the DEM. The Sink tool first fills the depressions in the DEM and then clumps the identified depressions together using a Clump tool (Lindsay, 2019). The Sink output was then converted to a polygon layer with Whitebox GAT Raster to Polygon conversion tool and imported to ArcGIS. Soils with a hydro group of C or D (i.e. soils with slow infiltration rates and very slow infiltration rates, respectively) were extracted from the Detailed Soil Survey layer. Ducks Unlimited Canada (2010) states that poorly and very poorly drained mineral soils indicate terrestrial areas that likely supported wetlands in the past. ArcGIS Dissolve tool was then used to dissolve the boundaries between soil groups. ArcGIS Select by Location was used to select the Sinks that are completely within the hydro group C or D soils. ArcGIS Select by Location was then used to further select from those sinks that are within the hydro group C or D those that are completely within agricultural fields, which is defined from the Economic Field Boundary layer described in section 3.4.3.1. ArcGIS Buffer tool was used to buffer the stream layer by 100 meters, and the resultant layer was then dissolved. Select by Location was then used to remove from the selected sinks layer any sinks that intersected the buffered stream layer. Sinks that were within the 100 meter stream buffer were removed from the analysis because IMWEBs-Wetland can only model the impacts of isolated wetlands, thus sinks within 100 meters of any stream were assumed to be riparian wetlands. The existing wetlands in the landuse layer were exported to a new layer, and the Select by Location tool was used to ensure that any sinks that intersected existing wetlands were removed from the selection. The ‘Tilled’ and ‘Undifferentiated’ classes from the Southern Ontario Land Resource Information System (SOLRIS) Landuse layer were exported to a new layer. ArcGIS Dissolve tool was then used to dissolve the boundaries between the two classes.

Select by Location was used to ensure the selected sinks are all completely within the ‘Tilled’ and ‘Undifferentiated’ SOLRIS Landuse classes. The ‘Tilled’ and ‘Undifferentiated’ classes in the SOLRIS Landuse layer are assumed to be associated with agricultural production fields (i.e. not farm buildings nor hedgerows). Sinks that are within the economic field boundary layer and within the ‘Tilled’ and ‘Undifferentiated’ SOLRIS landuse classes were selected as this research focuses on restoring wetlands in locations that are currently agricultural production fields. Lastly, Select by Location tool was used to ensure that any sinks that intersected the tile drainage layer were removed from the selection. Sinks that intersect the tile drainage layer cannot easily be restored to wetlands, unless the tile drains are removed, because tile drains increase soil drainage which prevents wetlands from establishing. The selected sinks were then exported to a new layer, a new field in the attribute table was created, and ArcGIS Calculate Geometry was used to calculate the area of each sink in hectares. ArcGIS Select by Attributes was used to select from the previous sink layer all those sinks with a surface area equal to or greater than 0.1 ha. According to Wanhong Yang (Personal communication, April 12, 2019) the minimum size of isolated wetlands that Ducks Unlimited Canada restores is 0.1 ha. Moreover, the Lynn River subwatershed IMWEBs-Wetland model uses a 30 m spatial resolution, therefore a 0.1 ha wetland will be approximately equal to 1 pixel in the model. Figure 3-3 illustrates the 59 identified wetland restoration locations in the Lynn River subwatershed.

Figure 3-3 The 59 identified wetland restoration locations in the Lynn River subwatershed 3.3.3 Fill Depressions & Difference The Fill Depressions tool in Whitebox GAT was used to estimate the maximum volume of the wetland restoration locations that were identified with the Sink tool in section 3.3.2. The Fill Depression tool was run on the 1 m LiDAR DEM (described in section 3.3.1). The difference between the filled DEM and the not filled DEM was then calculated using the Raster Calculator. Lastly, the Zonal Statistics tool in ArcGIS was run on the differenced raster, with the identified wetland restoration locations as zone boundaries, to calculate the sum of the filled pixels within each wetland restoration polygon, expressed in m3. The sum of the filled pixels within each wetland restoration polygon represents the wetland maximum volume. The identified wetland restoration locations are all modelled as Drained-Altered wetlands in the IMWEBs-Wetland model, as defined by DUC. The IMWEBs-Wetland model classifies five types of isolated wetlands based on DUC wetland inventory data, Drained-Altered, Drained- Consolidated, Drained-Lost, Altered, and Intact. Liu et al. (2016) explain that these wetland classifications were chosen because the IMWEBs-Wetland model is likely to be applied in Alberta and DUC will be a major source of wetland data for the model.

Flow out of Drained-Altered wetlands occurs when water level in the wetland is higher than the drain bottom elevation. To characterize Drained-Altered wetlands, the wetland surface areas and wetland volumes calculated above were assumed to be the maximum wetland surface areas and the maximum wetland volumes. The normal wetland surface area (i.e. corresponding to the depth to drain bottom) was assumed to be 1/3 of the maximum wetland surface area, and the corresponding normal volume was calculated with Equation 3-1 and Equation 3-2 (Liu et al. 2018). The wetland restoration bottom hydraulic conductivity was assumed to be 0.5 mm/hour, as field measured data was not available. Yang et al. (2008) empirically specified the wetland bottom hydraulic conductivity to be 0.5 mm/hour for a Western Manitoba watershed. Moreover, Rahman et al. (2016) found that calibrated SWAT wetland bottom hydraulic conductivity values range from 0.30 to 8.0 mm/hour. Lastly, Qi et al. (2019) explain that restored wetlands bottom hydraulic conductivity is typically larger than existing wetlands bottom hydraulic conductivity, due to the construction and excavation process. Similarly, in this study, the wetland restoration locations bottom hydraulic conductivity is 0.5 mm/hour, whereas the existing wetlands bottom hydraulic conductivity is 0.05 mm/hour. For wetlands with a surface area less than 70 ha: Equation 3-1

푉 = 2.85 퐴1.22 For wetlands with a surface area greater than 70 ha: Equation 3-2

푉 = 7.1퐴 + 9.97 where 퐴 is the wetland surface area (ha) and 푉 is the corresponding full supply volume, expressed in 103m3 (Wiens, 2001). 3.4 Data Preparation 3.4.1 Data Preparation for IMWEBs Model The geospatial data and observational data used in the IMWEBs-Wetland model are listed in Table 3-1. The necessary pre-processing steps are outlined in Appendix A: IMWEBs- Wetland Data Preprocessing. Briefly, the geospatial data were clipped to the Lynn River subwatershed, resampled to 30 m if needed, and projected to Universal Transverse Mercator

North American 1983 Zone 17N. The climate data, flow data, and point source data with missing records were filled with values from nearby stations or averages as the IMWEBs-Wetland model requires climate data and flow data for every day of simulation. Table 3-1 The geospatial and observational data used in the IMWEBs-Wetland model

IMWEBs-Wetland Use Scale Date(s) Source Input 1 m Derive hydrologic Lake Erie Lidar Digital Elevation resolution parameters (watershed, 2018 Classified Point Cloud Model resampled to subbasins, slope, streams) (MNRF) 30 m 15 m Southern Ontario Land Drive rainfall/runoff and resolution Landuse/Landcover 2011 Resource Information sediment transport resampled to System (LIO) 30 m Links user's landuse code to Landuse Lookup IMWEBS-Wetland landuse N/A N/A N/A table codes Crop inventory Drive rainfall/runoff and Annual Crop Inventory 30 m 2011-2017 (Landcover) sediment transport (AAFC) Ontario Detailed Soil Drive rainfall/runoff and Soil data 1:50,000 2015 Survey version 3.0 sediment transport (AAFC) Links user's soil code to Soil Lookup table IMWEBs-Wetland soil N/A N/A N/A codes Used for Stream burn-in: Ontario's Hydrology Streams DEM altered to match the 1: 10,000 2010 Network - Watercourse provided stream network (LIO) Derive field parameters Field boundaries (area, overlapping 1: 10,000 2013 Teranet Parcel Fabric percentage with subbasins) 15 m Southern Ontario Land Existing wetland Derive wetland parameters resolution 2011 Resource Information boundaries (contributing area, volume) resampled to System (LIO) 30 m Daily precipitation, maximum temperature, Environment and Climate data minimum temperature, solar N/A 1970-2017 Climate Change Canada radiation, relative moisture, wind speed, wind direction Flow data Daily discharge N/A 2000-2017 Water Survey of Canada Monthly wastewater Point source discharge, Municipal treated treatment plant sediment, and total N/A 2008-2016 wastewater effluent discharges phosphorus (Government of Ontario)

3.4.1.1 Summary of Wetland Restoration Locations and Existing Wetlands Table 3-2 presents the 59 identified wetland restoration locations modelled in the Lynn River subwatershed IMWEBs-Wetland model with their contributing areas, maximum areas, maximum volumes, linear distance to the subwatershed outlet, and their Universal Transverse Mercator North American 1983 Zone 17N coordinates. The distance to outlet was calculated using the Point Distance tool in ArcGIS and represents the linear distance from each wetland to the subwatershed outlet. Table 3-2 The 59 wetland restoration location identified in the Lynn River subwatershed with their contributing areas, maximum areas, maximum volumes, linear distance to the subwatershed outlet, and UTM NAD 17N coordinates

Maximum Distance Wetland Wetland Contributing Maximum X Y Volume to outlet Status ID Area (ha) Area (ha) Coordinate Coordinate (104m3) (km) Restored 9 24.39 0.54 0.134 9.78 558728.81 4751215.69 Restored 11 3.69 0.27 0.058 13.24 548797.81 4750856.19 Restored 14 6.75 0.99 0.282 11.93 550239.01 4750400.81 Restored 15 6.03 0.45 0.108 12.10 549966.72 4750393.17 Restored 16 3.15 0.27 0.058 12.04 550055.23 4750390.72 Restored 18 3.60 0.27 0.058 12.47 549351.80 4750316.21 Restored 20 1.89 0.45 0.108 8.93 556466.38 4750227.69 Restored 21 0.36 0.63 0.162 12.41 549296.09 4750177.49 Restored 22 1.44 0.54 0.134 12.40 549183.75 4750042.42 Restored 24 3.06 0.45 0.108 12.26 549230.97 4749895.89 Restored 26 1.71 0.54 0.134 11.80 549574.18 4749587.23 Restored 28 9.18 1.62 0.513 12.62 548386.34 4749485.27 Restored 30 0.99 0.63 0.162 8.98 554185.53 4749521.63 Restored 33 8.01 0.63 0.162 13.76 546823.98 4749300.45 Restored 34 111.87 0.45 0.108 8.21 555363.59 4749182.44 Restored 35 43.38 0.81 0.220 7.77 559281.77 4749130.09 Restored 39 80.10 0.63 0.162 8.02 554681.67 4748699.32 Restored 41 2.70 1.26 0.378 11.65 548947.69 4748626.73 Restored 42 0.90 0.27 0.058 12.12 548298.43 4748544.97 Restored 43 2.25 0.90 0.251 12.04 548325.33 4748458.63 Restored 45 3.24 0.18 0.035 7.63 554259.70 4748034.82 Restored 47 20.34 0.45 0.108 8.03 553010.90 4747648.61 Restored 50 0.90 0.27 0.058 8.12 552772.90 4747564.26 Restored 51 2.07 1.08 0.313 7.94 552982.28 4747505.48 Restored 52 15.30 0.63 0.162 8.00 552880.88 4747499.55 Restored 58 0.81 0.45 0.108 6.78 553602.55 4746510.34 Restored 64 8.73 0.18 0.035 4.23 556865.97 4745489.30 Restored 65 1.26 0.18 0.035 4.29 559607.80 4745470.15 Restored 67 0.90 0.27 0.058 11.34 547490.76 4745393.96 Restored 73 1.17 0.45 0.108 7.22 551877.11 4745069.05 Restored 74 37.35 0.36 0.082 8.36 550558.91 4745014.49 Restored 78 31.32 0.18 0.035 8.34 550525.05 4744886.09 Restored 80 19.08 0.99 0.282 6.70 552219.34 4744621.83

Table 3-2 The 59 wetland restoration location identified in the Lynn River subwatershed with their contributing areas, maximum areas, maximum volumes, linear distance to the subwatershed outlet, and UTM NAD 17N coordinates, Continued Maximum Distance Wetland Wetland Contributing Maximum X Y Volume to outlet Status ID Area (ha) Area (ha) Coordinate Coordinate (104m3) (km) Restored 85 1.89 0.45 0.108 6.44 552307.63 4744216.56 Restored 86 0.63 0.27 0.058 6.37 552397.08 4744243.60 Restored 90 0.54 0.18 0.035 6.09 552513.57 4743830.63 Restored 94 9.99 0.18 0.035 6.83 551495.13 4743096.67 Restored 97 8.37 0.81 0.220 5.52 552759.96 4742748.13 Restored 98 1.71 0.27 0.058 7.76 550458.64 4742666.85 Restored 101 16.29 0.27 0.058 5.40 552846.64 4742596.50 Restored 103 3.87 0.72 0.191 8.75 549423.46 4742392.19 Restored 104 9.63 0.36 0.082 8.52 549646.86 4742371.31 Restored 106 1.98 0.36 0.082 5.52 552650.69 4742161.13 Restored 107 5.58 2.79 0.997 7.05 551094.88 4742004.65 Restored 109 3.33 0.63 0.162 6.67 551469.48 4741977.53 Restored 111 61.56 1.53 0.479 7.19 550940.54 4741877.73 Restored 112 1.26 0.27 0.058 7.04 551090.43 4741907.29 Restored 115 2.25 0.36 0.082 6.60 551527.67 4741818.06 Restored 116 1.35 0.45 0.108 6.03 552093.60 4741684.56 Restored 118 4.95 0.54 0.134 7.88 550240.72 4741469.27 Restored 122 2.52 0.72 0.191 8.06 550066.23 4741233.03 Restored 123 12.60 1.17 0.345 7.95 550175.06 4741119.84 Restored 124 9.00 0.54 0.134 1.99 556192.74 4740936.65 Restored 126 5.94 1.71 0.548 3.08 555111.42 4740808.89 Restored 127 1.35 0.36 0.082 2.71 555483.03 4740844.38 Restored 128 1.98 0.18 0.035 2.65 555544.88 4740818.47 Restored 136 16.92 0.63 0.162 8.90 549347.36 4739923.80 Restored 137 0.18 0.18 0.035 1.88 557008.78 4739930.91 Restored 139 2.16 0.18 0.035 6.20 552411.07 4739021.93

The existing wetlands modelled in the IMWEBs-Wetland model are taken from SOLRIS landuses classes Marsh, Thicket Swamp, and Open Water. ArcGIS Calculate Geometry function was used to estimate the existing wetlands’ surface areas. The IMWEBs-Wetland model estimated the existing wetlands’ volumes using the empirical equations shown in Equation 3-1 and Equation 3-2 (Wiens, 2001). The existing wetlands are all assumed to be Altered wetlands, as defined by DUC. Flow out of Altered wetlands occurs when water level in the wetland is higher than the drain bottom elevation. To characterize Altered wetlands, the maximum volume was calculated with Equation 3-1 and Equation 3-2 based on SOLRIS wetland data, while the normal wetland surface area (i.e. corresponding to the depth to drain bottom) was assumed to be 1/3 of the maximum surface area, and the corresponding normal volume was calculated with Equation 3-1 and Equation 3-2 (Liu et al. 2018). The existing wetlands bottom hydraulic

conductivity was assumed to be 0.05 mm/hour, as field measured data was not available, and this is the default wetland bottom hydraulic conductivity in the IMWEBs-Wetland model. Table 3-3 presents the 39 existing isolated wetlands modelled in the Lynn River subwatershed IMWEBs-Wetland model with their contributing areas, maximum areas, maximum volumes, linear distance to the subwatershed outlet, and their Universal Transverse Mercator North American 1983 Zone 17N coordinates. The distance to outlet was calculated using the Point Distance tool in ArcGIS and represents the linear distance from each wetland to the subwatershed outlet.

Table 3-3 The 39 existing wetlands in the Lynn River subwatershed with their contributing areas, maximum areas, maximum volumes, and distance to the subwatershed outlet

Maximum Distance Wetland Wetland Contributing Maximum X Y Volume to outlet Status ID Area (ha) Area (ha) Coordinate Coordinate (104m3) (km) Existing 1 7.11 1.71 0.548 14.29 549054.78 4752491.82 Existing 2 65.52 0.27 0.058 12.82 551610.24 4752493.19 Existing 3 15.66 1.98 0.656 14.14 548968.96 4752235.10 Existing 4 17.91 2.79 0.997 13.78 549467.28 4752169.32 Existing 5 11.07 0.90 0.251 14.11 548835.78 4752074.82 Existing 6 0.27 0.54 0.134 14.28 548454.78 4751966.82 Existing 7 8.10 0.54 0.134 14.21 548148.78 4751576.82 Existing 8 17.91 0.63 0.162 14.40 547757.28 4751441.82 Existing 10 78.30 4.50 1.786 9.53 558470.62 4750969.49 Existing 12 6.84 0.90 0.251 11.81 550922.28 4750811.82 Existing 13 8.28 0.81 0.220 13.89 547673.20 4750611.30 Existing 17 1.44 0.54 0.134 13.73 547629.78 4750301.82 Existing 19 45.27 1.62 0.513 13.73 547547.28 4750206.12 Existing 23 1.53 0.81 0.220 13.47 547689.78 4749971.82 Existing 27 4.68 1.62 0.513 8.23 559797.86 4749511.10 Existing 31 102.33 6.93 3.024 12.42 548410.68 4749201.34 Existing 36 2.16 0.99 0.282 10.06 551492.28 4749019.32 Existing 44 124.38 1.62 0.513 12.64 547337.28 4748044.32 Existing 49 17.64 0.45 0.108 6.24 558887.28 4747639.32 Existing 53 160.02 0.81 0.220 6.01 559217.28 4747354.32 Existing 54 48.42 0.81 0.220 6.13 559519.07 4747417.54 Existing 55 8.91 1.26 0.378 6.11 559851.22 4747306.48 Existing 57 2.61 0.54 0.134 5.44 559704.78 4746656.82 Existing 69 8.28 1.89 0.620 5.48 554182.91 4745259.95 Existing 71 6.12 0.54 0.134 3.69 558549.78 4745111.82 Existing 77 11.25 3.42 1.277 3.46 558572.96 4744884.50 Existing 81 203.67 1.44 0.445 3.14 558336.12 4744580.18 Existing 87 118.89 0.36 0.082 3.11 556614.78 4744174.32 Existing 92 3.15 0.27 0.058 9.06 549285.24 4743460.46 Existing 110 29.07 1.08 0.313 5.86 552286.89 4741974.85 Existing 114 4.59 3.60 1.360 6.19 551942.28 4741826.82 Existing 125 27.27 0.90 0.251 4.61 553547.28 4740836.82 Existing 130 1.62 1.62 0.513 7.14 551055.16 4740400.27 Existing 131 13.86 0.18 0.035 1.29 557342.28 4740424.32 Existing 132 1.17 0.63 0.162 9.05 549140.37 4740326.82 Existing 133 6.75 0.81 0.220 2.47 556037.28 4740131.82 Existing 134 3.96 1.89 0.620 5.02 553283.20 4740103.94 Existing 135 2.52 1.26 0.378 9.33 548916.85 4739924.02 Existing 138 18.54 0.36 0.082 5.74 552759.78 4739389.32

3.4.2 Development of Climate Scenarios Two climate scenarios were developed to assess the flow outcomes of wetland restoration, a historical climate scenario and a future climate change scenario (Table 3-4). The historical climate scenario (January 1, 1984 - December 31, 2014) is developed from four Environment and Climate Change Canada (ECCC) climate stations described in Appendix A.8. The future climate change scenario (January 1, 2015 - December 31, 2045) is taken from the Providing Regional Climates for Impacts Studies (PRECIS) model. PRECIS is a flexible, easy to use, and computationally inexpensive regional climate model designed to provide detailed climate scenarios (Wilson et al. 2011). The future climate dataset was generated from five PRECIS experiments with a 25x25 km resolution and driven by different boundary conditions. The use of PRECIS to generate future climate change scenarios has been well documented in literature (Wang et al. 2013, 2014a, 2014b, 2015a, 2015b). Table 3-4 Climate scenarios for the Lynn River subwatershed IMWEBs-Wetland model

Scenario Period Time Step Climate Variables Source Precipitation, Temperature, Wind Speed, Wind Historical 1984-2014 Daily Direction, Relative Moisture, Solar Radiation ECCC Precipitation, Temperature, Wind Speed, Wind Future 2015-2045 Daily Direction, Relative Moisture, Solar Radiation PRECIS

3.4.2.1 Description of the PRECIS data The future climate scenario for the Lynn River subwatershed including precipitation, temperature, wind speed, wind direction, solar radiation, and relative moisture were obtained from Ontario Climate Change Data Portal (http://www.ontarioccdp.ca). The Ontario Climate Change Data Portal currently offers climate projections under three emissions scenarios, Special Report on Emissions Scenarios (SRES) A1B, Representative Concentration Pathways (RCP)4.5, and Representative Concentration Pathways (RCP)8.5. The SRES A1B emissions scenario assumes a future of very rapid economic growth, low population growth and rapid introduction of new and more efficient technology (IPCC, nd). RCP4.5 scenario stabilizes radiative forcing at 4.5 W/m2 in the year 2100 without ever exceeding that value and corresponds to the lowest greenhouse gas (GHG) emissions (Thomson et al. 2011). RCP8.5 corresponds with the highest GHG emissions, and assumes high population and relatively slow income growth, leading to high energy demand and emissions (Riahi et al. 2011). In this study, the SRES A1B emissions

scenario was selected for developing the future climate scenario because the estimated SRES A1B GHG emissions are between the lower GHG emission RCP4.5 scenario and the higher GHG emission RCP8.5 scenario. The future climate datasets for precipitation, temperature, wind speed, wind direction, solar radiation, and relative moisture were downloaded at the 70th percentile, as the 80th and 90th percentile resulted in mean annual precipitation values that were too large (i.e. 1,991 mm/year and 3,538 mm/year, respectively). 3.4.2.2 Statistical analysis of future climate scenario A statistical analysis was performed on the two climate scenarios. Table 3-5 presents the mean annual precipitation, temperature, and wind speed for the 1984-2014 and 2015-2045 climate scenarios. Precipitation, temperature, and wind speed are the most sensitive climate parameters for the IMWEBs model, thus only these three parameters will be discussed. The annual mean precipitation increases from 947 mm/yr to 1,047 mm/yr, a 10.56 percent increase (Figure 3-4). The increase in precipitation occurs mainly in winter and spring months from January to June, while the change of precipitation in summer months are less substantial (Figure 3-5). The annual mean temperature increases from 8.26 °C to 12.48 °C, a 51.09 percent increase (Figure 3-6). Contrasting from precipitation, the increase of temperature is distributed evenly throughout the year (Figure 3-7). The annual mean wind speed increases from 5.13 m/s to 5.80 m/s, a 13.06 percent increase (Figure 3-8). The increase of wind speed occurs mainly in summer months from May to September while the change in wind speed in the winter months is less significant (Figure 3-9). Table 3-5 Mean precipitation, temperature, and wind speed of the two climate scenarios

