FINAL REPORT JANUARY 2015

FINAL REPORT JANUARY 2015 Assessing Impacts of Climate Change on Water Quantity and Quality in Two Different Agricultural Watersheds in Southern Ontario RESEARCH TEAM Principle Investigator Dr. R. P. Rudra, Professor School of Engineering, University of Guelph Guelph, ON, N1G 2W1 Phone: (519) 824-4120 x52110 Fax: (519) 836-0227 Email: rrudra@uoguelph.ca Co-Investigators Dr. B. K. Dubey, Assistant Professor School of Engineering, University of Guelph Guelph, ON, N1G 2W1 Phone: (519) 824-4120 x52506 Fax: (519) 836-0227 Email: bdubey@uoguelph.ca Golmar Golmohammadi, PDF School of Engineering, University of Guelph Guelph, ON, N1G 2W1 Phone: (519) 824-4120 x54321 Email: ggolmoha@uoguelph.ca January 2015 1

FINAL REPORT JANUARY 2014 This Final Report provides the Assessing Impacts of Climate Change on Water Quantity and Quality of Two different Agricultural Watersheds in Southern Ontario). The main objective of current research is to assess the impacts of climate change on water resources and sediment loads in cold climate condition of Ontario This interim progress report contains literature review on climate change modeling, an overview of existing hydrologic models, selection of a watershed model and a field scale model to study impact of climate change on water quantity and water quality in Ontario conditions, selection of watershed and field, and preliminary evaluation of the selected models. This report also, contains results of - SWAT model evaluation in the three watersheds in Canagagigue Creek watershed in the Grand River Basin. DRAINMOD model evaluation in a 14 ha field located near Ottawa, Ontario. - Climate change impact on Hydrology and sediment load at three Canagagigue watersheds using SWAT model - Climate change impacts on hydrology and nitrogen in Green Belt filed using DRAINMOD model Here are the objectives and tasks listed under the Agreement: A3. PROJECT OBJECTIVE The Recipient s specific project objectives are: 1. To prepare inputs to hydrological models from the Ontario-focused high-resolution (25km x 25km and 45km x 45 km) probabilistic climate projections; 2. To evaluate the effect of climate change on annual and seasonal water budget at field and watershed scale using watershed modelling approach; and, 3. To evaluate the effect of climate change on soil quality (soil erosion) and downstream surface water quality (sediment and nutrients) using hydrological modelling approaches. A4. RECIPIENT RESPONSIBILITIES The Recipient will: A4.1 Develop climate change scenarios using the projections from the following Ontario Ministry of the Environment (MOE) funded projects: a. High-Resolution (45km 45km) probabilistic climate projections over Ontario from multiple Regional and Global Climate Models. b. High-Resolution (25km 25km) Probabilistic Climate Projections over Ontario from Large Ensemble Runs of the UK PRECIS regional climate model. 3

c. prepare precipitation and temperature projections as input data for the hydrological modelling approaches to quantify the impact of climate change on soil and water resources under future climate scenarios A4.2 Select a field scale model capable of simulating soil erosion and surface and subsurface drainage water quality. This may include a single model, such as EPIC having the capability to simulate soil erosion, soil productivity, and water quality, however, other models such as WEPP for soil erosion and RZWQM or DRAINMOD for drainable water quality will be examined to shortlist a field scale model. A4.3 Use the climate data developed in section A4.1 and the model selected in section A4.2 to quantify the impact of climate change on annual and seasonal water budget, soil erosion and surface water quality (sediment, phosphorus and nitrogen) in the Kettle Creek A4.4 Select a watershed model capable of simulating stream water quality. The candidate models will include SWAT, AnnAGNPS, HSPF, and CANWET. A4.5 Use the climate data developed in section A4.1 and the model selected in section A4.4 to evaluate the impact of climate change on water budget, stream flow, and stream water quality in the Kettle Creek watershed in Ontario. A4.6 Prepare a final report to summarize the project findings, and publicize the results within a year of Project completion via conference presentations or journal publications or website. Provide proof of the above publication along with the final technical report. A5. TIMELINES The Recipient will undertake the responsibilities as described in section A4 according to the following approximate timelines for the Project: Recipient Responsibilities as identified by section number Section A4.1 Section A4.2 and Section A4.4 Section A4.3 and Section A4.5 Section A4.6 Date (after Effective Date of the Agreement) First 3 months From the 4th to 6th month From the 7th to the 10th month From the 11th to the 12th month. Completed Task during the period from March 15, 2014 to July 31, 2014: The research group has successfully completed most of task listed in the Section A4.1, A4.2, and A4.4. Overall, significant progress has been made in the project. - Two watersheds, Canagagigue and Kettle Creek were selected for this research in consultation with Dr. Goel from the Ontario Ministry of the Environment and Climate Change. The first watershed selected was Canagagigue Creek Watershed which was extensively studied during PLUARG (Pollution due to Land Use Activities Reference Group) period and extensive data are available for the calibration and validation of model before used to evaluate the impact of climate change on water resources. The other watershed was Kettle Creek watershed which was one of the watersheds used in the paired watershed study during the Ontario SWEEP (Soil Water Environment 4

Enhancement Program) Program. Recently, this watershed was also used for the evaluation of best management during the Ontario Watershed Based BMP Evaluation (WBBE) program. Due to lack of sediment data this watershed was not further investigated in this project. - Detailed literature review of several widely used hydrologic models has been conducted and the Soil and Water Assessment Tool (SWAT) was selected for this research, mainly because of continuous development of its user-friendly interface from GIS, complete documentation support in its theoretical interpretation, a user manual and tutorial explanations, and even open source code is available with a free download, physically based analysis functions, flexible input modifications and extensive applications around the world. Moreover, it can be applied to different size of watersheds. - Meetings with (Dr. Pradeep Kumar Goel, Senior Scientist, Ministry of the Environment and Climate Change) on the regular time basis regarding the project and receiving technical advice for different stages of the project. - Collection of climate data; The climate dataset used in this project were provided by CCDP (Climate Change Data Portal) of Regina University which were derived from the high resolution (25km 25km) climate projections developed by the IEESC at University of Regina using the PRECIS modeling system (Wang and Gordon, 2013). The climatic parameters which are used in this project to run the SWAT model are: precipitation, min/max temperature, wind speed, relative humidity, all provided by CCDP for the period of 1960-1990 and 2015-2095. The climate data required for modeling by SWAT model is available for for the period 2015-2095 (Wang and Gordon, 2013). - Model setup and initial simulations for both watersheds using the different type of required information; The data required for this step were mainly the Digital Elevation Models (DEM) and maps of stream network and land use and soil data which provided by different organizations (see the details in report) and also climatic information derived from the closest stations to the study area. - Watershed model calibration and validation; The SWAT model was calibrated and validated for three watersheds. - Field scale model calibration and validation; DRAINMOD model was calibrated and validated for the Green Belt watershed in Ontario, Canada. - The climate scenario under consideration in this study based on PRECIS modeling system (Wang and Gordon, 2013), was used to run the SWAT model in three watersheds and for both historical (1960-1990) and future (2015-2095) time periods. - The SWAT model was applied to simulate annual, seasonal and monthly changes in streamflow for all three watersheds. These results will be used to evaluate the impacts of climate change on hydrology of these two watersheds in Ontario. - Finally, the validated SWAT model was used to analyze the effects of climate change on water quantity and quality. - The climate scenario under consideration in this study based on PRECIS modeling system (Wang and Gordon, 2013), was used to run the DRAINMOD model on a 14 ha field at the Agriculture Canada Green Belt for both historical (1960-1990) and future (2015-2095) time periods. - The DRAINMOD model was applied to simulate annual, seasonal and monthly changes 5

in tile drainage outflow. These results were used to evaluate the impacts of climate change on hydrology of the field. - Finally, the validated DRAINMOD model was used to analyze the effects of climate change on water quantity and quality. 6