Mean Mean Mean Wind Year Precipitation Temperature Speed (mm/year) (°C/year) (m/s) 1984-2014 947 8.26 5.13 2015-2045 1,047 12.48 5.80

1100

1000

900 Precipitation (mm/yr) Precipitation

800 1984-2014 2015-2045 Year

Figure 3-4 Average annual precipitation of the two climate scenarios

150 125 100 75 50

25 Precipitation (mm/yr) Precipitation 0 1 2 3 4 5 6 7 8 9 10 11 12 Month

1984-2014 2015-2045

Figure 3-5 Average monthly precipitation of the two climate scenarios

15 C)

° 10

5 Temperature ( Temperature

0 1984-2014 2015-2045 Month

Figure 3-6 Average annual temperature of the two climate scenarios

C) °

Temperature ( Temperature 0

-10 1 2 3 4 5 6 7 8 9 10 11 12 Month

1984-2014 2015-2045

Figure 3-7 Average monthly temperature of the two climate scenarios

2 Wind Speed (m/s) Speed Wind

0 1984-2014 2015-2045 Year

Figure 3-8 Average annual wind speed of the two climate scenarios

4 Wind Speed (m/s) Speed Wind

3 1 2 3 4 5 6 7 8 9 10 11 12 Month

1984-2014 2015-2045

Figure 3-9 Average monthly wind speed of the two climate scenarios

3.4.3 Data Preparation for Wetland Economic Model The wetland economic model is comprised of three main cost categories, the opportunity cost of the forgone cropping returns, the restoration administration and engineering costs, and the inconvenience costs associated with farming a field with a restored wetland. The opportunity cost of the forgone cropping returns is estimated from annual crop inventories, crop yields, crop

prices, and crop production costs, which is detailed below. The restoration administration and engineering costs found in Table 3-12 are estimates from Stephanie Drayer, the ALUS Norfolk Coordinator. The inconvenience costs are taken from Cortus et al. (2011) and represent the percentage increase in crop inputs costs due to farming a field with wetlands present. Inconvenience costs are differentiated by field size. 3.4.3.1 Economic Field Boundary Layer The economic field boundary layer was created from Teranet Parcel Fabric data for Norfolk County acquired from the Data Resource Centre at the University of Guelph library. First, the Teranet Parcel Fabric data was clipped to the Lynn River subwatershed using the Clip function in ArcGIS. The resultant shapefile was then projected to Universal Transverse Mercator North American 1983 Zone 17N using the Project tool in ArcGIS. Parcels were assumed to be agricultural if there was at least 2 hectares of any crop type 4 out of 6 years of the crop inventory data for years 2012-2017. To identify these parcels, the annual crop inventory data for years 2012-2017 was reclassified to binary rasters with ‘1’ representing any crop type, and ‘0’ representing all other non-crop classes in the annual crop inventories using the Reclassify tool in ArcGIS. This was done for all years of crop inventory from 2012-2017. After reclassification, the Raster Calculator was used to identify cells classified as agriculture (1 in the binary raster) for at least 4 out of the 6 years of crop inventory data. (i.e. "reclass_2012.tif" + "reclass_2013.tif" + "reclass_2014.tif" + "reclass_2015.tif" + "reclass_2016.tif" + "reclass_2017.tif" >= 4). To identify agricultural parcels, the Zonal Statistics as Table tool in ArcGIS was used with the parcel data as the feature zone data and the agriculture 4 out of 6 raster as the value raster. The resultant table from the Zonal Statistics as Table tool was joined to the parcel shapefile to identify parcels with at least 2 hectares of agriculture within. The Select by Attribute tool in ArcGIS was used to select the parcels with a sum of 22 pixels or larger and these parcels were exported to a new shapefile for the field boundary layer. A sum of 22 pixels or larger means 1.98 hectares of that parcel is classified as agriculture 4 out of 6 years. Two hectares was chosen because agricultural parcels will often contain some non-crop pixels, such as those associated with farm buildings. Moreover, non-agricultural parcels could have small areas classified as agriculture, such as those associated with backyard gardens. Lastly, the resultant field boundary layer was imported to Google Earth Pro where it was visually inspected with Google Earth Pro satellite imagery from at least 2 years to confirm the parcel is associated

with agricultural landuse. Approximately 15 of the automatically identified parcels were associated with industrial (i.e. quarry) or residential landuses in Google Earth Pro, and theses were manually edited out of the field layer in ArcGIS. 3.4.3.2 Forgone Cropping Return Estimates Annual Crop Inventory data were taken from Agriculture and Agri-Food Canada’s Annual Crop Inventory for years 2011-2017. The annual crop inventories were clipped to the Lynn River subwatershed using the Clip function in ArcGIS. The resultant rasters were saved as a TIF file and then projected to Universal Transverse Mercator North American 1983 Zone 17N using the Project Raster tool in ArcGIS. Table 3-6 shows the dominant crop types by area (percent) for the Lynn River subwatershed averaged for years 2011-2017, which indicate crop production covers an average 62.21 percent of the subwatershed area. The dominant crops are corn, soybean, hay (pasture and forages), and winter wheat. Uniquely, tobacco and ginseng make up a small percentage of annual crop production in the Lynn River subwatershed. A crop budget was then estimated for corn, soybean, hay, and winter wheat. Table 3-6 Average percent of the Lynn River subwatershed covered by dominant crop types for years 2011-2017, expressed as a percentage of total land area

Average Land Area of the Lynn Crop River Subwatershed Covered by Dominant Crop Types* Corn 16.74% Soybeans 15.69% Hay 9.36% Winter Wheat 5.32% Rye 4.80% Vegetables 3.67% Tobacco 2.39% Ginseng 1.57% Orchards 1.38% Other Crops 1.29%

Crop budgeting was used to estimate the average return of corn, soybean, hay, and winter wheat production in Norfolk County. Crop budgeting requires crop yields, crop prices, and production costs to estimate net returns. 10 year averages for crop yields, crop prices, and production costs were calculated to account for yearly variability in yields, prices, and costs that are associated with differing growing season conditions and changing economic markets. The

average net return of these dominant crops in the subwatershed was used to estimate the opportunity costs of the wetland restoration locations. The opportunity costs of the wetland restoration locations are equal to the forgone cropping returns because once a wetland has been restored in an agricultural field, the land area that the wetland now occupies can no longer be used for crop production. Annual crop yields are taken from Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) Area and Production Estimates by County. The annual yields for corn, soybean, hay, and winter wheat in Norfolk County for years 2008-2017 were averaged to determine a 10 year average annual yield. Crop prices for corn, soybean, hay, and winter wheat are taken from OMAFRA’s Historical Provincial Estimates by Crop for years 2008-2017. Crop prices for corn, soybean, hay, and winter wheat for years 2008-2017 were first converted from nominal prices to real Canadian 2018 dollars (CAD2018) using the average annual rate of inflation from the Bank of Canada. After converting to real CAD2018, crop prices for years 2008-2017 were averaged to determine 10 year average crop prices. Production cost data for corn, soybean, alfalfa-timothy (i.e. hay), and winter wheat are taken from OMAFRA’s Field Crop Budgets Publication 60 for years 2008-2017. Production costs for corn, soybean, hay, and winter wheat for years 2008-2017 were first converted from nominal prices to real Canadian 2018 dollars (CAD2018) using the average annual rate of inflation from the Bank of Canada. After converting to real CAD2018, production costs for years 2008-2017 were averaged to determine 10 year average production costs. Production costs in the Field Crop Budgets are broken into categories depending on management practises such as conventional tillage versus no-till, and genetically modified (GM) versus non-genetically modified (non-GM). Thus, assumptions regarding the most common crop management practises in the subwatershed were made. All corn in the subwatershed was assumed to be grain corn, because based on a 10 year average (2008-2017) of OMAFRA Area and Production Estimates by County, 93.78 percent of total corn acres seeded is grain corn. Moreover, corn is assumed to be GM as 85.85 percent of corn grown in Ontario was GM in the 2017 Agricultural Census (Statistics Canada, 2019a). Lastly, corn is assumed to be conventional tillage because based on the 2016 Agricultural Census (Statistics Canada, 2019b) 35.02 percent of the total land prepared for seeding in the Norfolk-Haldimand Census Division was tillage

incorporating most crop residue into the soil (i.e. conventional tillage), while 31.70 percent of the total land prepared for seeding was no-till, and the remaining 33.38 percent was tillage retaining most crop residue on surface (i.e. conservation tillage). However, as John Molenhuis (Personal communication, September 27, 2018) explains this data is not crop specific, as the adoption of no-till is much higher in soybeans and wheat than it is for corn. Soybean is assumed to be GM as 71.54 percent of soybean grown in Ontario was GM in the 2017 Agricultural Census (Statistics Canada, 2019a). Moreover, soybean and winter wheat are both assumed to be no-till because according to John Molenhuis (Personal communication, September 27, 2018) the adoption of no-till is much higher in soybeans and wheat. Winter wheat is assumed to be all soft red winter wheat because according to Joanna Follings (Personal communication, October 11, 2018) crop insurance shows that 95 percent of the winter wheat planted in Norfolk County was soft red in 2017. These assumptions are only for estimating production costs, different crop management assumptions were used for simulating crop production in the Lynn River subwatershed IMWEBs-Wetland model (see Appendix A.3). The 10 year average annual crop budget for corn, soybeans, hay, and winter wheat is shown in Table 3-7. The average annual crop budgets were calculated by first multiplying the average yield by the average price to arrive at the average revenue. The average production costs are then subtracted from the average revenue to arrive at the average net return before land and overhead costs. Land and overhead costs were not estimated because according to John Molenhuis (Personal communication, January 14, 2019) land and overhead costs will be approximately the same for each crop, and thus the relative difference in return between the crops will be the same.

Table 3-7 Data for the crop budget used to calculate average net return before land and overhead costs

Average Average Average Average Average Average Crop Total Crop Net Crop Yield Net Return Price Revenue Costs Return (bu/acre) ($/ha) ($/bu) ($/acre) ($/acre) ($/acre) Grain Corn 154.34 5.64 870.48 563.00 307.48 759.80 Soybeans 41.24 13.51 557.15 290.92 266.23 657.87 Winter Wheat 71.06 6.44 457.63 348.90 108.73 268.68 2.63 156.14 Hay (metric ($/metric 410.65 341.40 69.25 171.12 tons/acre) ton) Note all monetary values are CAD2018. Crop costs are composed of operating expenses such as seeding, insecticide treatment, herbicide treatment, tractor and machine costs, insurance, trucking, drying, operator labour, and storage.

3.5 IMWEBs Model Setup, Calibration, and Validation After the input data for IMWEBs-Wetland model were prepared, model setup began. Model setup began with watershed definition, which converted user data into the modelling system and built the IMWEBs interface watershed (Shao et al. 2018). Watershed definition included subbasin delineation, soil and landuse reclassification, and field and farm definition (Shao et al. 2018). Subbasin delineation included setting the subbasin threshold area, wetland burn-in, inserting subbasin outlets, selecting watershed outlet, and generating subbasins. The subbasin threshold area is the minimum drainage area that will be used to define the starting cell of the user stream network, and this value was set to 10 ha. Wetland burn-in ensures that flow directions are generated appropriately within wetlands. The wetland burn-in step requires three inputs, the minimum wetland area, the riparian wetland threshold area, and the main stream buffer size, which were 0.1 ha, 300 ha, and 100 m, respectively. The next step involved inserting subbasin outlets at the monitoring location, which was specified in a shapefile that contained the point location of the flow calibration station. Next, the subwatershed outlet was selected, which was specified as the location of the flow calibration station. The final step in subbasin delineation automatically finished the delineation process after selecting the “Delineate” button in the interface. The soil and landuse reclassification processes link the user soil and landuse categories to the IMWEBs-Wetland model database. First, the landuse data was reclassified based on the

“VALUE” field in the raster dataset. Then the “LookUp Table” button was selected to auto-fill the classification table. These two steps were repeated for soil reclassification as well. Only field boundaries for the Lynn River subwatershed were defined in the IMWEBs- Wetland interface, as detailed farm boundary data were not available. Field definition was done with the 2017 Crop Inventory, as this was the most recent crop inventory available. First, the 2017 Crop Inventory was reclassified to only contain agricultural crops (i.e. no built-up area, no forests, etc.). The Region Group tool in ArcGIS was then used to identify regions of neighbouring cells with the same crop type, and a unique number was assigned to each region in the output. Four neighbors were specified instead of eight neighbors in the Region Group tool as using eight neighbors would result in more cells being assigned to the same region, resulting in larger field boundaries that likely would not correspond well with other years of crop inventory data. The Raster to Polygon tool in ArcGIS was then used to convert the Region Group output from a raster to a polygon, ensuring that polygons are not simplified. The resultant polygon layer was then Clipped to the agricultural landuses in the landuse layer (i.e. Tilled and Undifferentiated) to ensure the field boundaries only cover agricultural landuses. Next, all polygons in the layer were selected, and the Explode function in ArcGIS was used to ensure there were not any multipart polygons. Lastly, Calculate Geometry was used to determine the area of the polygons, and polygons 900 m2 and larger were selected and exported to create the Field boundaries layer. Polygons smaller than 900 m2 represent less than 1 cell in the 30 m x 30 m model and thus were removed. After model setup, model calibration and validation began. Calibration was done manually, as no automatic calibration tools currently exist in IMWEBs-Wetland (Liu et al. 2016). Manual calibration involved altering the sensitive input parameters, within a realistic threshold, to better match the calculated model output with observed field data (Gupta et al. 1998). Model validation involved demonstrating that the model can make sufficiently accurate predictions at a specific site (Refsgaard, 1997). Expert opinion from Yongbo Liu (Personal communication, June 25, 2019) of the most sensitive input parameters to IMWEBs-Wetland was used to select the parameters that were calibrated in this research. The model calibration period was 2011-2017 as there were 7 years of crop inventory data available for years 2011-2017, and the model validation period was the previous 7 years (i.e. 2004-2010). 1997-2003 was run as a warm-up period to allow for a full crop rotation before the

validation period. A warm-up period is required in the IMWEBs-Wetland model to adjust the initial parameter sets. Model calibration was done with the most sensitive input parameters first, namely K_pet (Correction factor for PET), T0 (Snowmelt temperature), and T_snow (Snowfall temperature). For instance, T0 was first altered from 4.0 to 2.0, and the IMWEBs-Wetland model was run again with the new value for T0. The new model results with T0 altered were then compared to the observed data to see if changing T0 resulted in the new model output better matching the observed data. This trial and error approach was repeated until satisfactory model calibration was achieved. Model validation was done after model calibration, by running the IMWEBs-Wetland model for the validation period (i.e. 2004-2010 with 1997-2003 as a warm-up period) with the obtained best-fit parameter set from model calibration. Model calibration and validation were assessed using both graphical comparisons and quantitative evaluators. Graphical comparison involved comparing the simulated model output to the observed data and attempting to match them as best as possible. The model bias, Nash- Sutcliffe efficiency coefficient (NSE), and the coefficient of determination (R2) were used to assess the accuracy of model calibration and validation quantitatively for daily stream discharge. The model bias measures the systematic under or over prediction for a simulation period and reflects the ability of the model to reproduce water balance (Liu and Yang, 2016). Model bias values near 0.0 indicate a good fit, and the value 0.0 represents a perfect simulation of observed stream discharge. Model bias is defined as:

풏 ∑풊=ퟏ 푸풔풊 Equation 3-3 푩풊풂풔 = 풏 − ퟏ ∑풊=ퟏ 푸풐풊 where 푄푠푖 is the simulated daily discharge on day 푖, 푄표푖 is the observed daily discharge on day 푖, and 푛 is the number of daily discharge values (Liu and Yang, 2016). The NSE describes the predictive ability of the model to simulate streamflow. NSE determines the relative magnitude of the residual variance compared to the measured data variance (Nash and Sutcliffe, 1970). The NSE value can range from negative infinity to 1.0, with 1.0 indicating a perfect fit between the simulated and observed stream flow. NSE values between 0.0 and 1.0 are generally viewed as acceptable, whereas values <0.0 indicate that the average observed stream flow would have been a better predictor than the simulated stream flow (Moriasi et al. 2007; Liu and Yang, 2016). NSE is defined as:

풏 ퟐ ∑풊=ퟏ (푶풊− 푺풊) Equation 3-4 푵푺푬 = ퟏ − 풏 ̅ ퟐ ∑풊=ퟏ(푶풊− 푶) where 푂푖 is the observed daily discharge, 푆푖 is the simulated daily discharge, 푂̅ is the average observed discharge throughout the simulation period, and 푛 is the number of daily discharge values (Nash and Sutcliffe, 1970). The R2 is the square of the Pearson’s correlation coefficient (r) (i.e. R2 = r2). The R2 describes the proportion of the total variance in the observed data that can be explained by the model (Legates et al. 1999). R2 values range from 0.0 to 1.0, with higher values indicating less error variance and better agreement. Typically, values greater than 0.5 are considered acceptable (Moriasi et al 2007). R2 is defined as:

ퟐ ∑풏 (푶 −푶̅) (푺 −푺̅) Equation 3-5 푹ퟐ = ( 풊=ퟏ 풊 풊 ) 풏 ̅ ퟐ 풏 ̅ ퟐ √∑풊=ퟏ(푶풊−푶) √∑풊=ퟏ(푺풊−푺)

where 푂푖 is the observed daily discharge, 푂̅ is the average observed daily discharge throughout the simulation period, 푆푖 is the simulated daily discharge, 푆̅ is the average simulated daily discharge throughout the simulation period, and 푛 is the number of daily discharge values (Krause et al. 2005). 3.6 Scenario Analysis The scenario analysis stage involved defining wetland restoration and wetland loss scenarios, to predict the effects that wetlands have on peak flow and low flow in the Lynn River subwatershed. There are three wetland scenarios with varying levels of wetlands present in the watershed, the existing wetland scenario, the wetland restoration scenario, and the loss of existing wetlands scenario (Table 3-8). In addition to these three wetland scenarios, there are also two climate scenarios, the first is a historical climate scenario (January 1, 1984 – December 31, 2014) which uses historical climate data from nearby Environment Canada and Climate Change stations (see Appendix A8). The second climate scenario (January 1, 2015 – December 31, 2045) uses climate change projections from the PRECIS model to predict the impact that the varying levels of wetlands will have under climate change conditions (see Section 3.4.2).

Table 3-8 The six scenarios that will be analysed with the IMWEBs-Wetland model

Scenario Number Climate Scenario Wetland Scenario I Historical (1984-2014) Existing II Historical (1984-2014) Restoration III Historical (1984-2014) Loss IV Climate Change (2015-2045) Existing V Climate Change (2015-2045) Restoration VI Climate Change (2015-2045) Loss

Scenario I is the baseline scenario, employing historical climate data and the existing wetlands in the Lynn River subwatershed. Scenario II (wetland restoration under historical climate scenario) and Scenario III (wetland loss under historical climate scenario) will be compared to Scenario I to predict the impact that wetland restoration and wetland loss will have on peak flows and low flows under historical climate conditions. Scenario V (wetland restoration under climate change scenario) and Scenario VI (wetland loss under climate change scenario) will be compared to Scenario IV (existing wetlands under climate change scenario) to predict the impact that future climate change will have on wetland restoration and wetland loss, respectively. Lastly, Scenario IV will be compared to Scenario I to predict the impact that future climate change will have on the subwatershed. The flow outcomes of wetland restoration that were assessed include the reduction in peak flows and the increase in low flows. Wetlands reduce peak flows by attenuating excess water until groundwater recharge can occur naturally (Javaheri et al. 2014). Peak flows typically occur in springtime during snowmelt events. Moreover, wetlands can also increase low flows during low flow periods by slowly releasing stored water (Bullock et al. 2003). Increasing low flows during summer months when flow is typically low is important for the passage of various fish and aquatic species. Moreover, increasing low flows in the summer months is important for water quality because stagnant rivers will not transport discharged wastewater treatment plant effluent as far as a river with flowing water would. The six scenarios described in Table 3-8 were evaluated at both the subwatershed outlet and at individual wetland locations. Evaluating the six scenarios at the subwatershed outlet enables the impact these scenarios have on the entire subwatershed to be assessed. Moreover, the peak flow decrease at the subwatershed outlet was distributed back to individual wetlands based on the individual wetlands local flow retention values. Individual wetlands act as reservoirs in

the landscape, by retaining local flow until groundwater recharge occurs naturally, or until the depth to drain is reached, at which point flow out of the wetland occurs. Therefore, the peak flow decrease that is observed at the subwatershed outlet is caused by individual wetlands ability to retain local flow. To distribute the peak flow decrease at the subwatershed outlet back to individual wetlands the individual wetlands average flow retention values (see section 4.2.4) were summed, and the percent that each individual wetland contributes to the summed flow retention were calculated. These percentages were used to distribute the average peak flow decrease at the subwatershed outlet back to individual wetlands. Distributing the outlet peak flow decrease back to individual wetlands provides spatially heterogeneous water quantity outcomes for cost-effectiveness analysis. Furthermore, the low flow increases of individual wetlands will also be analyzed to determine if small tributaries near wetland restoration locations have low flow increases. 3.6.1 Subwatershed Outlet Peak Flow Change The subwatershed outlet peak flow change were calculated by first identifying the maximum flow at the outlet for each year of simulation (i.e. 1984-2014 and 2015-2045) under the existing wetland scenario by creating a Pivot table in Excel (Table 3-9). Thus, for each year simulated one maximum flow event at the outlet was identified. The maximum flow at the outlet for each year of simulation was then determined for the wetland restoration scenario by creating another Pivot table in Excel (Table 3-9). It was then confirmed that the maximum flow at the outlet for each year of simulation under the wetland restoration scenario occurred on the same date as the maximum flow at the outlet under the existing scenario using the VLOOKUP function in Excel (Table 3-9). Ensuring that the maximum flows at the outlet occur on the same date under the wetland scenarios ensures that the comparison between scenarios is based on the same storm event. The difference between the maximum flow at the outlet for each year of simulation under the wetland restoration scenario and the existing wetlands scenario were calculated, and these differences for each year of simulation were then averaged to determine the average annual peak flow reduction at the subwatershed outlet due to wetland restoration (Table 3-9). This was also repeated for the wetland loss scenario, by creating a Pivot table in Excel of the maximum flow at the outlet for each year of simulation under the wetland loss scenario (Table 3-10). It was confirmed that the maximum flow at the outlet for each year of simulation under the wetland loss

scenario occurred on the same date as the existing scenario to ensure that the comparison between scenarios is based on the same storm event (Table 3-10). The difference between the maximum flow at the outlet for each year of simulation under the wetland loss scenario and the existing wetlands scenario were calculated, and these differences for each year of simulation were then averaged to determine the average annual peak flow increase at the subwatershed outlet due to the loss of existing wetlands (Table 3-10). The same procedure was used for calculating the subwatershed outlet peak flow changes under the climate change scenario (2015- 2045) for the wetland restoration and wetland loss scenarios.

Table 3-9 Maximum flow at the subwatershed outlet for each year of simulation under the existing wetland scenario and the wetland restoration scenario for the historical climate scenario, and the difference and percent change between the maximum flow at the outlet under the two wetland scenarios

Existing Wetland Wetlands Restoration Date of Maximum Maximum Difference Percent Year Maximum Flow at Flow at (m3/sec) Change Flow Outlet Outlet (m3/sec) (m3/sec) 1984 15.03 14.87 1984-02-13 -0.162 -1.08% 1985 14.23 14.07 1985-02-23 -0.158 -1.11% 1986 7.92 7.77 1986-03-10 -0.147 -1.86% 1987 6.01 5.92 1987-11-25 -0.088 -1.46% 1988 6.38 6.27 1988-07-16 -0.112 -1.76% 1989 5.64 5.57 1989-09-22 -0.064 -1.13% 1990 12.53 12.41 1990-12-29 -0.120 -0.95% 1991 8.41 8.29 1991-07-29 -0.126 -1.50% 1992 8.59 8.47 1992-11-12 -0.127 -1.48% 1993 10.13 10.10 1993-01-04 -0.027 -0.27% 1994 8.98 8.93 1994-02-20 -0.052 -0.58% 1995 8.80 8.64 1995-01-15 -0.154 -1.75% 1996 8.70 8.70 1996-01-18 0.000 0.00% 1997 7.31 7.19 1997-02-20 -0.119 -1.62% 1998 5.09 5.03 1998-01-07 -0.061 -1.20% 1999 7.74 7.70 1999-01-23 -0.049 -0.63% 2000 7.97 7.89 2000-07-09 -0.074 -0.93% 2001 4.85 4.79 2001-02-09 -0.060 -1.24% 2002 3.76 3.71 2002-05-12 -0.054 -1.44% 2003 7.94 7.79 2003-03-17 -0.147 -1.86% 2004 7.50 7.36 2004-12-30 -0.144 -1.92% 2005 6.18 6.09 2005-11-28 -0.089 -1.43% 2006 9.99 9.85 2006-03-09 -0.136 -1.36% 2007 9.03 8.95 2007-03-14 -0.078 -0.86% 2008 10.79 10.73 2008-12-27 -0.056 -0.52% 2009 15.68 15.45 2009-02-11 -0.230 -1.47% 2010 6.18 6.05 2010-12-31 -0.133 -2.15% 2011 10.68 10.60 2011-03-10 -0.084 -0.78% 2012 4.65 4.61 2012-10-29 -0.039 -0.85% 2013 9.32 9.16 2013-12-21 -0.158 -1.69% 2014 6.68 6.59 2014-11-23 -0.091 -1.37% Average -0.101 -1.23%

Table 3-10 Maximum flow at the subwatershed outlet for each year of simulation under the existing wetland scenario and the wetland loss scenario for the historical climate scenario, and the difference and percent change between the maximum flow at the outlet under the two wetland scenarios

Existing Wetland Wetlands Loss Date of Maximum Maximum Difference Percent Year Maximum Flow at Flow at (m3/sec) Change Flow Outlet Outlet (m3/sec) (m3/sec) 1984 15.03 15.22 1984-02-13 0.195 1.30% 1985 14.23 14.45 1985-02-23 0.222 1.56% 1986 7.92 8.01 1986-03-10 0.099 1.25% 1987 6.01 6.14 1987-11-25 0.132 2.20% 1988 6.38 6.54 1988-07-16 0.158 2.48% 1989 5.64 5.79 1989-09-22 0.151 2.67% 1990 12.53 12.65 1990-12-29 0.120 0.96% 1991 8.41 8.59 1991-07-29 0.178 2.11% 1992 8.59 8.75 1992-11-12 0.154 1.79% 1993 10.13 10.24 1993-01-04 0.110 1.08% 1994 8.98 9.19 1994-02-20 0.209 2.33% 1995 8.77 8.80 1995-01-15 0.026 0.29% 1996 8.70 8.88 1996-01-18 0.180 2.07% 1997 7.31 7.41 1997-02-20 0.096 1.31% 1998 5.09 5.15 1998-01-07 0.062 1.21% 1999 7.74 7.94 1999-01-23 0.196 2.54% 2000 7.97 8.11 2000-07-09 0.149 1.87% 2001 4.85 4.89 2001-02-09 0.040 0.83% 2002 3.76 3.82 2002-05-12 0.054 1.45% 2003 7.94 8.11 2003-03-17 0.174 2.19% 2004 7.50 7.62 2004-12-30 0.121 1.61% 2005 6.18 6.25 2005-11-28 0.067 1.08% 2006 9.99 10.13 2006-03-09 0.136 1.36% 2007 9.03 9.21 2007-03-14 0.183 2.03% 2008 10.79 10.88 2008-12-27 0.084 0.78% 2009 15.68 15.91 2009-02-11 0.232 1.48% 2010 6.18 6.34 2010-12-31 0.156 2.53% 2011 10.68 10.85 2011-03-10 0.168 1.58% 2012 4.65 4.73 2012-10-29 0.079 1.70% 2013 9.32 9.43 2013-12-21 0.106 1.14% 2014 6.68 6.84 2014-11-23 0.161 2.42% Average 0.134 1.63%

The peak flow reduction (i.e. ‘Difference’ in Table 3-9) for each year of simulation at the subwatershed outlet under the wetland restoration scenario listed in Table 3-9 were used to create a probability distribution of peak flow reduction at the subwatershed outlet due to wetland restoration. The probability distribution was used to determine the 10 year and 30 year return periods for peak flow reduction due to wetland restoration. A 10 year return period represents the reduction in peak flow that would occur on average once every ten years due to wetland restoration. Similarly, a 30 year return period represents the reduction in peak flow that would occur on average once every 30 years due to wetland restoration. Peak flow decreases with a 10 year return period and a 30 year return period were calculated in addition to the average peak flow decrease to show a range of probable peak flow reduction values across the simulation period. To create the probability distributions of peak flow reduction at the subwatershed outlet due to wetland restoration, first the ‘Difference’ for each year of simulation were sorted and ranked in descending order (Table 3-11). Second, the probability of flow reduction being larger than the sorted and ranked ‘Difference’s were calculated as the rank (i.e. third column in Table 3-11) divided by the total number of years of simulation plus one (i.e. 32) (Table 3-11). Third, the return period was calculated for each year of simulation as one divided by the calculated probability (Table 3-11). Lastly, the ‘Difference’ values and the associated return periods for each year of simulation were graphed on a scatter plot in Excel, and a logarithmic best fit line and equation for the best fit line were added (see section 4.2.2). The best fit line equations were used to calculate the 10 year and 30 year return periods. The same procedure was used for determining the 10 year and 30 year return periods for flow reduction at the outlet due to wetland restoration under the climate change scenario (i.e. 2015-2045).

Table 3-11 Probability distribution and return period preparation

Probability Difference Rank of Larger (Reduction Return period (highest Peak Flow Year in Peak (years) (i.e. to Reduction Flow) 1/‘Probability’) lowest) (i.e. (m3/s) ‘Rank’/32) 2009 0.230 1 0.031 32.00 1984 0.162 2 0.063 16.00 1985 0.158 3 0.094 10.67 2013 0.158 4 0.125 8.00 1995 0.154 5 0.156 6.40 2003 0.147 6 0.188 5.33 1986 0.147 7 0.219 4.57 2004 0.144 8 0.250 4.00 2006 0.136 9 0.281 3.56 2010 0.133 10 0.313 3.20 1992 0.127 11 0.344 2.91 1991 0.126 12 0.375 2.67 1990 0.120 13 0.406 2.46 1997 0.119 14 0.438 2.29 1988 0.112 15 0.469 2.13 2014 0.091 16 0.500 2.00 2005 0.089 17 0.531 1.88 1987 0.088 18 0.563 1.78 2011 0.084 19 0.594 1.68 2007 0.078 20 0.625 1.60 2000 0.074 21 0.656 1.52 1989 0.064 22 0.688 1.45 1998 0.061 23 0.719 1.39 2001 0.060 24 0.750 1.33 2008 0.056 25 0.781 1.28 2002 0.054 26 0.813 1.23 1994 0.052 27 0.844 1.19 1999 0.049 28 0.875 1.14 2012 0.039 29 0.906 1.10 1993 0.027 30 0.938 1.07 1996 0.000 31 0.969 1.03 3.6.2 Subwatershed Outlet Low Flow Change The subwatershed outlet low flow increase was determined by first identifying time periods in each year of simulation that typically exhibit low flow at the subwatershed outlet. The hydrograph of the subwatershed outlet was visually inspected to identify months that typically

have low flows across all years. July to September were assumed to be low flow time periods based on the visual inspection. The July to September predictions of subwatershed outlet flow for each year of simulation under the existing wetland scenarios, wetland restoration scenarios, and wetland loss scenarios were imported to a new Excel spreadsheet. A 7 day moving average of the subwatershed outlet flow was determined for each day of simulation in the July to September low flow periods for each of the six scenarios. A 7-day moving average was used to calculate 7Q10, which is the lowest 7 day average flow that occurs (on average) once every 10 years (EPA, 2018). 7Q10 is a design flow statistic that is used by many regulatory agencies to define low flow for setting discharge limits (EPA, 2018). A Pivot table in Excel was created to determine the minimum 7 day moving average for each year of simulation under the six scenarios. It was then confirmed that the minimum 7 day moving average under the wetland restoration scenarios and the wetland loss scenarios for each year of simulation occurred on the same date as the minimum 7 day moving average under the existing scenarios to ensure the comparison between scenarios is based on the same storm event. 3.6.3 Individual Wetlands Flow Retention Individual wetlands act as reservoirs in the landscape, by retaining local flow until groundwater recharge occurs naturally, or until the depth to drain is reached, at which point flow out of the wetland occurs. Moreover, the reduction in peak flow that is observed at the subwatershed outlet is caused by individual wetlands’ ability to retain local flow. The flow that goes into each wetland represents the flow that would normally occur if that wetland were not present. The flow that goes out of the wetland represents the local flow regime with the wetland present. Thus, the difference between the flow into the wetland and the flow out of the wetland is the flow retention of that wetland. After determining the maximum flow into each wetland for each year of simulation, the maximum flow out of the wetland that is associated with that same event needed to be determined. The maximum flow out that is associated with the same event as the maximum flow in may not occur on the same date, as there is often a lag time between the maximum flow in and the maximum flow out, as shown in Figure 3-10. The lag time between the maximum flow in and the maximum flow out that is associated with that same storm event was defined as 0-5 days.

Representative Wetland Restoration Storm Event Flow in and Flow out

0.008 /s)

3 0.006 0.004 0.002

Flow Flow (m 0

Date

Flow in Flow out

Figure 3-10 A representative wetland restoration location flow in and flow out under a storm event

First, the maximum flow into each wetland for each year of simulation was determined with a Pivot table in Excel. Second, VLOOKUP in Excel was used to identify the days (i.e. rows in the raw data) that contain the maximum flow into each wetland for each year of simulation. Third, an IF AND statement was used in Excel to calculate the flow retention associated with the maximum flow into each wetland for each year of simulation. The IF AND statement insured that the next 5 rows in the raw data had the same wetland ID, checked whether the flow into that wetland on that day is the maximum flow into that wetland for that year (i.e. from previously created Pivot table), found the maximum flow out of that wetland that is within the 0-5 day range, and differenced the maximum flow in and the maximum flow out within the 0-5 day range to determine wetland flow retention. The flow retention of each year for each wetland were then averaged, to determine the average flow retention of each wetland. The average flow retention of each wetland was then used to distribute the subwatershed decrease in outlet peak flow back to individual wetland restoration locations. The decrease in peak flow that is modelled at the subwatershed outlet is caused by individual wetlands’ ability to retain local flow. To distribute the peak flow decrease at the subwatershed outlet back to individual wetlands the individual wetlands average flow retention values (see section 4.2.4) were summed, and the percent that each individual wetland contributes to the summed flow retention were calculated. These percentages were used to distribute the average peak flow decrease at the subwatershed outlet back to individual wetlands.

3.6.4 Individual Wetlands Increase in Low Flow Individual wetlands low flow increases were determined with only the July to September data (i.e. the identified low flow period of each year) for all years of simulation. The flow that goes into each wetland represents the flow that would normally occur if that wetland were not present. The flow that goes out of the wetland represents the flow regime with the wetland present. Thus, the difference between the flow into the wetland and the flow out of the wetland is the water quantity outcome of that wetland. The days that flow out of the wetland is larger than flow into the wetland were flagged and these days were imported to a new spreadsheet in Excel. The days with flow out of the wetland larger than flow into the wetland signify days with low flow increases due to the presence of the wetland. The difference between wetland flow out and wetland flow in on the days that have wetland flow out larger than wetland flow in was calculated, and these differences were averaged for each wetland location, to determine the average low flow increase of each wetland. 3.7 Wetland Economic Cost Estimation The wetland economic model is comprised of three main cost categories, the opportunity cost of the forgone cropping returns, the restoration administration and engineering costs, and the inconvenience costs associated with farming a field with one or several restored wetlands present. This section describes these three cost categories and the methods used to combine them to establish the annual cost of wetland restoration in the Lynn River subwatershed. 3.7.1 Opportunity Costs The average net return before land and overhead costs for corn, soybeans, winter wheat, and hay listed in Table 3-7 were represented in the wetland economic model with available crop inventory data for years 2011-2017. First, the dollar values in Table 3-7 were multiplied by 100 to convert from dollars to cents, because the Reclassify tool in ArcGIS works best with integer values. Second, the annual crop inventory rasters were reclassified using the Reclassify tool in ArcGIS to represent cropping gross revenue, so that all corn cells are 75,980 cents, all soy cells are 65,787 cents, all wheat cells 26,868 cents, all hay cells 17,112 cents, and all remaining cells were reclassified as No Data. Third, the Cell Statistics tool in ArcGIS was used to calculate the average value of each cell using the 2011-2017 reclassified crop inventories as inputs. The Cell Statistics tool calculates the average revenue of each cell based on the above reclassification and

ignores No Data values in the average calculation. The Raster Calculator in ArcGIS was then used to convert from cents to dollars (i.e. divide by 100). The Zonal Statistics as Table tool in ArcGIS was used on the averaged raster (i.e. the output from Cell Statistics tool), using the field boundaries as the feature zone data, to calculate the average annual revenue of each field. The resultant database table was Joined to the field boundaries layer and exported to a new layer to make the join permanent. The average return raster is shown in Figure 3-11. The 2017 Farmland Value and Rental Value Survey by Deaton (2018) found the approximate cash rent per tillable acre in the Haldimand-Norfolk Census Division to be $200 (i.e. $494.20/ha). The estimated average net return of the subwatershed should be near $494.20/ha, as land rental rates typically reflect the average return of that region. The average return of Figure 3-11 is $523.44/ha, suggesting that the spatial representation done above is within acceptable range.

Figure 3-11 The Lynn River subwatershed average annual return before land and overhead costs

3.7.2 Wetland Restoration Administration and Engineering Costs The wetland restoration administration and engineering costs listed in Table 3-12 are estimates from the ALUS Norfolk Coordinator, Stephanie Drayer. Stephanie Drayer has been involved in several ALUS wetland restoration projects in Norfolk County. Stephanie Drayer (Personal communication, May 27, 2019) explained that the wetland restoration engineering costs are primarily composed of contracted excavation services with a long-reach excavator. Moreover, Stephanie Drayer (Personal communication, May 27, 2019) states that the wetland restoration administration costs include site assessment, contractor communications, meeting approvals, and paperwork, with both herself and a Long Point Region Conservation Authority employee. The wetland restoration administration and engineering costs listed in Table 3-12 are approximately 25 percent more than the wetland restoration administration and engineering costs reported by Ducks Unlimited Canada (Pattison et al. 2011). The wetland restoration and engineering costs listed in Table 3-13 are associated with typical DUC wetland restoration projects in southern Ontario. These values were converted to Canadian 2018 dollars for comparison. The DUC costs may be lower than the ALUS Norfolk estimated costs because DUC undertakes many wetland restoration projects throughout Ontario. Contrastingly, ALUS Norfolk undertakes many projects in addition to wetland restoration, such as reforestation, creation of pollinator habitats, and grassed waterways. Thus, the discrepancy between the DUC and the ALUS Norfolk estimates for wetland restoration administration and engineering costs is likely due to the diversity of projects ALUS Norfolk delivers, whereas DUC primarily restores wetlands. The wetland restoration administration and engineering costs were evaluated for their sensitivities by increasing these costs by 1 percent and recalculating the annual cost of wetland restoration with the 1 percent increase. Table 3-12 Wetland restoration administration and engineering cost estimates for the Lynn River subwatershed

Wetland Restoration Wetland Restoration Components Costs Units Engineering costs (excavation with a long-reach excavator) 37,065.00 $/Hectare Administration 1,050.00 $/Wetland Total 38,115.00 $/1 Hectare Wetland

Table 3-13 Ducks Unlimited Canada (2011) wetland restoration administration and engineering cost estimates for southern Ontario

Wetland Restoration Costs Wetland Restoration Cost Components ($/hectare) Direct and indirect construction costs 15,273.55 Pre-construction indirect costs 9,545.96 Future Management Costs 3,818.39 Administration 1,909.19 Total 30,547.10 *Note all monetary values are CAD2018. Future Management Costs are those associated with the ongoing operation and maintenance of the project into the future (usually a 20-30 year term) (Pattison et al. 2011).

3.7.3 Inconvenience Costs In a Saskatchewan study, Cortus et al. (2011) estimates inconvenience costs for wetlands in farm fields as the percentage increase in crop inputs costs due to wetland nuisance factors by farm size and number of wetlands per farm. Cortus et al. (2011) report that for farms one square mile and smaller, with one to three wetlands present, there is an 8 percent increase in machinery costs. All the fields in this study are smaller than one square mile and contain three or less wetlands, thus an 8 percent increase in machinery costs will be used for all wetland restoration locations in the Lynn River subwatershed. Machinery costs were taken from Ontario Ministry of Agriculture, Food and Rural Affairs Field Crop Budgets Publication 60 for years 2008-2017. The yearly machinery costs were converted to CAD2018 and averaged to derive a ten year average machinery cost for each crop type. Table 3-14 shows the ten year average of 8 percent of machinery costs. Table 3-14 Ten year average of 8 percent of machinery costs

Inconvenience Crop Costs ($/ha/yr) CAD2018 Grain Corn 8.65 Soybeans 4.55 Winter Wheat 5.93 Hay 7.44

The inconvenience costs were spatially represented using similar methods as the opportunity costs were represented in section 3.7.1. First, the dollar values in Table 3-14 were multiplied by 100 to convert from dollars to cents, because the Reclassify tool in ArcGIS works best with integer values. Second, the annual crop inventory rasters for years 2011-2017 were reclassified to represent inconvenience cost using the Reclassify tool in ArcGIS, so that all corn cells are 865 cents, all soy cells are 455 cents, all wheat cells 593 cents, all hay cells 744 cents, and all remaining cells were reclassified as No Data. Third, the Cell Statistics tool in ArcGIS was used to calculate the average inconvenience cost of each cell using the 2011-2017 reclassified crop inventories as inputs. The Cell Statistics tool calculates the average inconvenience cost of each cell based on the above reclassification and ignores No Data values in the average calculation. The Raster Calculator in ArcGIS was then used to convert from cents to dollars (i.e. divide by 100). The Zonal Statistics as Table tool in ArcGIS was used on the averaged raster (i.e. the output from Cell Statistics tool), using the field boundaries as the feature zone data, to calculate the average inconvenience cost of each field. Thus, each wetland restoration location had a unique inconvenience cost other than any wetlands that were within the same field. 3.7.4 Annual Cost of Wetland Restoration The annual cost of wetland restoration consists of three main cost categories, the opportunity cost of the forgone cropping returns, the amortized restoration administration and engineering costs, and the inconvenience costs. This section describes the methods used to combine these costs to establish the annual cost of wetland restoration in the Lynn River subwatershed. The Intersect tool in ArcGIS was used to combine the attribute data from the average inconvenience cost of each field layer with the wetland restoration layer. Intersecting the attribute data from the average inconvenience cost of each field layer with the wetland restoration layer resulted in all wetlands having a unique inconvenience costs ($/ha) other than any wetlands that are within the same field. The same procedure was used to combine the attribute data from the average opportunity cost of each field with the wetland restoration layer. Similarly, all 59 wetland restoration locations had a unique opportunity cost ($/ha) other than wetlands that were within the same field. The attribute table was then exported as a text file and opened in Excel for further analysis. A new column was added for administration costs, and this column was populated with

$1,050/wetland for all wetland restoration locations. The administration costs were then converted from dollars per wetland to dollars per hectare by dividing the administration costs (i.e. $1,050/wetland) by each wetlands area in hectares. Thus, each identified wetland restoration location had a unique administration cost based on wetland surface area. Another new field was added for engineering costs, and this field was populated with $37,065/ha. Wetland restoration administration and engineering costs were then calculated as the per hectare administration costs plus the per hectare engineering costs. The wetland restoration administration and engineering costs are initial fixed costs. Whereas the opportunity costs and the inconvenience costs are ongoing annual costs. I amortized the initial fixed costs to spread the initial fixed cost over the expected lifespan of the wetland. Amortization was calculated with a 5 percent interest rate for 31 years, with the following equation:

풓(ퟏ+풓)풏 Equation 3-6 푨 = 푷 (ퟏ+풓)풏−ퟏ where 퐴 is the payment amount per period, 푃 is the initial principle amount, 푟 is the interest rate per period, and 푛 is the total number of payment periods. The wetland restoration lifespan was assumed to be 31 years based on the IMWEBs- Wetland simulation period. Moreover, the PRECIS future climate change scenario is a 31 year simulation. Furthermore, in the literature, researchers have used 20 year lifespans (Chen et al. 2009; Shultz and Leitch, 2003; Moudrak et al. 2018; Yang et al. 2016a), 25 year lifespans (Roach and Wade, 2006), 30 year lifespans (Ducks Unlimited Canada, 2015), and 50 year lifespans (Olof, 1998; Iovanna et al. 2008; Zanou et al. 2003) for wetland restoration projects. A 5 percent interest rate was taken from Kula (1984) estimate of the social time preference rate in Canada. A sensitivity analysis was performed on the assumed wetland lifespan as well as the chosen interest rate due to the uncertainty associated with these values (see section 4.4). Table 3-15 presents the annual cost of wetland restoration for all 59 identified wetland restoration locations. The annual opportunity costs, annual inconvenience costs, and the amortized administration and engineering costs are all expressed as $/ha/year. The annual cost of wetland restoration ($/ha/year) has been sorted in descending order with the most expensive wetlands presented first. The annual cost of wetland restoration in the Lynn River subwatershed ranges from a minimum annual cost of $2,735.69/ha/year to a maximum of $3,461.16/ha/year

with an average annual cost of $3,106.71/ha/year. Comparatively, Yang et al. (2016a) found that the annual economic cost of wetland restoration in the South Tobacco Creek watershed in Manitoba ranged from a minimum of $20.90/ha/year to a maximum of $409.90/ha/year with an average of $132.40/ha/year. Yang et al. (2016a) calculated annual costs are much smaller than the annual costs calculated in this research because their restoration administration cost was $321.00/wetland and their engineering cost was $387.90/ha. The discrepancy between wetland restoration administration and engineering costs is likely because Yang et al. (2016a) study site is in Manitoba whereas the study site for this research is in southern Ontario. Table 3-15 Annual cost of wetland restoration for all 59 identified wetland restoration locations

Administration Average Annual Average & Engineering Annual Cost Wetland Annual Cost of Wetland Annual Costs amortized of Wetland Area Opportunity Wetland ID Inconvenience for 31 years with Restoration (ha) Cost Restoration Cost ($/ha/yr) a 5% interest rate ($) ($/ha/yr) ($/ha/yr) ($/ha/yr) 64 0.18 702.38 7.62 2,751.16 3,461.16 623.01 94 0.18 675.57 7.48 2,751.16 3,434.21 618.16 137 0.18 624.06 6.70 2,751.16 3,381.91 608.74 90 0.18 600.85 5.61 2,751.16 3,357.62 604.37 50 0.27 689.61 6.97 2,626.46 3,323.03 897.22 86 0.27 671.94 6.59 2,626.46 3,304.99 892.35 112 0.27 648.19 6.95 2,626.46 3,281.61 886.03 18 0.27 642.91 6.96 2,626.46 3,276.33 884.61 101 0.27 635.25 6.04 2,626.46 3,267.76 882.29 20 0.45 723.24 7.47 2,526.70 3,257.41 1,465.84 34 0.45 704.23 6.95 2,526.70 3,237.88 1,457.04 11 0.27 593.59 6.95 2,626.46 3,227.00 871.29 47 0.45 689.61 6.97 2,526.70 3,223.27 1,450.47 115 0.36 646.47 6.68 2,564.11 3,217.25 1,158.21 67 0.27 576.18 5.61 2,626.46 3,208.25 866.23 85 0.45 671.94 6.59 2,526.70 3,205.23 1,442.35 35 0.81 727.55 7.79 2,460.19 3,195.52 2,588.38 118 0.54 678.57 6.76 2,501.76 3,187.09 1,721.03 52 0.63 689.61 6.97 2,483.94 3,180.52 2,003.73 128 0.18 416.73 5.54 2,751.16 3,173.43 571.22 65 0.18 409.80 6.48 2,751.16 3,167.44 570.14 104 0.36 593.79 6.28 2,564.11 3,164.18 1,139.10 9 0.54 634.35 6.96 2,501.76 3,143.06 1,697.25 78 0.18 383.83 6.47 2,751.16 3,141.46 565.46 109 0.63 646.47 6.68 2,483.94 3,137.09 1,976.37 51 1.08 689.61 6.97 2,439.41 3,135.98 3,386.86 24 0.45 601.37 6.43 2,526.70 3,134.51 1,410.53 21 0.63 642.91 6.96 2,483.94 3,133.81 1,974.30

Table 3-15 Annual cost of wetland restoration for all 59 identified wetland restoration locations, Continued Administration Average Annual Average & Engineering Annual Cost Wetland Annual Cost of Wetland Annual Costs amortized of Wetland Area Opportunity Wetland ID Inconvenience for 31 years with Restoration (ha) Cost Restoration Cost ($/ha/yr) a 5% interest rate ($) ($/ha/yr) ($/ha/yr) ($/ha/yr) 124 0.54 621.64 5.98 2,501.76 3,129.37 1,689.86 22 0.54 601.37 6.43 2,501.76 3,109.57 1,679.17 39 0.63 614.84 6.96 2,483.94 3,105.74 1,956.62 30 0.63 614.80 5.90 2,483.94 3,104.65 1,955.93 97 0.81 635.25 6.04 2,460.19 3,101.49 2,512.21 42 0.27 458.43 6.15 2,626.46 3,091.05 834.58 16 0.27 457.84 6.07 2,626.46 3,090.38 834.40 111 1.53 648.19 6.95 2,421.07 3,076.22 4,706.61 58 0.45 538.62 6.79 2,526.70 3,072.10 1,382.45 107 2.79 648.19 6.95 2,401.19 3,056.34 8,527.19 73 0.45 513.07 6.16 2,526.70 3,045.92 1,370.66 103 0.72 561.79 5.39 2,470.58 3,037.77 2,187.20 98 0.27 401.06 6.48 2,626.46 3,034.00 819.18 45 0.18 272.10 7.03 2,751.16 3,030.30 545.45 139 0.18 253.86 6.93 2,751.16 3,011.95 542.15 28 1.62 570.15 7.04 2,418.62 2,995.82 4,853.23 15 0.45 457.84 6.07 2,526.70 2,990.62 1,345.78 127 0.36 416.73 5.54 2,564.11 2,986.38 1,075.10 116 0.45 436.06 6.90 2,526.70 2,969.66 1,336.35 122 0.72 490.03 6.51 2,470.58 2,967.12 2,136.32 74 0.36 383.83 6.47 2,564.11 2,954.41 1,063.59 14 0.99 494.53 6.39 2,445.08 2,945.99 2,916.53 123 1.17 490.03 6.51 2,434.61 2,931.15 3,429.44 136 0.63 440.32 6.10 2,483.94 2,930.36 1,846.13 43 0.9 458.43 6.15 2,451.88 2,916.47 2,624.82 106 0.36 320.44 6.25 2,564.11 2,890.80 1,040.69 41 1.26 447.23 6.13 2,430.50 2,883.86 3,633.66 26 0.54 344.58 7.18 2,501.76 2,853.52 1,540.90 33 0.63 359.86 5.87 2,483.94 2,849.68 1,795.30 126 1.71 416.73 5.54 2,416.44 2,838.71 4,854.19 80 0.99 283.53 7.09 2,445.08 2,735.69 2,708.34 Average 0.58 546.81 6.55 2,553.35 3,106.71 1,778.92 Note The annual cost of wetland restoration ($/ha/year) has been sorted in descending order with the most expensive wetlands presented first.

3.8 Cost-Effectiveness Analysis The cost-effectiveness of reductions in peak flow due to wetland restoration were determined at both the subwatershed outlet and at individual wetland restoration locations. The cost-effectiveness of peak flow decreases at the subwatershed outlet is important for

conservation managers and decision makers to see the impact of 100 percent wetland restoration at the subwatershed outlet. Moreover, determining the cost-effectiveness at the subwatershed outlet indicates whether wetland restoration is a feasible option for decreasing peak flows in the subwatershed. Furthermore, determining the cost-effectiveness of the peak flow decreases at the subwatershed outlet that each individual wetland restoration location contributes to is important for targeting wetland restoration efforts to locations with high cost-effectiveness. The cost-effectiveness of peak flow decreases at the subwatershed outlet was calculated with the average peak flow decrease as well as the peak flow decrease with a 10 year return period and the peak flow decrease with a 30 year return period. The average, 10 year return period, and 30 year return period measures for peak flow decreases were used to show a range of possible peak flow decreases due to wetland restoration at the subwatershed outlet. The cost- effectiveness of peak flow decreases at the subwatershed outlet was calculated with the annual cost of restoring all 59 identified wetland restoration locations in the subwatershed, expressed in dollars. The annual cost of restoring all 59 wetlands was calculated by summing each individual wetlands annual cost of wetland restoration, expressed in dollars (i.e. summing the 7th column in Table 3-15). Cost-effectiveness (expressed as $/m3/day) was calculated as the annual cost of restoring all 59 wetland restoration locations in the subwatershed divided by the average peak flow decrease, as well as the peak flow decrease with a 10 year return period and the peak flow decrease with a 30 year return period at the subwatershed outlet. The cost-effectiveness of peak flow decreases at individual wetland restoration locations uses the average peak flow decrease at the subwatershed outlet, and distributes this peak flow decrease back to individual wetlands based on individual wetlands local flow retention values. The individual wetlands average flow retention values (see section 4.2.4) were summed, and the percent that each individual wetland contributes to the summed flow retention were calculated. This percentage was then used to distribute the average peak flow decrease at the subwatershed outlet back to individual wetlands. Distributing the outlet peak flow decrease back to individual wetlands enables the spatial heterogeneity of the economic costs and peak flow decreases to be evaluated. Cost-effectiveness was calculated for each wetland as its annual cost (expressed in dollars) divided by its contribution to the average annual peak flow decrease at the subwatershed outlet (expressed in m3/day) to determine its cost-effectiveness (expressed in $/m3/day).

3.9 Economic Sensitivity Analysis A sensitivity analysis was performed on the annual cost of wetland restoration, expressed in $/ha/year. The sensitivity analysis was done to determine which input parameters have the greatest impact on the annual cost of wetland restoration. Determining which input parameters have the greatest impact on the annual cost of wetland restoration illustrates the uncertainty of the wetland economic model results, as highly sensitive input parameters increase the uncertainty of the calculated annual cost. The wetland economic cost estimation is inherently uncertain because the wetland restoration administration and engineering costs and the wetland inconvenience costs are taken from personal communication and literature, respectively. Furthermore, the choice of interest rate and the wetland lifespan were also analysed for their sensitivity due to the uncertainty associated with these values. To analyze sensitivity, each input parameter was increased by 1 percent, and the annual cost (expressed in $/ha/year) was recalculated with the increased parameter. For instance, administration costs were increased by 1 percent, and the annual cost of wetland restoration (expressed in $/ha/year) was recalculated with the 1 percent increase in administration costs. This was repeated for engineering costs, inconvenience costs, and the opportunity costs. The interest rate was analysed for sensitivity by changing the interest rate to 3 percent and 8 percent. Moreover, wetland lifespan was analysed for sensitivity by changing wetland lifespan to 20 years and 50 years. Equation 3-7 was used to calculate sensitivity elasticity, or the percent change in model output caused by a percentage change in an input parameter.

(푸 −푸 )÷(푸 +푸 ) Equation 3-7 풏 = ퟏ ퟎ ퟏ ퟎ (푷ퟏ−푷ퟎ)÷(푷ퟏ+푷ퟎ) where 푄1 is the newly calculated model output, 푄0 is the original model output, 푃1 is the input parameter value with a 1 percent increase, 푃0 is the original input parameter value.

Chapter 4 Results and Discussion

4.1 IMWEBs-Wetland Calibration and Validation The model calibration and validation stage aimed to match the model output with field observed data to ensure the model can accurately predict the water quantity outcomes of wetland restoration. The calculated stream discharges were compared to observational data, and the best input parameter set was one that minimized model bias, maximized Nash-Sutcliffe coefficient (NSE), and maximized the coefficient of determination (R2). The IMWEBs-Wetland model ran for the January 1, 2011 – December 31, 2017 calibration period and the January 1 2004 – December 31, 2010 validation period, in addition to the January 1, 1997 – December 31, 2003 model warm-up period, using the best-fit parameter set in Table 4-1. Table 4-2 shows the model bias, Nash-Sutcliffe coefficient, and the coefficient of determination obtained with the best-fit parameter set. The obtained model bias values shown in Table 4-2 for the calibration and validation periods indicate that the model is slightly underpredicting stream discharge, by 0.87 percent and 0.60 percent, respectively. The obtained NSE values of 0.58 and 0.44 shown in Table 4-2 for the calibration and validation periods indicate that the model is a better predicter of stream discharge than the average of the observed streamflow. The obtained R2 values of 0.58 and 0.47 shown in Table 4-2 for the calibration and validation periods indicate that the dispersion of the predicted streamflow is close to the dispersion of the observed streamflow. Figure 4-1 and Figure 4-2 show the flow calibration and flow validation comparisons of the observed daily streamflow to the IMWEBs-Wetland calculated streamflow between 2011-2017 and 2004-2010, respectively, along with daily precipitation and average temperature values. Figure 4-1 and Figure 4-2 indicate that the IMWEBs-Wetland model simulates discharge satisfactorily.

Table 4-1 IMWEBs-Wetland calibrated input parameters for the Lynn River subwatershed

IMWEBs- Acceptable Calibrated Wetland input Description Default value range value parameter K_pet Correction factor for PET 1.0 0.6 - 1.3 0.6 T0 Snowmelt temperature 4.0 (-)1.0 - 4.0 0.0 T_snow Snowfall temperature 1.5 (-)2.0 - 2.0 -1.0 Dependant Increase all runoff_co Potential runoff coefficient 0.0 - 10.0 on land cover values by 20% Dependant Decrease all fieldcap_layer1 Soil field capacity 0.0 - 1.0 on soil type values by 30% Channel bottom hydraulic k_chb 10.0 0.0 - 150.0 8.0 conductivity ki Interflow scale factor 30.0 0.0 - 10,000 130.0 kg Baseflow recession coefficient 0.001 0.0 - 1.0 0.009 Ratio between soil temperature k_soil10 1.0 0.0 - 3.0 2.25 at 10 cm and the mean

Table 4-2 Lynn River subwatershed IMWEBs-Wetland calibration and validation model bias, Nash-Sutcliffe coefficient, and coefficient of determination values

Flow Calibration Flow Validation (2011-2017) (2004-2010) Bias -0.87% Bias -0.60% NSE 0.581 NSE 0.435 R2 0.582 R2 0.472

2010, 2010,

2017, along 2017,

with daily precipitation and average air temperature air and average withprecipitation daily

along with daily precipitation and average air temperature air and alongprecipitation average daily with

Flow validation daily comparison of measured daily flow to the calculated flow 2004 for calculated the daily flow to measured of daily comparison validation Flow

Flow calibration daily comparison of measured flow flow for calculated 2011 of the to measured daily calibration Flow comparison

Figure Figure

4.2 IMWEBs-Wetland Scenario Analysis The scenario analysis stage involved defining wetland restoration and wetland loss scenarios, to predict the effects that wetlands have on peak flow and low flow in the Lynn River subwatershed. There are three scenarios with varying levels of wetlands present in the watershed, the existing wetland scenario, the wetland restoration scenario, and the loss of existing wetlands scenario (Table 4-3). In addition to these three wetland scenario, there are also two climate scenarios, the first is a historical climate scenario (January 1, 1984 – December 31, 2014) which uses historical climate data from nearby Environment Canada and Climate Change stations (see Appendix A8). The second climate scenario (January 1, 2015 – December 31, 2045) uses climate change projections from the PRECIS model to predict the impact that the varying levels of wetlands will have under climate change conditions (see Section 3.4.2). Table 4-3 The six scenarios that will be analysed with the IMWEBs-Wetland model

The six scenarios described in Table 4-3 were evaluated at both the subwatershed outlet and at individual wetlands locations. Evaluating the peak flow decreases and low flow increases at the subwatershed outlet enables the impact that wetlands have on the entire subwatershed to be analyzed. Moreover, the average peak flow decrease at the subwatershed outlet was distributed back to individual wetlands based on the individual wetlands local flow retention values. Briefly, the individual wetlands average flow retention values (see section 4.2.4) were summed, and the percent that each individual wetland contributes to the summed flow retention were calculated. These percentages were then used to distribute the average peak flow decrease at the subwatershed outlet back to individual wetlands. Distributing the outlet peak flow decrease back to individual wetlands provides spatially heterogeneous water quantity outcomes for cost- effectiveness analysis. Furthermore, the low flow increases of individual wetlands will also be analyzed to determine if small tributaries near wetland restoration locations have low flow increases.

4.2.1 Comparison of Streamflow under Historical Climate and Climate Change Scenarios Table 4-4 presents the average, median, minimum, maximum, and variance of daily streamflow at the subwatershed outlet under the historical climate and climate change scenarios with existing wetlands. The historical climate scenario (1984-2014) uses historical climate data from nearby Environment Canada and Climate Change stations, and the climate change scenario (2015-2045) uses climate change projections from the PRECIS model. The average, median, minimum, maximum, and variance of streamflow were calculated with the IMWEBs-Wetland predicted daily streamflow at the subwatershed outlet and Excel was used to find the average, median, minimum, maximum, and variance for both climate scenarios. The climate change scenario resulted in a 10.49 percent increase in average daily flow, 15.45 percent increase in median daily flow, 45.15 percent increase in minimum daily flow, 29.02 percent decrease in maximum daily flow, and a 17.89 percent decrease in daily flow variance compared to the historical climate scenario. Figure 4-3 and Figure 4-4 depict streamflow at the subwatershed outlet with existing wetlands under the historical climate and climate change scenarios, respectively. Outlet flow in Figure 4-3 and Figure 4-4 have been presented on axis’s with the same scale for ease of comparison. Figure 4-3 and Figure 4-4 illustrate that maximum flows are higher and that minimum flows are lower under the historical climate scenario. Moreover, streamflow variance is smaller under the climate change scenario compared to the historical scenario. Thus, outlet flow under the historical climate scenario exhibits more variability than under the climate change scenario. The higher outlet flow variability under the historical climate scenario is likely due to the different sources of climate data. The raw historical precipitation data contains more days with no precipitation (i.e. value 0.0) compared to the climate change prediction data which contains more days with precipitation values ranging from 0.0 to 1.0. Moreover, the average annual precipitation increases from 947 mm/year under the historical climate scenario to 1,047 mm/year under the future climate scenario (see Section 3.4.2.2), which is likely why low flow is higher under the climate change scenario. These results are contrary to Fossey et al. (2016b) results which showed that peak flows in Quebec are predicted to increase under climate change conditions. This discrepancy is likely due to the different sources of climate data.

Table 4-4 Comparison of average, median, minimum, and maximum outlet daily streamflow under historical climate and climate change scenarios with existing wetlands

Historical Climate Climate Change Indicator Scenario Scenario (1984 – 2014) (2015 – 2045) Average Flow (m3/sec) 1.81 2.01 Median Flow (m3/sec) 1.62 1.87 Min Flow (m3/sec) 0.37 0.54 Max Flow (m3/sec) 15.68 11.13 Flow Variance (m3/sec) 0.95 0.78

Outlet Streamflow with Existing Wetlands under Historical Climate Scenario 20 -30

15 20

/sec) 3 10 70

Flow Flow (m 5 120

0 170

1984-01-01 1989-01-01 1994-01-01 1999-01-01 2004-01-01 2009-01-01 2014-01-01 Precipitation (mm) Precipitation (mm) Temperatureor (C) Precipitation Flow Temperature

Figure 4-3 Outlet daily streamflow with existing wetlands under historical climate scenario with average temperature and precipitation

Outlet Streamflow with Existing Wetlands under Climate Change Scenario 20 -30

15 20

/sec) 3

10 70 (C)

Flow Flow (m 5 120

0 170

2015-01-01 2020-01-01 2025-01-01 2030-01-01 2035-01-01 2040-01-01 2045-01-01 Precipitation (mm) Temperatureor

Precipitation Flow Temperature

Figure 4-4 Outlet daily streamflow with existing wetlands under climate change scenario with average temperature and precipitation 70

4.2.2 Subwatershed Outlet Peak Flow Change The subwatershed outlet peak flow changes shown in Table 4-5 occur under the historical climate scenario (1984-2014). Briefly, the maximum flow at the outlet for each year of simulation under the existing wetlands, wetland restoration, and wetland loss scenarios were determined by creating Pivot tables in Excel. It was then confirmed that the maximum flow at the outlet for each year of simulation under the three wetland scenarios occurred on the same date, to ensure that the peak flow change comparisons are based on the same storm event. The difference between the maximum flow at the outlet for each year of simulation under the wetland restoration scenario and the existing wetlands scenario were calculated, and these differences for each year of simulation were then averaged to determine the average annual peak flow decrease shown in Table 4-5. This was also repeated for the wetland loss scenario by differencing the maximum flow at the outlet for each year of simulation under the wetland loss scenario and the existing wetlands scenario, and then averaging these differences to determine the average annual peak flow increase shown in Table 4-5. Figure 4-5 shows the changes to the average annual peak flows at the subwatershed outlet under the existing wetlands, wetland restoration, and wetland loss scenarios for the historical climate scenario (1984-2014). The wetland restoration scenario results in an average annual peak flow decrease of 0.101 m3/sec, or a 1.23 percent decrease compared to the average annual peak flows under the existing scenario (Table 4-5). Moreover, the 1.23 percent decrease in average annual peak flows results from wetland restoration occupying only 0.25 percent of the subwatershed land surface area. The wetland loss scenario results in an average annual peak flow increase of 0.134 m3/sec, or a 1.63 percent increase compared to the average annual peak flows under the existing scenario. Similarly, the 1.63 percent increase in average annual peak flows results from losing wetlands occupying only 0.38 percent of the subwatershed land surface area.