EXECUTIVE SUMMARY Quantifying available water resources for natural ecosystem and society is one of the most important objectives for hydrologists. To assess the availability of water resources in a future climate, that is very useful to link hydrological models to data generated by atmospheric global circulation models. Therefore, hydrological simulations under climate change scenarios can be a useful tool to understand how a change in global climate could affect the availability and variability of regional water resources. This research addresses this important issue in two different watersheds in Ontario. The main goal of current study is to assess the impacts of climate change on water resources and sediment loads in cold climate condition of Ontario This report provides literature review on climate change modeling, an overview of existing hydrologic models, selection of a watershed model to study impact of climate change on water resources in Ontario conditions, selection of watersheds, and preliminary evaluation of the selected model. Based on the literature review presented in this report, the watershed scale model, Soil and Water Assessment Tool (SWAT) was selected to evaluate the impacts of climate change in two different watersheds, Canagagigue Creek in Ontario. This report contains results of a watershed scale model, SWAT completed on the Canagagigue Creek watersheds in the Grand River Basin and a field scale model, DRAINMOD, completed at the Green Belt field of Agriculture and Agri-Food Canada at Ottawa, Ontario. This report also includes both SWAT and DRAINMOD models predictions under future climate change scenario of PRECIS under different percentiles to evaluate the effects of climate change on annual and seasonal water budget, sediment and pollutants loads. INTRODUCTION The global climate is changing and is becoming one of the most important issues around the world (Solomon et al., 2007). One of the most significant potential consequences of climate change in the long-term would be changes in regional hydrological cycle, which in turn, can change surface and subsurface water quality and quantity regimes. The observational evidence around the world shows that many natural systems are being affected by regional climate changes (IPCC; Parry et al., 2007). Assessment of water resources variability involves different and complex interplay of hydrology, ecology, meteorology, climatology, soil science, agronomy and socio-economical components (IPRC, 2010). Analysis of the climate change impacts on quantity and quality of water resources in a watershed requires the use of model which has the capability to integrate the most important hydrological, chemical, and ecological processes. Integrated modeling approach is becoming an important tool in regional scale analysis of water resources variability. Therefore, hydrologic models have become an important tool for the analysis of climate change repercussions and water resources balance (Singh et al., 1995). This report focuses on review of existing hydrologic models, selection and application of a hydrologic model to analyze the impacts of climate change on water quantity, sediments and nutrients loads in watersheds in Ontario. The climate data in this project were provided by CCDP (Wang et al., 2013). The PRECIS model was driven by an ensemble of GCMs which are based on the standard HadCM3Q0 model, each GCM has a set of perturbations to its dynamical and physical formulation. These perturbations are made within the known bounds of modelling uncertainty along with HadCM3Q (Wilson et al. 2011). This ensemble of GCMs can be used to estimate the uncertainty in regional climate 7

model results due to uncertainty in driving GCM formulation and the uncertainty in fine-scale climate change due to uncertainties in global and regional model formulation. The external forcing was from the SRES A1B emissions scenario. The main objective of this project is to assess the effects of climate change, based on the projection from the PRECIS on water resources and sediment at various watersheds in Ontario. Project Objectives: - To prepare inputs to hydrological models from the Ontario-focused high resolution (25km 25km and 45km 45km) probabilistic climate projections. - To evaluate the effects of climate change on annual and seasonal water budget at field and watershed scale using watershed modeling approach. - To evaluate the effect of climate change on soil quality (soil erosion) and downstream surface water quality (sediments and nutrients) using hydrological modeling approaches. 8