Table 4-5 Average annual peak flow changes under historical climate scenario for existing wetlands, wetland restoration, and removal of all wetlands

Average Average Annual Annual Peak Average Peak Flow Percent of Flow Change Annual Percent Change Subwatershed with respect to Historical Climate Scenario Peak with respect to Land Area Existing Flow Existing Occupied by Wetlands (m3/sec) Wetlands Wetlands Scenario Scenario (m3/sec) Existing wetlands 8.47 N/A N/A 0.38% Restored and existing wetlands 8.37 -0.101 -1.23% 0.63% Removal of existing wetlands 8.61 0.134 1.63% 0.00%

at the subwatershed outlet the at

historical climate scenario with wetlands, withwetlands, and no climate existing historical

the

with wetlandwithrestoration

nnualflows under peak

Figure Figure

The subwatershed outlet peak flow changes shown in Table 4-6 occur under the climate change scenario (2015-2045). The average annual peak flow changes were calculated the same as under the historical climate scenario (as described above). Figure 4-6 shows the changes in the average annual peak flows at the subwatershed outlet under the existing wetlands, wetland restoration, and wetland loss scenarios for the years 2015-2045. The wetland restoration scenario results in an average annual peak flow decrease of 0.080 m3/sec, or a 1.12 percent decrease compared to the average annual peak flows under the existing scenario (Table 4-6). The 1.12 percent decrease in average annual peak flows results from wetland restoration occupying only 0.25 percent of the subwatershed land surface area. The wetland loss scenario results in an average annual peak flow increase of 0.076 m3/sec, or a 1.07 percent increase compared to the average annual peak flows under the existing scenario (Table 4-6). Similarly, the 1.07 percent increase in average annual peak flows results from losing wetlands occupying only 0.38 percent of the subwatershed land surface area. The climate change scenario results in smaller percent changes to peak flows under the wetland restoration and the wetland loss scenarios compared to the historical climate scenario. The smaller percent changes can likely be explained by the smaller peak flows that are observed under the climate change scenario (see Section 4.2.1). Thus, results in this study indicate that the wetland restoration and wetland loss scenarios will have similar peak flow changes at the subwatershed outlet under the historical climate and climate change scenarios. Table 4-6 Average annual peak flow reductions under climate change scenario for existing wetlands, wetland restoration, and removal of all wetlands

Average Average Annual Annual Peak Peak Flow Percent of Average Flow Change Percent Change Subwatershed Annual with respect to Climate Change Scenario with respect to Land Area Peak Flow Existing Existing Occupied by (m3/sec) Wetlands Wetlands Wetlands Scenario Scenario (m3/sec) Existing wetlands 7.13 N/A N/A 0.38% Restored and existing wetlands 7.05 -0.080 -1.12% 0.63% Removal of existing wetlands 7.21 0.076 1.07% 0.00%

at the subwatershed outlet the at subwatershed

wetlandrestoration

nnual peak flows under climate change scenario with no wetlands, with existing wetlands, and withwith change and with wetlands, wetlands, scenario nnualno climate existing flows under peak

Figure Figure

The peak flow reduction for each year of simulation at the subwatershed outlet under the wetland restoration scenario was used to create a probability distribution of peak flow reduction at the subwatershed outlet due to wetland restoration. The probability distribution was used for determining the 10 year and 30 year return periods for peak flow reduction due to wetland restoration. A 10 year return period represents the peak flow reduction that will occur on average once every ten years due to wetland restoration. Similarly, a 30 year return period represents the peak flow reduction that will occur on average once every 30 years due to wetland restoration. Peak flow decreases with a 10 year return period and a 30 year return period were calculated in addition to the average peak flow reduction to show a range of probable peak flow reduction values across the simulation period. Briefly, the peak flow decrease for each year of simulation were sorted and ranked in descending order. Second, the probability of flow being larger was calculated as the rank divided by the total number of years of simulation plus one (see section 3.6.1). Third, the return period was calculated as one divided by the probability. Lastly, the peak flow decreases and their associated return periods for each year of simulation were graphed on a scatter plot in Excel, and a logarithmic best fit line and equation for the best fit line were added (Figure 4-7 and Figure 4-8). These best fit line equations were used to calculate the 10 year and 30 year return periods. The same procedure was used for determining the 10 year and 30 year return periods for flow reduction at the outlet due to wetland restoration under the climate change scenario (i.e. 2015- 2045). Figure 4-7 and Figure 4-8 show the peak flow decrease probability distributions under the historical climate scenario and the climate change scenario, respectively. The 10 year return period for the peak flow decreases under the historical climate scenario is 0.176 m3/s and the 30 year return period for the peak flow decreases is 0.236 m3/s. Furthermore, the 10 year return period for the peak flow decreases under climate change scenario is 0.138 m3/s and the 30 year return period for the peak flow decreases is 0.184 m3/s. As expected, the 10 and 30 year return periods under the climate change scenario are less than the 10 and 30 year return periods under the historical climate scenario, because the climate change scenario has smaller peak flows than the historical climate scenario (see Section 4.2.1).

Peak flow decrease return period for historical climate scenario 0.3

/sec) 0.25 3 0.2 y = 0.0549ln(x) + 0.0493 0.15 R² = 0.8715 0.1 0.05 0

Peak Flow Peak Flow Decrease (m 0 5 10 15 20 25 30 35 Return Period (years)

Figure 4-7 Frequency distribution of peak flow reductions at the Lynn River subwatershed outlet due to wetland restoration for the 31 year simulation under the historical climate scenario

Peak flow decrease return period for climate change scenario

0.2 /sec)

3 0.16 y = 0.0424ln(x) + 0.04 0.12 R² = 0.8232

0.08

0.04

0 5 10 15 20 25 30 35 Peak Flow Peak Flow Decrease (m Return Period (years)

Figure 4-8 Frequency distribution of peak flow reductions at the Lynn River subwatershed outlet due to wetland restoration for the 31 year simulation under the climate change scenario

The changes in the average annual peak flows at the subwatershed outlet under the six scenarios indicate that the existing wetlands and the identified wetland restoration locations do not largely impact streamflow at the subwatershed outlet. First, the number of existing wetlands (i.e. 39) and the number of wetland restoration locations (i.e. 59) limits the changes in peak flows at the subwatershed outlet. Moreover, these wetlands all have relatively small surface areas, volumes, and contributing areas, which also limits the changes to peak flows at the outlet. Second, the subwatershed outlet occurs downstream of the town of Simcoe, and downstream of a

wastewater treatment plant that discharges to the main stream, thus peak flows at the subwatershed outlet are likely impacted by urban runoff (i.e. where no wetland restoration occurs) and by wastewater treatment plant discharges. Lastly, there are 3 small reservoirs on the Lynn River, upstream of the subwatershed outlet, and another one downstream of the subwatershed outlet. These reservoirs were not modelled in the IMWEBs-Wetland model because there was no available reservoir discharge data for model input. However, reservoirs upstream of the subwatershed outlet have a large impact on the provision of flow at the outlet (Liu et al. 2014) and thus likely contributes to the small impact that wetland restoration and wetland loss has on the subwatershed outlet peak streamflow. Hansen et al. (2015) explain that wetlands on agricultural land are not likely to provide notable flood protection benefits unless they are located in close proximity to urban areas. The results in this study are similar to Martinez-Martinez et al. (2014) who found that wetland restoration resulted in negligible reductions in daily peak flow rates and frequency of peak flow events at the watershed outlet. Moreover, Martinez-Martinez et al. (2014) found that long-term daily average streamflow reduction at the watershed outlet ranges from 0.16 to 0.35 percent, and that as wetland area is increased, the streamflow reduction at the watershed outlet is also increased. Similarly, Shultz et al. (2003) studied subwatersheds in the Mississippi River Basin and found that restoring upland wetlands would reduce flood peaks by 1 percent to 23 percent with deep wetlands, and by 5 percent to 9 percent with shallow wetlands. Furthermore, Pattison et al. (2011) found that annual discharge would decrease by 3.4 percent if all historical wetlands were restored in a southern Ontario subwatershed (i.e. 3,647 ha of wetlands restored). Lastly, Yang et al. (2010) found that if wetlands in a Manitoba watershed were restored to 1968 levels, the peak discharge would be reduced by 23.4 percent. 4.2.3 Subwatershed Outlet Low Flow Change The average annual low flow changes were determined by first calculating a 7 day moving average for July-September for each year of simulation for the three wetland scenarios. July to September were assumed to be low flow periods based on a visual inspection of the subwatershed outlet hydrograph. A 7 day moving average of the subwatershed outlet flow was determined for each day of simulation in the July to September low flow periods under the three wetland scenarios. A Pivot table in Excel was created to determine the minimum 7 day moving average for each year of simulation under the three wetland scenarios. It was then confirmed that

the minimum 7 day moving average under the wetland restoration scenario and the wetland loss scenario for each year of simulation occurred on the same date as the minimum 7 day moving average under the existing wetlands scenario. A 7 day moving average was used because 7Q10 flow was going to be used to evaluate the low flow impacts of the various wetland and climate scenarios at the subwatershed outlet. 7Q10 is the lowest 7-day average flow that occurs (on average) once every 10 years (EPA, 2018). 7Q10 is a design flow statistic that is used by many regulatory agencies to define low flow for setting discharge limits (EPA, 2018). Under the historical climate change scenario, only 7 of the 31 (23 percent) years simulated had larger minimum 7 day moving averages under the wetland restoration scenario, and the average increase to low flows at the subwatershed outlet was 0.0012 m3/s, or an average 0.12 percent increase to low flows. Similarly, under the climate change scenario, only 11 of the 31 (35 percent) years simulated had low flow increases at the subwatershed outlet due to wetland restoration, and the average increase to low flows at the subwatershed outlet was 0.0004 m3/s, or an average 0.05 percent increase to low flows. Surprisingly, the minimum 7 day moving averages with the wetland loss scenario were slightly larger than the minimum 7 day moving averages with existing wetlands, suggesting that losing existing wetlands would increase low flows. The average low flow increase due to wetland loss under the historical climate scenario is 0.49 percent. Moreover, the average low flow increase due to wetland loss under the climate change scenario is 0.32 percent. Therefore, a small amount of wetland restoration may not have pronounced effect on increasing low flows at the subwatershed outlet because of the same issues described in Section 4.2.2 (i.e. small number of wetlands, small wetland surface areas, small wetland volumes, small wetland contributing areas, location of subwatershed outlet, and the reservoirs upstream of the outlet). Moreover, IMWEBs-Wetland calculates baseflow at the subbasin scale using a linear/non-linear reservoir method which is applicable for large subbasin (Liu et al. 2016). Liu et al. (2016) explains that when delineated subbasins are small, groundwater flow may not contribute to the subbasin outlet but will be somewhere in downstream reaches. IMWEBs- Wetland sets individual isolated wetlands as their subbasin outlet, and their drainage areas could be very small. As a result, groundwater may bypass the outlet from underground and would not join total outflow (Liu et al. 2016). Thus, the simulated impacts of the wetland restoration and wetland loss scenarios on low flows may be inaccurate due to the limitation of wetland

parameterization in the IMWEBs-Wetland model. However, the local low flow impacts of wetland restoration will be analyzed in Section 4.2.5, to determine if small tributaries near wetland restoration locations may have more low flow increases than was found at the subwatershed outlet. 4.2.4 Individual Wetland Flow Retention The average annual flow retention values shown in Table 4-7 and Table 4-8 for each wetland restoration location and each existing wetland occur under the historical climate scenario (1984-2014). Briefly, the maximum flow into each wetland for each year of simulation was determined with a Pivot table in Excel. VLOOKUP and IF AND statements were used to identify the flow out of each wetland that is associated with the maximum flow into that wetland (assuming a lag time between the maximum flow in and the associated flow out ranges from 0-5 days). The difference between the maximum flow into each wetland for each year and the associated flow out were calculated, and the annual differences for each wetland were averaged to determine the average flow retention of each wetland (see section 3.6.3). The average flow retention of each wetland (Table 4-7) was used to distribute the average subwatershed outlet peak flow decrease back to individual wetland restoration locations. The peak flow decrease that is observed at the subwatershed outlet is caused by individual wetlands ability to retain local flow. To distribute the peak flow decrease at the subwatershed outlet back to individual wetlands the individual wetlands average flow retention values (i.e. third column in Table 4-7) were summed, and the percent that each individual wetland contributes to the summed flow retention were calculated (i.e. fifth column in Table 4-7). These percentages were used to distribute the average peak flow decrease at the subwatershed outlet back to individual wetlands (i.e. sixth column in Table 4-7). The same procedure was used to distribute the peak flow increase at the subwatershed outlet due to losing existing wetlands back to individual existing wetlands (Table 4-8). Figure 4-9 and Figure 4-10 show the average annual flow retention for the existing wetland and wetland restoration scenarios with the historical climate scenario, respectively. Wetland 111 provides the largest flow retention of 2,073.88 m3/day, or 58 percent of the flow into the wetland, of the wetland restoration locations. Wetland 10 provides the largest flow retention of 5,160.31 m3/day, or 82 percent of the flow into the wetland, of the existing wetlands.

The existing wetlands retain local flow by an average of 65 percent, whereas the wetland restoration locations retain local flow by an average of 85 percent. Table 4-7 shows the top 10 best wetland restoration locations for decreasing peak flows at the subwatershed outlet in a bolded font. The subwatershed outlet would have an average annual peak flow decrease of 4,650.96 m3/day if the top 10 best wetland restoration locations for flow retention were restored under the historical climate scenario. Comparatively, restoring all 59 wetlands in the subwatershed would result in an average annual peak flow decrease of 8,746.91 m3/day at the subwatershed outlet. Thus, by targeting the top 10 best wetland restoration locations, 53.17 percent of the subwatershed outlet peak flow decrease that is achieved by all 59 wetland restoration locations can be achieved with only 10. Similarly, Table 4-8 shows the top 10 worst existing wetlands to lose due to the increase in peak flows at the subwatershed outlet that would result if they were lost. The subwatershed outlet would have an average annual peak flow increase of 8,018.97 m3/day if the top 10 worst existing wetlands to lose were lost. Comparatively, if all 39 existing wetlands were lost there would be an average annual peak flow increase of 11,577.60 m3/day at the subwatershed outlet. Thus, these top 10 worst existing wetlands to lose should be a priority for wetland conservation because if these 10 wetlands were lost, 69.26 percent of the subwatershed outlet peak flow increase that would be caused by losing all 39 existing wetlands would be caused by losing only 10.

Average Average Distribution of Average Peak Maximum Maximum Percent of Wetland Flow Decrease at Status Flow Flow Retention ID Subwatershed Outlet to Retention Retention Total Individual Wetlands (m3/day) (m3/day) (%) 111 Restored 2073.88 58% 14.18% 1239.90 80 Restored 1010.32 82% 6.91% 604.04 35 Restored 821.61 49% 5.62% 491.21 52 Restored 658.31 81% 4.50% 393.58 39 Restored 583.62 13% 3.99% 348.93 97 Restored 555.47 91% 3.80% 332.10 126 Restored 554.31 99% 3.79% 331.40 123 Restored 527.60 96% 3.61% 315.43 9 Restored 519.51 46% 3.55% 310.60 47 Restored 474.64 74% 3.24% 283.77 107 Restored 451.23 100% 3.08% 269.78 136 Restored 379.89 90% 2.60% 227.12 104 Restored 361.83 86% 2.47% 216.33 124 Restored 287.74 94% 1.97% 172.03 74 Restored 284.31 36% 1.94% 169.98 20 Restored 252.99 98% 1.73% 151.25 118 Restored 251.58 96% 1.72% 150.41 28 Restored 246.10 100% 1.68% 147.14 109 Restored 227.00 98% 1.55% 135.71 122 Restored 219.14 99% 1.50% 131.02 101 Restored 214.65 59% 1.47% 128.33 51 Restored 192.51 100% 1.32% 115.10 64 Restored 183.22 37% 1.25% 109.54 41 Restored 183.11 100% 1.25% 109.48 112 Restored 175.68 93% 1.20% 105.03 14 Restored 168.61 100% 1.15% 100.81 103 Restored 166.19 100% 1.14% 99.36 33 Restored 161.28 99% 1.10% 96.42 115 Restored 152.48 96% 1.04% 91.17 45 Restored 143.92 86% 0.98% 86.04 94 Restored 131.49 36% 0.90% 78.61 65 Restored 128.33 92% 0.88% 76.72 15 Restored 125.16 99% 0.86% 74.83 43 Restored 121.20 100% 0.83% 72.46 50 Restored 98.50 98% 0.67% 58.89 85 Restored 95.48 100% 0.65% 57.09 78 Restored 90.72 17% 0.62% 54.24 139 Restored 86.65 95% 0.59% 51.81 73 Restored 85.14 100% 0.58% 50.90 30 Restored 85.00 100% 0.58% 50.82

Table 4-7 Historical climate scenario distribution of average peak flow decrease at the subwatershed outlet back to individual wetlands based on individual wetlands average flow retention values for wetland restoration locations, Continued Average Average Distribution of Average Peak Maximum Maximum Percent of Wetland Flow Decrease at Subwatershed Status Flow Flow Retention ID Outlet to Individual Wetlands Retention Retention Total (m3/day) (m3/day) (%) 16 Restored 82.63 98% 0.56% 49.40 21 Restored 82.59 100% 0.56% 49.38 106 Restored 79.37 99% 0.54% 47.45 22 Restored 77.50 100% 0.53% 46.33 18 Restored 74.75 98% 0.51% 44.69 116 Restored 74.55 100% 0.51% 44.57 26 Restored 72.39 100% 0.49% 43.28 24 Restored 69.82 100% 0.48% 41.74 127 Restored 68.12 99% 0.47% 40.72 34 Restored 57.97 1% 0.40% 34.66 98 Restored 56.91 99% 0.39% 34.03 58 Restored 54.60 100% 0.37% 32.64 11 Restored 48.17 99% 0.33% 28.80 67 Restored 38.23 100% 0.26% 22.86 86 Restored 36.80 100% 0.25% 22.00 128 Restored 35.70 99% 0.24% 21.34 42 Restored 34.56 100% 0.24% 20.66 90 Restored 31.91 100% 0.22% 19.08 137 Restored 21.54 100% 0.15% 12.88 Sum 14,628.51 8,745.91

Average Average Distribution of Average Peak Maximum Maximum Percent of Wetland Flow Increase at Subwatershed Status Flow Flow Reduction ID Outlet to Individual Wetlands Retention Retention Total (m3/day) (m3/day) (%) 10 Existing 5160.31 82% 23.97% 2774.75 31 Existing 3194.57 63% 14.84% 1717.75 19 Existing 1320.25 60% 6.13% 709.91 110 Existing 954.02 66% 4.43% 512.99 69 Existing 787.29 87% 3.66% 423.33 125 Existing 785.40 72% 3.65% 422.32 77 Existing 763.11 91% 3.54% 410.33 4 Existing 754.11 88% 3.50% 405.49 3 Existing 705.69 85% 3.28% 379.46 114 Existing 488.43 99% 2.27% 262.63 27 Existing 465.17 90% 2.16% 250.12 5 Existing 432.28 84% 2.01% 232.44 7 Existing 422.84 71% 1.96% 227.36 8 Existing 412.02 44% 1.91% 221.55 54 Existing 410.13 20% 1.90% 220.53 55 Existing 377.02 87% 1.75% 202.73 1 Existing 368.72 90% 1.71% 198.27 49 Existing 335.18 48% 1.56% 180.23 130 Existing 323.46 98% 1.50% 173.93 134 Existing 301.32 97% 1.40% 162.02 13 Existing 300.04 86% 1.39% 161.34 133 Existing 265.55 87% 1.23% 142.79 135 Existing 257.53 91% 1.20% 138.48 36 Existing 245.85 89% 1.14% 132.19 138 Existing 209.79 30% 0.97% 112.81 71 Existing 201.60 85% 0.94% 108.40 12 Existing 197.64 92% 0.92% 106.27 92 Existing 183.41 81% 0.85% 98.62 132 Existing 143.32 92% 0.67% 77.07 6 Existing 126.96 90% 0.59% 68.27 23 Existing 123.68 99% 0.57% 66.50 57 Existing 120.15 89% 0.56% 64.61 17 Existing 114.82 93% 0.53% 61.74 2 Existing 87.36 5% 0.41% 46.97 131 Existing 84.83 5% 0.39% 45.61 44 Existing 39.02 1% 0.18% 20.98 81 Existing 37.07 <1% 0.17% 19.93 53 Existing 21.18 <1% 0.10% 11.39 87 Existing 10.18 <1% 0.05% 5.48 Sum 21,531.30 11,577.60

flow retention with historical climate scenario climate with flowhistorical retention

flow retention with historical climate scenari climate historical with flow retention

annual

Existing wetlands average Existingaverage wetlands

Wetland restoration average average Wetlandrestoration

Figure Figure Figure Figure

Figure 4-11 presents the average annual flow retention (expressed as m3/day) of the existing wetlands and wetland restoration locations under the historical climate scenario. The existing wetlands typically provide greater flow retention than the wetland restoration locations. Moreover, the north-west section of the subwatershed contains several wetland restoration locations that have small flow retention values. These wetlands all have small contributing areas (ranging from 0.36 ha to 1.71 ha) which contributes to their small flow retention values. However, the wetland restoration locations in the north-west section of the subwatershed all retain more than 95 percent of the flow that goes into them. Thus, it is important to consider both the flow retention value (m3/day) as well as the percent flow retention when evaluating the flow retention outcomes of the existing wetlands and wetland restoration scenarios. Furthermore, the wetland restoration locations with the largest flow retention values have large contributing areas (ranging from 15.30 ha to 61.56 ha) and above average wetland area and wetland volumes. This trend is also found with the existing wetlands, as existing wetlands with large contributing areas (ranging from 29.07 ha to 102.33 ha) and above average wetland areas and wetland volumes have the largest flow retention values. Lastly, the existing wetlands and the wetland restoration locations that have small flow retention values tend to be in closer proximity to streams, and thus have more stream connectivity. Whereas, the existing wetlands and the wetland restoration locations with large flow retention values tend to have less stream connectivity.

Figure 4-11 The Lynn River subwatershed existing wetlands and wetland restoration locations flow retention under historical climate scenario. Data are classified with Natural Breaks (Jenks) classification. The average annual flow retention values shown in Table 4-9 and Table 4-10 for each wetland restoration location and each existing wetland occur under the climate change scenario (2015-2045). The average annual flow retention of each wetland was calculated the same as under the historical climate scenario (as described above). The average flow retention of each wetland restoration location (Table 4-9) was used to distribute the subwatershed outlet peak flow decrease back to individual wetland restoration location. The peak flow decrease that is observed at the subwatershed outlet is caused by individual wetlands ability to retain local flow. To distribute the peak flow decrease at the subwatershed outlet back to individual wetlands the individual wetlands average flow retention values (i.e. third column in Table 4-9) were summed, and the percent that each individual wetland contributes to the summed flow retention were calculated (i.e. fifth column in Table 4-9). These percentages were used to distribute the average peak flow decrease at the subwatershed outlet back to individual wetlands (i.e. sixth column in

Table 4-9). The same procedure was used to distribute the peak flow increase at the subwatershed outlet due to losing existing wetlands back to individual existing wetlands (Table 4-10). Figure 4-12 and Figure 4-13 show the average annual flow retention for the wetland restoration locations and the existing wetlands under the climate change scenario, respectively. Wetland 111 provides the largest flow retention of 1,735.07 m3/day, or 68 percent of the flow into the wetland, of the wetland restoration locations. Wetland 10 provides the largest flow retention of 3,963.25 m3/day, or 80 percent of the flow into the wetland, of the existing wetlands. The existing wetlands retain local flow by an average 63 percent, whereas the wetland restoration locations retain local flow by an average of 86 percent. Table 4-9 shows the top 10 best wetland restoration locations for decreasing peak flows at the subwatershed outlet in a bolded font. The subwatershed outlet would have an average annual peak flow decrease of 4,032.14 m3/day if the top 10 best wetland restoration locations for flow retention were restored under the climate change scenario. Comparatively, restoring all 59 wetlands in the subwatershed would result in an average annual peak flow decrease of 6,929.00 m3/day at the subwatershed outlet. Thus, by spatially targeting the top 10 best wetland restoration locations, 58.19 percent of the subwatershed outlet peak flow decrease that is achieved by all 59 wetland restoration locations can be achieved with only 10. Similarly, Table 4-10 shows the top 10 worst existing wetlands to lose due to the increase in peak flows at the subwatershed outlet that would result if they were lost. The subwatershed outlet would have an average annual peak flow increase of 4,637.85 m3/day if the top 10 worst existing wetlands to lose were lost under the climate change scenario. Comparatively, if all 39 existing wetlands were lost there would be an average annual peak flow increase of 6,566.40 m3/day at the subwatershed outlet. Thus, these top 10 worst existing wetlands to lose should be a priority for wetland conservation efforts because if these 10 wetlands were lost, 70.63 percent of the subwatershed outlet peak flow increase that would be caused by losing all 39 existing wetlands can be caused by only 10.

Average Average Distribution of Average Peak Maximum Maximum Percent of Wetland Flow Decrease at Status Flow Flow Reduction ID Subwatershed Outlet to Retention Retention Total Individual Wetlands (m3/day) (m3/day) (%) 111 Restored 1735.07 68% 16.87% 1169.15 80 Restored 811.05 83% 7.89% 546.51 35 Restored 691.74 55% 6.73% 466.12 52 Restored 443.78 90% 4.32% 299.04 9 Restored 432.00 46% 4.20% 291.10 126 Restored 421.20 100% 4.10% 283.82 97 Restored 406.36 89% 3.95% 273.82 123 Restored 371.25 95% 3.61% 250.16 47 Restored 347.83 89% 3.38% 234.38 39 Restored 323.58 11% 3.15% 218.04 104 Restored 286.20 86% 2.78% 192.85 136 Restored 258.92 91% 2.52% 174.47 107 Restored 240.53 100% 2.34% 162.08 74 Restored 220.46 35% 2.14% 148.55 124 Restored 189.56 95% 1.84% 127.73 118 Restored 175.60 96% 1.71% 118.32 109 Restored 174.96 99% 1.70% 117.89 101 Restored 171.69 58% 1.67% 115.69 122 Restored 164.65 99% 1.60% 110.95 64 Restored 161.09 45% 1.57% 108.55 112 Restored 134.84 92% 1.31% 90.86 33 Restored 117.45 99% 1.14% 79.14 20 Restored 115.62 99% 1.12% 77.91 51 Restored 114.21 100% 1.11% 76.96 94 Restored 113.63 42% 1.11% 76.57 103 Restored 103.44 100% 1.01% 69.70 115 Restored 103.25 99% 1.00% 69.57 45 Restored 90.18 93% 0.88% 60.77 28 Restored 77.94 100% 0.76% 52.52 65 Restored 75.67 97% 0.74% 50.99 73 Restored 69.12 100% 0.67% 46.58 85 Restored 68.86 100% 0.67% 46.40 78 Restored 67.85 16% 0.66% 45.72 139 Restored 65.25 97% 0.63% 43.97 14 Restored 63.98 100% 0.62% 43.11 18 Restored 60.73 100% 0.59% 40.92 106 Restored 59.04 100% 0.57% 39.78 50 Restored 58.85 100% 0.57% 39.65 34 Restored 58.32 1% 0.57% 39.30 15 Restored 51.84 100% 0.50% 34.93

Table 4-9 Climate change scenario distribution of average peak flow decrease at subwatershed outlet back to individual wetlands based on individual wetlands average flow retention values with wetland restoration locations, Continued Average Average Distribution of Average Peak Maximum Maximum Percent of Wetland Flow Decrease at Status Flow Flow Reduction ID Subwatershed Outlet to Retention Retention Total Individual Wetlands (m3/day) (m3/day) (%) 30 Restored 50.64 100% 0.49% 34.12 116 Restored 50.44 100% 0.49% 33.99 41 Restored 49.82 100% 0.48% 33.57 16 Restored 43.00 99% 0.42% 28.98 127 Restored 41.10 100% 0.40% 27.69 11 Restored 41.04 100% 0.40% 27.65 98 Restored 40.73 100% 0.40% 27.45 43 Restored 35.46 100% 0.34% 23.89 58 Restored 29.76 100% 0.29% 20.05 128 Restored 26.33 100% 0.26% 17.74 90 Restored 24.65 100% 0.24% 16.61 86 Restored 24.56 100% 0.24% 16.55 21 Restored 21.07 100% 0.20% 14.20 22 Restored 20.92 100% 0.20% 14.10 26 Restored 20.21 100% 0.20% 13.62 42 Restored 17.28 100% 0.17% 11.64 24 Restored 16.77 100% 0.16% 11.30 137 Restored 16.05 100% 0.16% 10.81 67 Restored 15.52 100% 0.15% 10.46 Sum 10,282.91 6,929.00

Table 4-10 Climate Change scenario distribution of average peak flow increase at subwatershed outlet back to individual wetlands based on wetlands average maximum flow retention values with existing wetlands

Average Average Distribution of Average Peak Maximum Maximum Percent of Wetland Flow Increase at Status Flow Flow Reduction ID Subwatershed Outlet to Retention Retention Total Individual Wetlands (m3/day) (m3/day) (%) 10 Existing 3963.25 80% 24.99% 1641.24 31 Existing 2297.13 56% 14.49% 951.27 19 Existing 971.02 56% 6.12% 402.12 110 Existing 853.69 73% 5.38% 353.53 125 Existing 640.75 76% 4.04% 265.35 69 Existing 599.23 86% 3.78% 248.15 77 Existing 551.85 94% 3.48% 228.53 4 Existing 504.36 85% 3.18% 208.86 3 Existing 448.72 80% 2.83% 185.82 27 Existing 369.43 88% 2.33% 152.98 7 Existing 359.37 75% 2.27% 148.82 114 Existing 343.17 98% 2.16% 142.11 8 Existing 329.67 46% 2.08% 136.52 5 Existing 264.70 77% 1.67% 109.62 49 Existing 263.25 48% 1.66% 109.02 55 Existing 247.76 84% 1.56% 102.60 130 Existing 245.48 99% 1.55% 101.66 1 Existing 244.54 89% 1.54% 101.27 54 Existing 231.33 13% 1.46% 95.80 13 Existing 204.48 81% 1.29% 84.68 134 Existing 201.34 98% 1.27% 83.38 135 Existing 187.72 93% 1.18% 77.74 138 Existing 183.11 32% 1.15% 75.83 36 Existing 174.58 87% 1.10% 72.30 133 Existing 167.40 84% 1.06% 69.32 92 Existing 141.54 79% 0.89% 58.62 12 Existing 120.71 94% 0.76% 49.99 71 Existing 115.04 80% 0.73% 47.64 132 Existing 109.89 90% 0.69% 45.51 6 Existing 96.18 87% 0.61% 39.83 17 Existing 76.36 95% 0.48% 31.62 57 Existing 76.21 85% 0.48% 31.56 23 Existing 61.32 100% 0.39% 25.39 2 Existing 57.16 4% 0.36% 23.67 131 Existing 42.34 3% 0.27% 17.53 44 Existing 41.53 1% 0.26% 17.20 81 Existing 37.35 <1% 0.24% 15.47 53 Existing 22.41 <1% 0.14% 9.28 87 Existing 11.11 <1 % 0.07% 4.60 Sum 15,856.46 6,566.40

flow retention with climate change scenario change with flowclimate retention

annual

annual flow retention with climate change scenario change with annualflowclimate retention

Existingaverage wetlands

Wetland restoration average average Wetlandrestoration

Figure Figure Figure Figure

Figure 4-14 presents the average annual flow retention (expressed as m3/day) of the existing wetlands and the wetland restoration locations under the climate change scenario. The existing wetlands and wetland restoration locations provide smaller flow retention under the climate change scenario than under the historical climate scenario. Moreover, similar spatial patterns can be found under the historical climate scenario and the climate change scenario. For instance, the north-west section of the subwatershed contains several wetland restoration locations that have small flow retention values. Moreover, several of the existing wetlands in the north-west section of the subwatershed have large flow retention values under both the historical climate and climate change scenarios. Similar to the historical climate scenario, wetlands with large contributing areas, surface areas, and volumes results in the largest flow retention values.

Figure 4-14 The Lynn River subwatershed existing wetlands and wetland restoration locations flow retention under climate change scenario. Data are classified with Natural Breaks (Jenks) classification.

4.2.5 Individual Wetland Low Flow Increase The average low flow increases for the existing wetland and wetland restoration scenarios shown in Table 4-11 occur under the historical climate scenario. Briefly, with only the July to September data for each year of simulation, the days that flow out of each wetland is larger than flow into each wetland were flagged (i.e. days with low flow increases due to the presence of the wetland). The difference between wetland flow out and wetland flow in on the days that have wetland flow out larger than wetland flow in was calculated, and these differences were averaged for each wetland location, to determine the average low flow increase of each wetland (Table 4-11). Figure 4-15 shows the average low flow increases for the existing wetland and wetland restoration scenarios from July-September with the historical climate scenario. Only 9 of the 59 (15 percent) identified wetlands for restoration increase flows during low flow periods. Whereas, 17 of the 39 (44 percent) existing wetlands in the subwatershed provide increases to low flows during low flow periods. Of the existing wetlands, wetland 10 provides the largest flow increase of 111.43 m3/day, or an average 76 percent increase in flow, whereas wetland 111 provides the largest flow increase of 41.90 m3/day, or an average 58 percent increase in flow, of the wetland restoration scenario (Table 4-11). The existing wetlands and wetland restoration locations that provide that largest flow increases have large contributing areas, large wetland surface areas, and large wetland volumes. These results show that a larger percentage of the existing wetlands (i.e. 44 percent) provide low flow increases and that the existing wetlands low flow increases are larger than the wetland restoration low flow increases (Table 4-11). Moreover, the wetland restoration scenario analysis indicates that a small amount of wetland restoration may not have pronounced effects on increasing low flows during low flow periods in this subwatershed, as only 15 percent of the identified wetland restoration locations provide low flow increases. These results are supported by Hansen et al. (2015) which explains that while many wetlands provide groundwater recharge, the majority of wetlands do not. However, with improved wetland parametrization (i.e. hydraulic conductivity, maximum volume/area vs. normal volume/area), wetland restoration locations may show more low flow increases than was calculated in this research.

Wetland Restoration and Existing Wetlands Average Flow Increase During Low Flow Periods from July-Sept under Historical Climate Scenario (m3/day) 120

100

/day) 3 80 60 40

20 Flow Flow Increase (m 0 111 39 35 80 9 104 64 112 101 10 19 54 110 8 125 27 49 2 7 138 69 131 5 6 13 92 Wetland ID

Table 4-11 Average low flow increases during low flow periods (July-September) with historical climate scenario

Average low flow Average low flow Wetland ID increase (m3/day) increase (percent) Status 111 41.90 58% Restored 39 23.51 68% Restored 35 19.96 63% Restored 80 15.38 71% Restored 9 13.31 60% Restored 104 9.91 52% Restored 64 9.26 79% Restored 112 8.86 97% Restored 101 8.64 71% Restored Average 16.75 69% Restored 10 111.43 76% Existing 19 42.24 68% Existing 54 34.58 79% Existing 110 21.80 50% Existing 8 19.60 78% Existing 125 18.65 82% Existing 27 18.36 63% Existing 49 17.19 77% Existing 2 16.75 85% Existing 7 15.90 81% Existing 138 14.90 84% Existing 69 14.78 47% Existing 131 13.05 98% Existing 5 9.48 80% Existing 6 9.43 100% Existing 13 9.24 82% Existing 92 9.16 87% Existing Average 23.33 77% Existing

The average low flow increases for the existing wetland and wetland restoration scenarios shown in Table 4-12 occur under the climate change scenario. The average low flow increases for each wetland were calculated the same as under the historical climate scenario (as described above). Figure 4-16 shows the average low flow increases from July-September with the climate change scenario. Only 5 of the 59 (8 percent) identified wetlands for restoration increase flows during low flow periods. Whereas, 11 of the 39 (28 percent) existing wetlands in the subwatershed provide increases to low flows during low flow periods. Of the existing wetlands,

wetland 10 provides the highest average flow increase of 65.00 m3/day, or an average 72 percent increase in flow, whereas wetland 111 provides the highest average flow increase of 22.47 m3/day, or an average 47 percent increase in flow, of the wetland restoration scenario (Table 4-12). These results indicate that under the future climate change scenario fewer of the existing wetlands and the wetland restoration locations will provide low flow increases and that the low flow increases the wetlands provide will be smaller than under the historical climate scenario. These results are expected as low flows are already higher under the climate change scenario, thus increasing low flows under the climate change scenario is less critical than under the historical climate scenario. As expected, Wetland 10, of the existing wetlands, and wetland 111, of the wetland restoration locations, also provide the greatest flow retention.

Wetland Restoration and Existing Wetlands Average Flow Increase During Low Flow Periods from July-Sept under Climate Change Scenario (m3/day) 70

60 /day) 3 50 40 30 20

10 Flow Flow Increase (m 0 111 39 35 9 78 10 19 54 2 8 49 138 7 125 131 110 92 Wetland ID

Figure 4-16 Average flow increase during low flow periods from July-September under climate change scenario. Orange bars indicate wetland restoration locations, blue bars indicate existing wetlands

Table 4-12 Average flow increases during low flow periods (July-September) with climate change scenario

Average low flow Average low flow Wetland ID increase (m3/day) increase (percent) Status 111 22.47 47% Restored 39 21.49 65% Restored 35 13.40 61% Restored 9 10.17 63% Restored 78 8.64 100% Restored Average 15.24 67% Restored 10 65.00 72% Existing 19 27.44 61% Existing 54 26.42 76% Existing 2 15.67 81% Existing 8 14.27 82% Existing 49 13.37 82% Existing 138 12.74 82% Existing 7 12.25 83% Existing 125 12.13 84% Existing 131 11.71 98% Existing 110 11.68 40% Existing 92 8.64 93% Existing Average 19.28 78% Existing

4.3 Cost-Effectiveness Analysis The cost-effectiveness of peak flow decreases due to wetland restoration were determined at both the subwatershed outlet and at individual wetland restoration locations. The cost- effectiveness of peak flow decreases at the subwatershed outlet is important for conservation managers and decision makers to see the impact of 100 percent wetland restoration at the subwatershed outlet. Moreover, determining the cost-effectiveness at the subwatershed outlet indicates whether wetland restoration is a feasible option for decreasing peak flows in the subwatershed. Determining the cost-effectiveness of the peak flow decreases at the subwatershed outlet that each individual wetland restoration location contributes to is important for spatially targeting wetland restoration efforts to locations with high cost-effectiveness. The cost-effectiveness of peak flow decreases at the subwatershed outlet was calculated with the average peak flow decrease as well as the peak flow decrease with a 10 year return period and the peak flow decrease with a 30 year return period. The average, 10 year return

period, and 30 year return period measures for peak flow decreases were used to show a range of possible peak flow decreases due to wetland restoration at the subwatershed outlet. The cost- effectiveness of peak flow decreases at the subwatershed outlet was calculated with the annual cost of restoring all 59 wetlands in the subwatershed, expressed in dollars. Cost-effectiveness (expressed in $/m3/day) was calculated as the annual cost of restoring all 59 wetland restoration locations in the subwatershed divided by the average peak flow decrease, as well as the peak flow decrease with a 10 year return period and the peak flow decrease with a 30 year return period. The cost-effectiveness of peak flow decreases at individual wetland restoration locations uses the average peak flow decrease at the subwatershed outlet, and distributes this peak flow decrease back to individual wetlands based on individual wetlands local flow retention values. The individual wetlands average flow retention values (see section 4.2.4) were summed, and the percent that each individual wetland contributes to the summed flow retention were calculated. This percentage was used to distribute the average peak flow decrease at the subwatershed outlet back to individual wetlands. Cost-effectiveness was calculated for each wetland as its annual cost (expressed in dollars) divided by its contribution to the average annual peak flow decrease at the subwatershed outlet (expressed in m3/day) to determine its cost-effectiveness (expressed in $/m3/day). 4.3.1 Annual Cost of Wetland Restoration The annual cost of wetland restoration is comprised of three main cost categories, the opportunity cost of the forgone cropping returns, the restoration administration and engineering costs, and the inconvenience costs. All wetlands have a unique opportunity cost and unique inconvenience cost other than any wetlands that are within the same field. Similarly, all wetlands have a unique administration cost other than any wetlands that have the same surface area. Briefly, the administration costs were converted from dollars per wetland to dollars per hectare by dividing the administration costs (i.e. $1,050/wetland) by each wetlands’ area in hectares. Wetland restoration administration and engineering costs were calculated as the per hectare administration costs plus the per hectare engineering costs. The wetland restoration administration and engineering costs are initial fixed costs. Whereas the opportunity costs and the inconvenience costs are ongoing annual costs. Thus, the wetland restoration administration and engineering costs were amortized to calculate annual administration and engineering costs.

Amortization spreads the initial fixed cost over the expected lifespan of the wetland. Amortization was calculated with a 5 percent interest rate for 31 years. The annual cost saving achieved by targeting the top 10 best wetland restoration locations for flow retention was $77,049.35. The total annual cost of restoring all 59 wetland restoration locations was $104,956.59, whereas the annual cost of restoring the top 10 best wetland restoration locations for flow retention was $27,907.24. Thus, by targeting the top 10 best wetland restoration locations for flow retention there was a 73.41 percent decrease in annual cost. Figure 4-17 illustrates the annual cost of wetland restoration in the Lynn River subwatershed for each of the 59 identified wetland restoration locations. Annual costs have been expressed in $/ha/year in Figure 4-17 to standardize for wetland size. Thus, the variability between wetland restoration locations annual costs in Figure 4-17 is largely due to differences in opportunity costs and inconvenience costs.

Figure 4-17 The Lynn River subwatershed annual wetland restoration costs, expressed in $/ha/year

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4.3.2 Subwatershed Outlet Peak Flow Decrease Cost-Effectiveness Analysis The cost-effectiveness analysis presented in Table 4-13 uses the average annual peak flow decrease at the subwatershed outlet as well as the peak flow decrease at the outlet with a 10 year return period and the peak flow decrease at the outlet with a 30 year return period (expressed in m3/day). The average, 10 year return period, and 30 year return periods for peak flow decreases were used to show a range of possible peak flow decreases due to wetland restoration at the subwatershed outlet. The cost-effectiveness of peak flow decreases at the subwatershed outlet was calculated with the annual cost of restoring all 59 wetlands in the subwatershed, expressed in dollars. Cost-effectiveness (expressed as $/m3/day) was calculated as the annual cost of restoring all 59 wetland restoration locations in the subwatershed (expressed in dollars) divided by the average peak flow decrease at the subwatershed outlet (expressed in m3/day), as well as the peak flow decrease with a 10 year return period and the peak flow decrease with a 30 year return period at the subwatershed outlet. Table 4-13 illustrates that the annual cost-effectiveness varies depending on the peak flow decrease achieved that year. For instance, with an average peak flow decrease, the cost- effectiveness of wetland restoration is $12.00/m3/day under the historical climate scenario and $15.15/m3/day under the climate change scenario. Whereas, with a 10 year return period peak flow decrease, the cost-effectiveness of wetland restoration becomes $6.91/m3/day under the historical climate scenario and $8.83/m3/day under the climate change scenario. Cost- effectiveness is further increased with a 30 year return period peak flow decrease, resulting in $5.15/m3/day under the historical climate scenario and $6.59/m3/day under the climate change scenario. As expected, wetland restoration is less cost-effective (i.e. higher $/m3/day value) under the climate change scenario due to the smaller average peak flow decrease. It was assumed that the average opportunity costs and average inconvenience costs would not change between the two climate simulation periods.

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Table 4-13 Subwatershed outlet peak flow decrease cost-effectiveness analysis with the average annual peak flow decrease, peak flow decrease with a 10 year return period, and the peak flow decrease with a 30 year return period, with the annual cost of restoring all 59 wetlands in the subwatershed

Historical Climate (1984-2014) Reduction in Reduction in Reduction in Peak Flow with Peak Flow Parameter Average Peak a 10 Year with a 30 Year Flow Return Period Return Period

Reduction in Subwatershed 8,746 15,182 20,393 Peak Flow (m3/day)

Subwatershed Annual Cost $104,956.59 $104,956.59 $104,956.59 of Wetland Restoration ($)

Peak Flow Decrease Cost- $12.00 $6.91 $5.15 Effectiveness ($/m3/day)

Climate Change (2015-2045) Reduction in Reduction in Reduction in Peak Flow with Peak Flow Parameter Average Peak a 10 Year with a 30 Year Flow Return Period Return Period Reduction in Subwatershed 6,929 11,891 15,916 Peak Flow (m3/day)

Subwatershed Annual Cost $104,956.59 $104,956.59 $104,956.59 of Wetland Restoration ($)

Peak Flow Decrease Cost- $15.15 $8.83 $6.59 Effectiveness ($/m3/day)

4.3.3 Individual Wetland Peak Flow Decrease Cost-Effectiveness Analysis The cost-effectiveness of peak flow decreases at individual wetland restoration locations shown in Table 4-14 uses the average peak flow decrease at the subwatershed outlet, and distributes this peak flow decrease back to individual wetlands based on individual wetlands local flow retention values. The individual wetlands average flow retention values (see section 4.2.4) were summed, and the percent that each individual wetland contributes to the summed flow retention were calculated. This percentage was used to distribute the average peak flow decrease at the subwatershed outlet back to individual wetlands. Cost-effectiveness was

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calculated for each wetland as its annual cost (expressed in dollars) divided by its contribution to the average annual peak flow decrease at the subwatershed outlet (expressed in m3/day) to determine its cost-effectiveness (expressed in $/m3/day). The average cost-effectiveness of peak flow decreases was $20.45/m3/day and $36.28/m3/day under the historical climate and climate change scenarios, respectively (Table 4-14). Comparatively, the average cost-effectiveness of the top 10 best wetland restoration locations for flow retention under the historical climate scenario was $6.79/m3/day, which has been presented with a bolded font in Table 4-14. Similarly, under the climate change scenario, the average cost-effectiveness of the top 10 best wetland restoration locations for flow retention was $8.22/m3/day, which has been presented with a bolded font in Table 4-14. Thus, by targeting the top 10 best wetland restoration locations for flow retention the average cost-effectiveness is improved. Moreover, by targeting the top 10 best wetland restoration locations based on the cost- effectiveness presented in Table 4-14, the average cost-effectiveness was $5.20/m3/day and $5.97/m3/day under the historical climate and climate change scenarios, respectively. Therefore, similar improvements in cost-effectiveness can be achieved by targeting the top 10 best wetland restoration locations based only on flow retention or by targeting based on cost-effectiveness. Yang et al. (2016a) found that the cost-effectiveness of wetland restoration in terms of total phosphorus reductions in the South Tobacco Creek watershed in Manitoba ranges from $6.30/kg to $39.90/kg with an average of $21.70/kg. Moreover, Shultz et al. (2003) evaluated the economic feasibility of restoring previously drained wetlands to reduce flood damage in a subwatershed located in North Dakota. Shultz et al. (2003) found that none of the wetland restoration options they evaluated were economically feasible for reducing flood damage, even under the most optimistic scenario of low restoration cost on low cost land producing up to 2 feet of available storage over a 100 year time frame with a 3% discount rate. However, Shultz et al. (2003) found that with the inclusion of other non-flood related wetland values wetland restoration may be economically feasible.

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Peak Flow Decrease Cost- Peak Flow Decrease Cost- Wetland Wetland ID Effectiveness under Historical Effectiveness under Climate ID Climate Scenario ($/m3/day) Change Scenario ($/m3/day) 111 3.80 111 4.03 80 4.48 80 4.96 52 5.09 35 5.55 47 5.11 64 5.74 104 5.27 9 5.83 35 5.27 104 5.91 9 5.46 47 6.19 39 5.61 52 6.70 64 5.69 74 7.16 74 6.26 101 7.63 45 6.34 94 8.07 101 6.88 39 8.97 65 7.43 45 8.98 97 7.56 97 9.17 94 7.86 112 9.75 136 8.13 136 10.58 112 8.44 65 11.18 20 9.69 139 12.33 124 9.82 78 12.37 78 10.43 124 13.23 139 10.46 123 13.71 123 10.87 118 14.55 118 11.44 115 16.65 115 12.70 109 16.76 109 14.56 126 17.10 126 14.65 20 18.81 50 15.24 122 19.25 122 16.31 18 21.62 16 16.89 50 22.63 15 17.99 33 22.68 33 18.62 106 26.16 18 19.79 16 28.79 106 21.93 73 29.43 103 22.01 98 29.85 98 24.08 85 31.09 85 25.27 103 31.38

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Table 4-14 Individual wetland peak flow decrease cost-effectiveness analysis calculated with the average annual peak flow decrease at the subwatershed outlet distributed back to individual wetlands (expressed in m3/day) and annual cost of wetland restoration (expressed in $) under the historical climate and climate change scenarios, Continued Peak Flow Decrease Cost- Peak Flow Decrease Cost- Wetland Wetland ID Effectiveness under Historical Effectiveness under Climate ID Climate Scenario ($/m3/day) Change Scenario ($/m3/day) 127 26.40 11 31.51 128 26.76 128 32.19 73 26.93 90 36.39 14 28.93 34 37.08 51 29.43 15 38.53 116 29.98 127 38.82 11 30.25 116 39.32 107 31.61 51 44.01 90 31.68 107 52.61 28 32.98 86 53.91 41 33.19 137 56.30 24 33.79 30 57.32 26 35.60 14 67.65 43 36.22 58 68.94 22 36.24 42 71.68 67 37.90 67 82.82 30 38.49 28 92.40 21 39.98 41 108.24 42 40.39 43 109.85 86 40.56 26 113.17 34 42.04 22 119.13 58 42.35 24 124.85 137 47.27 21 139.06 Note data has been sorted with the most cost-effective wetlands presented first. Figure 4-18 and Figure 4-19 illustrate the spatial heterogeneity of wetland restoration cost-effectiveness in the Lynn River subwatershed. There are no obvious spatial patterns for the most cost-effective wetlands (i.e. low cost-effectiveness value), however, the north-west corner of the subwatershed contains several wetlands that are not highly cost-effective. The wetlands in the north-west corner of the subwatershed with poor cost-effectiveness have small flow retention values and slightly above average opportunity costs compared to other wetlands, thus these wetlands should not be targeted for wetland restoration. Furthermore, the wetland restoration locations that exhibit the highest cost-effectiveness have large contributing areas and either slightly above average or slightly below average opportunity costs and inconvenience costs. Thus, the variability in cost-effectiveness is largely caused by the variability of individual wetlands flow retention and less by the variability in wetland opportunity costs and

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inconvenience costs. Moreover, as expected, the cost-effectiveness of wetland restoration is reduced (i.e. higher cost-effectiveness value) under the climate change scenario compared to the historical climate scenario. The climate change scenario is less cost-effective than the historical climate scenario because the climate change scenario has smaller peak flow decreases.

Figure 4-18 Individual wetlands peak flow decrease cost-effectiveness analysis calculated with the average peak flow decrease at the subwatershed outlet distributed back to individual wetlands (expressed in m3/day) and annual cost of wetland restoration (expressed in $) under the historical climate scenario. Data is classified with Natural Breaks (Jenks) classification.

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Figure 4-19 Individual wetlands peak flow decrease cost-effectiveness analysis calculated with the average peak flow decrease at the subwatershed outlet distributed back to individual wetlands (expressed in m3/day) and annual cost of wetland restoration (expressed in $) under the climate change scenario. Data is classified with Natural Breaks (Jenks) classification. 4.4 Economic Sensitivity Analysis A sensitivity analysis was performed on the annual cost of wetland restoration, expressed in $/ha/year. The sensitivity analysis was done to determine which input parameters have the greatest impact on the annual cost of wetland restoration. Determining which input parameters have the greatest impact on the annual cost of wetland restoration illustrates the uncertainty of the wetland economic model results, as highly sensitive input parameters increase the uncertainty of the calculated annual cost. To analyze sensitivity, each input parameter was increased by 1 percent, and the annual cost (expressed in $/ha/year) was recalculated with the increased parameter. The sensitivity elasticity was then calculated with Equation 3-7, which represents the percentage increase in the annual cost that occurs due to a 1 percent increase in an input parameter.

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Table 4-15 presents the sensitivity analysis of the wetland restoration engineering costs, opportunity costs, administration costs, and inconvenience costs. As expected, the engineering costs exhibit the highest sensitivity of 0.7795, or for a one percent increase in engineering costs there was a 0.7795 percent increase in the calculated annual cost, because the engineering costs had the largest base (i.e. $37,065/ha). Moreover, inconvenience costs exhibit the lowest sensitivity of 0.0019 because inconvenience costs had the smallest base (i.e. on average $6.55/ha/year). The opportunity cost and the administration cost sensitivities were 0.1815 and 0.0380, respectively. Table 4-15 Sensitivity analysis of the four input costs for wetland restoration annual cost calculation

One Percent Newly Calculated Original Increase in the Annual Cost Sensitivity Input Parameter Value Input ($/ha/year) (i.e. with Elasticity ($/ha) Parameter a 1% increase in the ($/ha) input parameter) Engineering Cost 37,065.00 37,436.65 3,076.61 0.7795 Opportunity Cost 546.81 552.29 3,058.37 0.1815 Administration Cost 2,748.91 2,776.41 3,053.99 0.0380 Inconvenience Cost 6.55 6.62 3,052.90 0.0019 Note the opportunity cost, inconvenience cost, and administration cost listed in the above table are averages from all wetland restoration locations.

Table 4-16 presents the sensitivity analysis of the interest rate and the assumed wetland lifespan used for amortizing the wetland restoration administration and engineering costs. The interest rate and the assumed wetland lifespan are analysed for their sensitivity due to the uncertainty associated with these values. The sensitivity of the interest rate and the wetland lifespan are assessed based on the average peak flow decrease cost-effectiveness values shown in Table 4-13 (i.e. $12.00/m3/day for the historical climate scenario, and $15.15/m3/day for the climate change scenario). Decreasing the interest rate from 5 percent to 3 percent results in the cost-effectiveness of wetland restoration increasing (i.e. lower cost-effectiveness value) from $12.00/m3/day to $9.84/m3/day, resulting in a sensitivity elasticity of -0.3954 ([(9.84- 12.00)/(9.84+12.00)]/[(3.00-5.00)/(3.00+5.00)]) under the historical climate scenario. Increasing the interest rate from 5 percent to 8 percent results in the cost-effectiveness of wetland restoration decreasing (i.e. higher cost-effectiveness value) from $12.00/m3/day to $15.66/m3/day, resulting in a sensitivity elasticity of 0.5739 ([(15.66-

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12.00)/(15.66+12.00)]/[(8.00-5.00)/(8.00+5.00)]) under the historical climate scenario. Decreasing the assumed wetland lifespan from 31 years to 20 years reduces the cost- effectiveness of wetland restoration from $12.00/m3/day to $14.46/m3/day, resulting in a sensitivity elasticity of 0.4313 ([(14.46-12.00)/(14.46+12.00)]/[(20.00-31.00)/(20.00+31.00)]) under the historical climate scenario. Lastly, increasing the assumed wetland lifespan from 31 years to 50 years increases the cost-effectiveness of wetland restoration from $12.00/m3/day to $10.57/m3/day, resulting in a sensitivity elasticity of -0.2699 ([(10.57- 12.00)/(10.57+12.00)]/[(50.00-31.00)/(50.00+31.00)]). Thus, the choice of interest rate and wetland lifespan used for amortizing the wetland restoration administration and engineering costs largely impacts the calculated cost-effectiveness of wetland restoration. Table 4-16 Sensitivity analysis of the interest rate and wetland lifespan used for calculating the annual cost of wetland restoration

Historical Climate Scenario Newly Calculated Interest Rate and Lifespan Input Sensitivity Cost-Effectiveness Parameters Elasticity ($/m3/day) 3% Interest Rate, 31 Year Lifespan 9.84 -0.3954 8% Interest Rate, 31 Year Lifespan 15.66 0.5739 5% Interest Rate, 20 Year lifespan 14.46 0.4313 5% Interest Rate, 50 Year Lifespan 10.57 -0.2699 Climate Change Scenario Newly Calculated Interest Rate and Lifespan Input Sensitivity Cost-Effectiveness Parameters Elasticity ($/m3/day) 3% Interest Rate, 31 Year Lifespan 12.42 -0.3958 8% Interest Rate, 31 Year Lifespan 19.77 0.5734 5% Interest Rate, 20 Year lifespan 18.25 0.4309 5% Interest Rate, 50 Year Lifespan 13.34 -0.2703 Note: Sensitivity elasticity is calculated as the percent change in cost-effectiveness caused by a percentage change in the interest rate or wetland lifespan (Equation 3-7).

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Chapter 5 Conclusion This research employed the integrated modelling approach to evaluate the cost- effectiveness of wetland restoration scenarios in the Lynn River subwatershed in Norfolk County of southern Ontario. Norfolk County lost as estimated 82 percent of its wetland area since post- European settlement (DUC, 2010). Wetlands were mainly drained during or shortly after European settlement to increase arable land available for agriculture. Wetlands provide important hydrologic functions in the landscape, such as decreasing peak flows and increasing low flows. To address wetland loss, wetland conservation policies and wetland restoration programs, such as ALUS, have been implemented. ALUS (Alternative Land Use Services) is a compensation based program, operating in Norfolk County, designed to encourage the production of ecosystem services on farmlands through the implementation of BMPs, such as wetland restoration (ALUS, 2019a). Briefly, wetland restoration locations were identified with GIS-based techniques. The flow outcomes of wetland restoration in terms of peak flow decreases and low flow increases were estimated with the IMWEBs-Wetland model. The annual costs of wetland restoration were estimated with the opportunity costs of the forgone cropping returns, the restoration administration and engineering costs, and the inconvenience costs associated with farming a field with a wetland present. Lastly, cost-effectiveness was evaluated at both the subwatershed outlet and at individual wetland locations as the annual cost of wetland restoration divided by the peak flow decrease caused by wetland restoration. Peak flow decreases were used as the indicator of flow outcomes because wetland restoration did not result in low flow increases at all identified wetland restoration locations. There were three wetland scenarios with varying levels of wetlands present in the subwatershed, the existing wetland scenario, the wetland restoration scenario, and the loss of existing wetlands scenario. Moreover, there were also two climate scenarios, a historical climate scenario (January 1, 1984 – December 31, 2014) which uses historical climate data from nearby ECCC stations, and a future climate change scenario (January 1, 2015 – December 31, 2045) which uses climate change projections from the PRECIS model. Therefore, there was a total of six scenarios assessed in this research. The climate change scenario results in a 10.49 percent increase in average streamflow, 15.45 percent increase in median streamflow, 45.15 percent increase in minimum streamflow, 110

and a 29.02 percent decrease in maximum streamflow at the subwatershed outlet compared to the historical climate scenario. Therefore, maximum flows are higher and low flows are lower under the historical climate scenario. Thus, flow at the subwatershed outlet under the historical climate scenario exhibits more variability than under the climate change scenario likely due to the different sources of data (i.e. historical climate scenario uses observation data whereas climate change scenario uses simulated data). The results of this study indicate that wetland restoration occupying 0.25 percent of the Lynn River subwatershed land area results in an average peak flow decrease of 1.23 percent and 1.12 percent, under the historical climate and climate change scenarios, respectively. Moreover, losing the existing wetlands that occupy 0.38 percent of the Lynn River subwatershed land area results in an average peak flow increase of 1.63 percent and 1.07 percent, under the historical climate and climate change scenarios, respectively. As expected, the climate change scenario results in smaller percent changes to peak flows compared to the historical climate scenario because peak flows are smaller under the climate change scenario. Under the historical climate scenario, wetland 10 provides the largest flow retention of 4,878.81 m3/day, or 78 percent of flow into the wetland, of the existing wetlands, whereas wetland 111 provides the largest flow retention of 1,872.37 m3/day, or 52 percent of flow into the wetland, of the wetland restoration locations. Similarly, under the climate change scenario, Wetland 10 provides the largest flow retention of 3,878.52 m3/day, or 78 percent of flow into the wetland, of the existing wetlands, whereas wetland 111 provides the largest flow retention of 1,672.54 m3/day, or 62 percent of flow into the wetland, of the wetland restoration locations. Results indicate that the existing wetlands typically provide greater flow retention than the wetland restoration locations. Moreover, the wetlands with the largest flow retention values have large contributing areas and above average wetland area and wetland volumes. The subwatershed outlet would have an average annual peak flow decrease of 4,650.96 m3/day if the top 10 best wetland restoration locations for flow retention were restored under the historical climate scenario. Comparatively, restoring all 59 wetlands in the subwatershed would result in an average annual peak flow decrease of 8,746.91 m3/day at the subwatershed outlet. Thus, by spatially targeting the top 10 best wetland restoration locations, 53.17 percent of the subwatershed outlet peak flow decrease that is achieved by all 59 wetland restoration locations can be achieved with only 10. Similarly, the subwatershed outlet would have an average annual

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peak flow decrease of 4,032.14 m3/day if the top 10 best wetland restoration locations for flow retention were restored under the climate change scenario. Comparatively, restoring all 59 wetlands in the subwatershed under the climate change scenario would result in an average annual peak flow decrease of 6,929.00 m3/day at the subwatershed outlet. Thus, by spatially targeting the top 10 best wetland restoration locations, 58.19 percent of the subwatershed outlet peak flow decrease that is achieved by all 59 wetland restoration locations can be achieved with only 10. Wetland restoration results in little to no change to low flows at the Lynn River subwatershed outlet, as only 7 of the 31 years under the historical climate scenario result in increased low flows and only 11 of the 31 years under the climate change scenario result in increased low flows. Similarly, under the historical climate scenario, only 9 of the 59 identified wetland restoration locations increase low flows, and 17 of the 39 existing wetlands in the subwatershed increase low flows. Of the existing wetlands, wetland 10 provides the largest flow increase of 111.43 m3/day, or an average 76 percent increase in flow, whereas wetland 111 provides the largest flow increase of 41.90 m3/day, or an average 58 percent increase in flow, of the wetland restoration scenario under the historical climate scenario. Moreover, under the climate change scenario, only 5 of the 59 identified wetland restoration locations increase low flows, and 11 of the 39 existing wetlands increase low flows. Of the existing wetlands, wetland 10 provides the highest average flow increase of 65.00 m3/day, or an average 72 percent increase in flow, whereas wetland 111 provides the highest average flow increase of 22.47 m3/day, or an average 47 percent increase in flow, of the wetland restoration locations under the climate change scenario. As expected, the wetlands with the greatest flow retention also provide the largest increase to low flows (i.e. wetland 10 of the existing wetlands, and wetland 111 of the wetland restoration locations). Furthermore, under the climate change scenario fewer of the existing wetlands and the wetland restoration locations provide low flow increases and the low flow increases the wetlands provide are smaller than under the historical climate scenario. Moreover, existing wetlands provide more low flow increases and the existing wetlands low flow increases are larger than the wetland restoration low flow increases. These results are supported by Hansen et al. (2015) which explain that while many wetlands provide groundwater recharge, the majority of wetlands do not. However, with improved wetland parametrization (i.e. hydraulic

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conductivity, maximum volume/area vs. normal volume/area), wetland restoration locations and existing wetlands may show more low flow increases than was calculated in this research. The cost-effectiveness of peak flow decreases in the Lynn River subwatershed was determined to be $12.00/m3/day under the historical climate scenario and $15.15/m3/day under the climate change scenario. The cost-effectiveness of wetland restoration is decreased (i.e. higher cost-effectiveness value) under the climate change scenario due to the smaller peak flows of the climate change scenario. The north-west corner of the subwatershed contains several wetland restoration locations that are not overly cost-effective. The wetlands with poor cost- effectiveness have small flow retention values due to their small contributing areas, and slightly above average opportunity costs compared to other wetlands, thus these wetlands should not be targeted for wetland restoration. Moreover, the wetland restoration locations that exhibit the greatest cost-effectiveness have large contributing areas and either slightly above average or slightly below average opportunity costs and inconvenience costs. Thus, the variability in cost- effectiveness is largely caused by the variability of individual wetlands flow retention and less by the variability in wetland opportunity costs and inconvenience costs. My research illustrates the advantage of the integrated modelling approach, which considers both the economic costs and the environmental outcomes of wetland restoration. My research can be used to inform decision makers on the increased cost-effectiveness of spatially targeting wetland restoration efforts based on the spatial variability of both the water quantity outcomes and the economic costs of wetland restoration. The cost-effectiveness values can be used by conservation managers and decision makers to estimate the average cost of reducing a specified amount of peak flows with wetland restoration efforts in the Lynn River subwatershed. Moreover, the cost-effectiveness figures and tables can be used to spatially target wetland restoration efforts to individual wetland restoration locations with high cost-effectiveness. The IMWEBs-Wetland model can be used to illustrate to farmers the impact that restoring an individual wetland on their property would have on the local flow regime as well as the streamflow at the Lynn River subwatershed outlet. IMWEBs-Wetland can illustrate the local flow impacts of restoring an individual wetland on a farmer’s field due to its fully distributed model structure. In contrast, most hydrologic models currently used to assess wetland restoration efforts are semi-distributed, which clump wetlands together for parametrization (Golden et al. 2014). Enabling farmers to see the local flow impacts of wetland restoration on their farm fields

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may result in wetland restoration seeming more favourable to farmers because local flow changes caused by wetland restoration can decrease springtime peak flows which can also decrease the length of time agricultural fields are spent flooded or fully saturated in the springtime. This can allow farmers with historically flooded fields to plant earlier. 5.1 Limitations and Recommendations This section presents the limitations encountered during the preparation of this thesis and identifies recommendations to mitigate these limitations in future studies. A majority of the limitations encountered throughout this thesis are associated with data. For instance, the IMWEBs-Wetland model requires the wetland bottom hydraulic conductivity be specified. However, field measured data of wetland bottom hydraulic conductivity were not available in the Lynn River subwatershed, thus an assumption of 0.5 mm/hour for the hydraulic conductivity of wetland restoration locations and 0.05 mm/hour for the hydraulic conductivity of the existing wetlands was made. The wetland bottom hydraulic conductivity assumptions impact the calculated flow retention values as well as the low flow increase values because wetland bottom hydraulic conductivity dictates the rate of water percolation through the wetland bottom. The rate of water percolation through the wetland bottom impacts the flow retention values because wetlands with increased bottom hydraulic conductivities will be able to retain more flows, as some flow is released through the wetland bottom as percolation, which results in more volume being available for flow retention. The rate of water percolation through the wetland bottom also impacts the calculated low flow increases because the percolated water will eventual reach the groundwater table and become groundwater recharge. Thus, measuring the bottom hydraulic conductivity of existing wetlands and restored wetlands would be a useful future study. The crop management assumptions made for estimating production costs in the subwatershed simplify the calculations of the opportunity costs because it is assumed that all growers in the subwatershed produce crops with the same crop management practices (i.e. all corn is grain corn, genetically modified, and no-till). Similarly, the crop management assumptions used in the IMWEBs-Wetland model for simulating crop production do not vary by field, and instead all fields with the same crops are assumed to have the same management (i.e. all corn is fertilized with 30 kg/ha of phosphorus on May 5). These assumptions could be improved upon with a survey being administered to farmers in the subwatershed. A future study

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that includes a survey of crop management practices such as tillage, fertilization, planting, harvesting, and genetic modification would yield meaningful information. The wetland economic cost estimation done in this research could be more complex by incorporating variability in crop prices, crop yields, or crop costs. Variability in any of these input cost parameters would result in a distribution of net returns for each crop instead of a constant net return for each crop. This would result in a distribution of cost-effectiveness instead of one cost-effectiveness value for each wetland restoration location. Moreover, I did not estimate the economic costs of losing the existing wetlands in the subwatershed. The economic costs of losing the existing wetlands in the subwatershed would be composed of the costs of draining the wetland and the possible gross margin benefits in the future. Estimating the economic costs of losing the existing wetlands in the subwatershed would be a useful future study to determine which existing wetlands in the Lynn River subwatershed have the highest cost-effectiveness for wetland conservation. The IMWEBs-Wetland model classifies five types of isolated wetlands based on Ducks Unlimited Canada wetland inventory data, Drained-Altered, Drained-Consolidated, Drained- Lost, Altered, and Intact. Liu et al. (2016) explain that these wetland classifications were chosen because the IMWEBs-Wetland model is likely to be applied in Alberta and DUC will be a major source of wetland data for the model. Moreover, the DUC equations used to estimate wetland volume from wetland surface area were originally designed for prairie wetlands in Saskatchewan (Wiens, 2001). Thus, the results in this study may be limited because IMWEBs-Wetland was first designed for prairie watersheds. A future study that evaluates the applicability of the DUC wetland classifications and the DUC equations for estimating wetland volume to southern Ontario wetlands would be useful for IMWEBs-Wetland model development. The IMWEBs-Wetland calibration and validation stages were time consuming due to a lack of automatic calibration and because calibration is done at the pixel level. Future studies could look at the use of automatic calibration techniques for the IMWEBs-Wetland model. Moreover, the manual calibration process could be improved upon in future studies by enabling IMWEBs-Wetland to run at the field level instead of the pixel level during calibration and validation. Enabling IMWEBs-Wetland to run at the field level instead of the pixel level would decrease the length of time for each model run, which would quicken the calibration and validation stages.

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Appendices

Appendix A IMWEBs-Wetland Data Preprocessing A.1 DEM The 30 m DEM used in the IMWEBs-Wetland model is created from the 1 m LiDAR DEM created for wetland identification in section 3.3. The 1 m LiDAR DEM was resampled to 30 m using Whitebox GAT Resample tool, specifying bilinear interpolation as the resampling method. A.2 Landuse The landuse data is taken from the Southern Ontario Land Resource Information System (SOLRIS) version 2.0 (2011) with a spatial resolution of 15 meters. The landuse data was clipped to the Lynn River subwatershed using the Clip function in ArcGIS. The landuse data was then resampled to a spatial resolution of 30 meters using the nearest neighbor resampling method using the Resample tool in ArcGIS. The resultant raster was then projected to Universal Transverse Mercator North American 1983 Zone 17N using the Project Raster tool in ArcGIS. The landuse lookup table is created to link the SOLRIS landuse codes to corresponding IMWEBs-Wetland landuse codes. Table A-1 shows the SOLRIS landuse classes and the corresponding IMWEBs-Wetland landuse codes chosen for each landuse type. Table A-1 Lynn River subwatershed Landuse Lookup Preparation

IMWEBs IMWEBs Percent SOLRIS Class Code ID IMWEBs Class 44.63% Tilled AGRR 2 Agricultural Land-Row Crops 25.39% Undifferentiated AGRL 1 Agricultural Land-Generic 7.10% Treed Swamp WETF 10 Wetlands-Forested 6.70% Deciduous Forest FRSD 7 Forest-Deciduous 3.38% Transportation UTRN 107 Transportation 3.11% Mixed Forest FRST 6 Forest-Mixed 2.91% Built-Up Area - Impervious URHD 101 Residential High-Density 2.61% Built-Up Area - Pervious URLD 104 Residential Low-Density 1.08% Hedge Rows FRST 6 Forest-Mixed 0.88% Forest FRST 6 Forest-Mixed 0.79% Plantations - Tree Cultivated FRST 6 Forest-Mixed 0.37% Thicket Swamp WETL 9 Wetland-Mixed 0.32% Extraction - Aggregate UIDU 106 Industrial 0.26% Coniferous Forest FRSE 8 Forest-Evergreen 0.26% Open Water WATR 18 Water 0.17% Marsh WETN 11 Wetland-Non-Forested 0.04% Open Tallgrass Prairie RNGE 15 Range-Grasses

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A.3 Crop Management The IMWEBs-Wetland model requires crop management information to simulate crop production in the subwatershed. The IMWEBs-Wetland model requires crop management information such as planting date, harvest date, percent residue cover after harvest, tillage method, tillage dates, fertilizer and manure application rates, and fertilizer and manure application dates. Detailed crop management data at the field level are not readily available for the Lynn River subwatershed, thus generalized crop management assumptions were created for the dominant crops grown in the subwatershed. The dominant crop types are corn, soybean, hay, winter wheat, rye, vegetables, tobacco, ginseng, and orchards, which were identified from AAFC’s Crop Inventory datasets for years 2011-2017. Table A-2 lists the crop management assumptions for the Lynn River subwatershed IMWEBs-Wetland model. These assumptions are taken from a previous SWAT model done on a nearby southern Ontario watershed (Ontario Ministry of the Environment, 2014), Agriculture Canada’s Fertilizers for Various Crops (Agriculture Canada, 1956), and Alternative Field Crops Manuals (Harrison et al. 2019; OMAFRA, 2019). The crop management assumptions were used to develop a 7 year crop rotation which is repeated every 7 years in the IMWEBs-Wetland model. To develop the crop rotation, AAFC’s Crop Inventory for years 2011-2017 was reclassified to only contain crops with developed crop management assumptions, as listed in Table A-2. The Zonal Statistics as Table tool in ArcGIS was used with the reclassified crop inventories as input, using the field boundary layer as the feature zone data, to determine which crop type is majority in each field (i.e. the most dominant crop in each field). This was repeated for 2011-2017 Crop Inventories. These tables were then imported to Excel and crop management tables were generated for each field based on the assumptions listed in Table A-2.

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Table A-2 Crop management assumptions made for the Lynn River subwatershed IMWEBs-Wetland model

Crop Operation Date Rate Corn Planting May 1 Chemical fertilizer Pure P (broadcast) May 5 30 kg/ha Pure N (broadcast) May 5 125 kg/ha Manure Swine manure April 30 44900 l/ha Tillage Spring till (cultivator) May 5 Fall till (chisel plough) November 1 Harvest and kill October 15 Residue cover 10% Soybeans Planting May 10 Chemical fertilizer Pure P (broadcast) May 10 35 kg/ha Pure N (broadcast) May 10 40 kg/ha Manure Swine manure October 15 (last fall) 78,600 l/ha Tillage Spring till (cultivator) May 15 Fall till (chisel plough) November 1 Harvest October 1 Residue cover 30% Hay Planting October 1 (last fall) Chemical fertilizer Pure P (broadcast) October 1 (last fall) 35 kg/ha Pure N (broadcast) October 1 (last fall) 85 kg/ha Harvest (cut 1) June 30 Harvest (cut 2) September 10 Residue cover 60% Winter Wheat Planting October 10 (last fall) Chemical fertilizer Pure P (broadcast) October 10 (last fall) 80 kg/ha Pure N (broadcast) October 10 (last fall) 20 kg/ha Pure N (broadcast) May 1 120 kg/ha Tillage Fall till (cultivator) October 10 (last fall) Harvest and kill July 25 Residue cover 50% Rye Planting October 10 (last fall) Chemical Fertilizer Pure N (broadcast) May 5 30 kg/ha Pure N (broadcast) August 5 20 kg/ha Pure P (broadcast) August 5 5 kg/ha Tillage Disked (chisel plough) August 5 Residue 100%

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Table A-2 Crop management assumptions made for the Lynn River subwatershed IMWEBs-Wetland model, Continued Crop Operation Date Rate Vegetables Planting May 10 Chemical fertilizer Pure P (broadcast) May 10 70 kg/ha Pure N (broadcast) May 10 80 kg/ha Tillage Spring till (cultivator) May 10 Fall till (chisel plough) October 1 Harvest October 1 Residue cover 30% Tobacco Planting May 25 Chemical Fertilizer Pure N (planting) May 25 34 kg/ha Pure P (planting) May 25 39 kg/ha Pure N (side-dressing) June 22 56 kg/ha Pure P (side-dressing) June 22 10 kg/ha Tillage Spring till (cultivator) May 25 Fall till (chisel plough) September 15 Harvest and kill September 15 Residue cover 30% Ginseng Planting September 1 Chemical fertilizer Pure P (broadcast) September 1 175 kg/ha Manure Beef manure June 1 148,263 l/ha Harvest and kill October 1 (3 years later) Residue cover 10% Orchards Planting N/A Chemical fertilizer Pure N (top-dress) May 5 65 kg/ha Pure P (top-dress) May 5 15 kg/ha Harvest September 15 Residue cover 100%

A.4 Soil Data The soil data is taken from Agriculture and Agri-Food Canada’s Ontario Detailed Soil Survey (DSS) version 3.0 (2015) at a 1:50,000 scale. The DSS dataset is comprised of the geospatial polygon boundaries as a shapefile, with linkages to attribute data found in the associated Component File (CMP), Soil Names File (SNF), Soil Layer File (SLF), Component Rating Table (CRT), Polygon Rating Table (PRT). The polygon shapefile data (dss_v3_on.shp) was clipped to the Lynn River subwatershed using the Clip function in ArcGIS. The resultant

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shapefile was projected to Universal Transverse Mercator North American 1983 Zone 17N using the Project Raster tool in ArcGIS. The Component File (dss_v3_on_cmp.dbf) was then joined to the shapefile data (dss_v3_on.shp) using ‘POLY_ID’ as the common field in the ArcGIS Join function. The Component Rating Table (dss_v3_on_crt.dbf) was then joined to the shapefile data (dss_v3_on.shp) using ‘CMP_ID’ as the common field in the ArcGIS Join function. After both database tables were joined, the attribute table of the shapefile (dss_v3_on.shp) was exported to a new table for further analysis. This new table was imported to Excel where the usersoil table and soil lookup tables were created. The usersoil table was created by first identifying the unique soil layers within the Lynn River subwatershed. The attribute information found in the Soil Layer File (SLF) was then used to define each soil layer’s required properties. Required properties in the usersoil table include the soil depth, porosity, texture, hydraulic conductivity, field capacity, relative moisture, wilting point, bulk density, percent sand, percent clay, percent silt, and Universal Soil Loss Equation k value. Unfortunately, some of the soil polygons in the DSS were not classified, such as those associated with urban areas. For modelling purposes, all soil polygons in the watershed must be defined in the usersoil table. Therefore, some soil polygons in the original DSS were reclassified to the dominant soil type in the nearby neighbourhood. Moreover, AAFC’s Soil Survey Complex was also used as a supplement to classify some of the unclassified polygons in the DSS. Once all polygons were classified, the shapefile (dss_v3_on.shp) was converted to a raster TIF file with a spatial resolution of 30 meters using the Polygon to Raster tool in ArcGIS. Table A-3 and Figure A-1 show the usersoil table and the soil polygon layer for the Lynn River subwatershed IMWEBs-Wetland model.

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Table A-3 Usersoil table created for the Lynn River subwatershed IMWEBs-Wetland

model

P_ P_

Y1 K1

FC1

KS1

TY1

WP1

RM1

SILT1

CODE

SOIL_ SOIL_

SNAM

USLE_ USLE_

SAND1 CLAY1

POROSI DENSIT

INDEX1 DEPTH1 14 ALU 5 100 0.44 0.11 3.9 0.04 0.05 1.08 15 25 60 0.04 10 BAY 31 100 0.44 0.11 3.9 0.04 0.05 1.06 61 12 27 0.04 40 BFO 4 100 0.45 0.19 4.5 0.04 0.09 1.33 45 33 22 0.13 29 BOO 44 100 0.45 0.19 4.5 0.04 0.09 1.34 65 6 29 0.18 32 BRR 25 100 0.45 0.19 4.5 0.04 0.09 1.30 68 12 20 0.18 6 BRT 3 100 0.46 0.23 5.8 0.03 0.12 1.33 7 19 74 0.30 35 BRT1 9 100 0.45 0.19 4.5 0.04 0.09 1.36 40 14 46 0.18 33 BVY 21 105 0.47 0.34 8.3 0.04 0.21 1.21 10 38 52 0.32 15 CWO 1 100 0.50 0.28 5 0.02 0.14 0.88 23 22 55 0.38 30 CWO1 42 100 0.45 0.19 4.5 0.04 0.09 1.38 66 6 28 0.13 5 FOX 24 100 0.44 0.11 3.9 0.04 0.05 1.28 64 12 24 0.04 2 GNY 59 110 0.44 0.11 3.9 0.04 0.05 1.08 78 8 14 0.04 31 LIC 39 100 0.45 0.19 4.5 0.04 0.09 1.22 76 10 14 0.18 9 NDE 100 150 0.45 0.19 4.5 0.04 0.09 0.88 84 6 10 0.35 27 OKL 40 100 0.45 0.19 4.5 0.04 0.09 1.14 70 10 20 0.18 13 SIH 56 130 0.45 0.19 4.5 0.04 0.09 1.47 90 4 6 0.18 8 SLI 43 100 0.45 0.19 4.5 0.04 0.09 1.15 69 9 22 0.18 3 STD 47 100 0.44 0.11 3.9 0.04 0.05 1.38 64 5 31 0.04 39 TLD 4 100 0.47 0.34 8.3 0.04 0.21 1.14 7 42 51 0.32 34 TLD1 9 100 0.45 0.19 4.5 0.04 0.09 1.40 56 18 26 0.18 21 TUC 5 100 0.46 0.23 5.8 0.03 0.12 1.22 22 23 55 0.30 25 TUC1 29 100 0.45 0.19 4.5 0.04 0.09 1.53 79 8 13 0.18 38 UNBRT 3 100 0.46 0.23 5.8 0.03 0.12 1.33 7 19 74 0.30 18 UNGNYO 59 110 0.44 0.11 3.9 0.04 0.05 1.08 78 8 14 0.04 1 UNPFL 87 240 0.44 0.11 3.9 0.04 0.05 1.43 97 1 2 0.04 23 UNSTD 47 100 0.44 0.11 3.9 0.04 0.05 1.38 64 5 31 0.04 22 UNVSS 16 105 0.45 0.19 4.5 0.04 0.09 1.13 55 17 28 0.13 12 UNWSH 58 110 0.45 0.19 4.5 0.04 0.09 1.38 72 4 24 0.18 28 UNWUSO 38 100 0.45 0.19 4.5 0.04 0.09 1.21 74 10 16 0.18 7 UNZAL 5 100 0.50 0.28 5 0.02 0.14 1.08 15 25 60 0.38 26 UNZOR 35 150 0.47 0.34 8.3 0.04 0.21 0.10 23 60 17 0.32 20 VIT 62 100 0.45 0.19 4.5 0.04 0.09 1.34 72 4 24 0.35 24 VSS 35 105 0.47 0.34 8.3 0.04 0.21 0.10 55 17 28 0.32 11 WAM 78 150 0.44 0.06 3.4 0.02 0.02 1.41 89 2 9 0.08 16 WAT 59 175 0.44 0.11 3.9 0.04 0.05 1.41 80 4 16 0.11 17 WIL 49 142 0.45 0.19 4.5 0.04 0.09 1.39 71 5 24 0.18 37 WIL1 53 100 0.45 0.19 4.5 0.04 0.09 1.32 79 6 15 0.13 19 WRN 53 100 0.44 0.11 3.9 0.04 0.05 1.30 77 6 17 0.11 4 WSH 58 110 0.45 0.19 4.5 0.04 0.09 1.38 72 4 24 0.18 36 WUS 38 100 0.45 0.19 4.5 0.04 0.09 1.21 74 10 16 0.18

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Figure A-1 The Lynn River subwatershed soil types A.5 Streams The streams layer is taken from Land Information Ontario’s Ontario Hydrology Network – Watercourse layer (2010) with a scale of 1: 10,000. The Watercourse layer was clipped to the Lynn River subwatershed using the Clip function in ArcGIS. The resultant shapefile was then projected to Universal Transverse Mercator North American 1983 Zone 17N using the Project tool in ArcGIS. A.6 Field Boundaries Only field boundaries were defined in the Lynn River subwatershed, as detailed farm boundary data were not available. Field definition was done with the 2017 Crop Inventory. First, the 2017 Crop Inventory was reclassified to only contain agricultural crops (i.e. no built-up area, no forests, etc.). The Region Group tool in ArcGIS was then used to identify regions of neighbouring cells with the same crop type, and a unique number was assigned to each region in the output. Four neighbors were specified instead of 8 neighbors in the Region Group tool as using 8 neighbors would result in more cells being assigned to the same region, resulting in larger field boundaries that likely would not line up well with other years of crop inventory data. The Raster to Polygon tool in ArcGIS was then used to convert the Region Group output from a raster to a polygon, ensuring that polygons are not simplified. The resultant polygon layer was then Clipped to the agricultural landuses in the landuse layer (i.e. tilled and undifferentiated) to 131

ensure the field boundaries only cover agricultural landuses. Next, all polygons in the layer were selected, and the Explode function in ArcGIS was used to ensure there were not any multipart polygons. Lastly, Calculate Geometry was used to determine the area of the polygons, and polygons 900 m2 and larger were selected and exported to create the Field boundaries layer. Polygons smaller than 900 m2 represent less than 1 cell in the 30mx30m model and thus were removed. A.7 Existing Wetlands The existing wetlands used in the IMWEBs-Wetland model are taken from SOLRIS landuses classes Marsh, Thicket Swamp, and Open Water. ArcGIS Calculate Geometry function was used to estimate the existing wetlands’ surface areas. The IMWEBs-Wetland model estimated the existing wetlands’ volumes using the empirical equations shown in Equation A-1 and Equation A-2. The calculated volumes from the empirical equations resulted in an acceptable average depth value of 0.28 m. For comparison, Babbar-Sebens et al. (2013) assumed a typical wetland design depth of 0.5 m in their SWAT model of a watershed in Indiana, USA. Moreover, Martinez-Martinez et al. (2015) simulated all wetlands in their SWAT model of a watershed in Michigan, USA with a depth of 0.3048 m (12 inches). Lastly, Martinez-Martinez et al. (2014) used SWAT to evaluate wetland restoration scenarios with varying wetland surface areas (50, 100, 250, and 500 ha) and with varying wetland depths (15, 30, 61, and 91 cm). Martinez- Martinez et al. (2014) found that increasing wetland surface area had a greater impact on long term average daily streamflow than increasing wetland depth. Thus, streamflow is more sensitive to wetland surface area than wetland depth. For wetlands with a surface area less than 70 ha: Equation A-1 푽 = ퟐ. ퟖퟓ 푨ퟏ.ퟐퟐ

For wetlands with a surface area greater than 70 ha: Equation A-2 푽 = ퟕ. ퟏ푨 + ퟗ. ퟗퟕ where 퐴 is the wetland surface area (ha) and 푉 is the corresponding full supply volume, expressed in 103m3 (Wiens, 2001). The existing wetlands are all assumed to be Altered wetlands, as defined by Ducks Unlimited Canada. Flow out of Altered wetlands occurs when water level in the wetland is higher than the drain bottom elevation. To characterize Altered wetlands, the maximum volume

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was calculated with Equation A-1 and Equation A-2 based on SOLRIS wetland data, while the normal wetland surface area (i.e. corresponding to the depth to drain bottom) was assumed to be 1/3 of the maximum surface area, and the corresponding normal volume was calculated with Equation A-1 and Equation A-2 (Liu et al. 2018). The existing wetlands bottom hydraulic conductivity was assumed to be 0.05 mm/hour, as field measured data was not available, and this is the default wetland bottom hydraulic conductivity in the IMWEBs-Wetland model. A.8 Climate Data Figure A-2 shows the location of the four Environment and Climate Change Canada (ECCC) climate stations used for the Lynn River subwatershed IMWEBs-Wetland model. The required climate parameters and units for the IMWEBs-Wetland model are listed in Table A-4, as well as the ECCC stations used for each climate parameter. The ECCC Simcoe climate station ceased operation in 1999, and there are currently no ECCC climate stations operating in the Lynn River subwatershed. The IMWEBs-Wetland model requires climate data for every day of simulation, thus any missing records or years without data were filled with data from the nearest ECCC climate station. Table A-5 shows the nearest ECCC climate stations for each climate station. Hourly data for relative moisture, wind speed, and wind direction were all converted to the daily time step by averaging. Hourly solar radiation data was converted to the daily time step by summing.

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Figure A-2 Climate station locations for the Lynn River subwatershed IMWEBs-Wetland model

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Table A-4 IMWEBs-Wetland required climate parameters and units, as well as ECCC stations used for each climate parameter and the dates with available data over the simulation period

Climate Parameter Units Stations Dates Simcoe 1970-1986 ; 1992-1999 Delhi 1970-2017 Precipitation mm Scotland 1971-2014 Waterford 1971-2014 Simcoe 1970-1986 ; 1992-1999 Maximum/Minimum C° Delhi Temperature 1970-2017 Scotland 2001-2014 Simcoe 1970-1986 ; 1994-1999 Relative Moisture % Delhi 2000-2017 Solar Radiation MJ/m2.day Delhi 2000-2014 Simcoe 1970-1986 ; 1994-1999 Wind Speed m/s Delhi 2000-2017 Simcoe 1970-1986 ; 1994-1999 Wind Direction 10's degree Delhi 2000-2017

Table A-5 The ECCC climate stations used in the IMWEBs-Wetland model and their nearest ECCC climate station used to fill missing climate records

ECCC Climate Station Nearest Stations (first listed is nearest) Simcoe Waterford > Delhi > Scotland Delhi Scotland > Simcoe > Waterford Scotland Delhi > Waterford > Simcoe Waterford Simcoe > Scotland > Delhi

A.9 Flow Data Flow data for years 2000-2017 is taken from a Water Survey of Canada flow station on the Lynn River, downstream of the Town of Simcoe, as shown in Figure A-3. Water Survey of Canada flow data is reported daily, expressed in m3/sec.

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Figure A-3 Flow station for the Lynn River subwatershed IMWEBs-Wetland model A.11 Point Source Data There is one municipal wastewater treatment plant in the Lynn River subwatershed, the Simcoe Waste Pollution Control Plant (WPCP). The Simcoe WPCP discharges treated wastewater to the Lynn River, south-east of the town of Simcoe, shown in Figure A-4. Total monthly sewage flow for 2008-2016 are taken from the Government of Ontario’s Municipal Treated Wastewater Effluent for the Simcoe WPCP. Total monthly sewage flows were converted to daily flows by dividing the monthly flow value by the number of days in that month. Average flow values were used for years 1970-2007, as no older data exists. Average flow values were also used for the 2014-2045 future climate scenario.

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Figure A-4 Simcoe Municipal Wastewater Treatment Plant location in the Lynn River subwatershed

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