LITERATURE REVIEW This chapter provides some background information about climate modelling, hydrologic models and their roles in climate change. Climate Change Modeling Global Climate Models Global climate models (GCM) can simulate the response from climate system to the variation in GHG concentrations. The resolution of the atmospheric part of a typical GCM is about 250 km in the horizontal with 20 levels in the vertical. Hence GCMs make projections at a relatively coarse resolution it cannot represent the fine-scale detail that characterizes the climate in many regions of the world, especially in regions with complex topography or heterogeneous land surface cover or coastlines. Therefore, GCMs cannot access the spatial scales that are required for climate impact and adaptation studies (Wang et al., 2012). Downscaling GCM climate data downscaling approaches are ranked into two major categories: statistically based and dynamically based. Statistical downscaling approach is based on the construction of relationships between the largescale and local variables calibrated from historical data. Dynamical downscaling approach uses comprehensive physical models of the climate system. This allows direct modelling of the dynamics of the physical systems that characterize the climate of a region. Statistical relations between climate variables are established and climate data are then adjusted. This approach, although is computationally less costly and it can provide information at point locations, but is used less than the dynamical method because the statistical relationships may not hold in a future climate and that long time series of relevant data are required to form the relationships (Jones et al. 2004, Beaumont et al., 2008). Regional Climate Model (CRCM) Some studies require climate data on a smaller scale than the developed by GCMs. In such cases, Regional Climate Models (RCM) can be used. RCMs are similar to GCMs and have vertical levels but they have higher resolution, about 50 km or less. The development of Canadian RCM (CRCM) in 1990 (Caya and Laprise, 1999) and now CRCM is now a mature Regional Climate Simulator. Mitchell et al (2003) indicated the capacity of the CRCM to simulate relatively well the precipitation rate in winter over Canada in comparison with observed Climatic Research Unit (CRU) values. The fourth generation of the simulator (CRCM4) was developed is a more sophisticated land-surface scheme. The dynamical regional ocean-ice simulator was implemented for Hudson Bay (Saucier et al., 2004) and the Gulf of St-Lawrence (Saucier et al., 2003). A report presented an overview of the climate context in Ontario (Ouranous, 2010). This study was done by presenting the historical climate of Ontario as well as the ability of the CRCM simulation to reproduce it over the recent past period. It was observed that some biases do exist in the CRCM simulation and should be taken into account when analyzing climate change scenarios. A study was conducted by Langlois et al (2004) to assess the ability of the Canadian 9

Regional Climate Model (CRCM), developed by the Canadian Network for Regional Climate Model at the Universite du Québec a` Montréal (UQA`M) to simulate snow cover climate conditions over eastern Canada, a region characterized by a marked latitudinal range of climate conditions from maritime or continental in the south, to subarctic over northern Québec (Sturm et al., 1995; Brown and Goodison, 1996). This study focused on snow cover dynamics, specifically the ability of such a high spatial resolution climate model to simulate correctly at regional scale the onset and melt of snow during the fall and spring transition seasons. In conclusion, the application of CRCM is the most popular approach to develop climate change scenario in Canada. Also, the comparison of the modeling results from different climate change scenarios which applied to the same study area and the same problem can increase the confidence in the results and help the assessing and the understanding of the impacts of the climate change on hydrology. Regional Climate Model - PRECIS PRECIS (Providing Regional Climates for Impacts Studies) a regional modelling system is designed to provide regional climate scenarios for impacts studies (Wilson et al., 2011). PRECIS climate model is an atmospheric and land surface model of limited area and high resolution which is locatable over any part of the globe (CSEE, 2011). Dynamical flow, the atmospheric sulphur cycle, clouds and precipitation, radiative processes, the land surface and the deep soil are all considered. Boundary conditions are required at the limits of the model s domain to provide the meteorological forcing for the RCM. Information about all the climate elements as they evolve through being modified by the processes represented in the model is produced (Jones et al., 2004). A full range of meteorological variables can be diagnosed by the model. Predefined comprehensive sets of output variables, available at different temporal resolutions, are provided to allow experiments for standard purposes to be configured quickly. Hydrological Models Understanding the natural processes those occur in a watershed is a challenge for both scientists and engineers (Wu and Chen, 2009). Over the past few decades, great strides have been made in technology and modeling techniques that allow users to make informed and relatively accurate representations of ungauged watersheds that previously would have been impractical (Frana, 2012). Hydrologic and water quality models are useful tools to understand the problems and to identify acceptable solutions through best management practices (Borah and Bera, 2003). A model can be used to understand why a hydrologic system is behaving in a particular manner and to predict how a hydrologic system will behave in the future (Fetter, 2001). These uses, understanding of observed flow and prediction of future behavior, are integral in creating real world infrastructure that will sustainably exist within the hydrologic and hydraulic systems (Frana, 2012). With proper calibration, physically based models can be applied to widely varying landscapes to obtain useful results. Hydrological models are of a major importance for the analysis of climate change repercussions and water resources analysis (Singh et al., 1995). Several hydrologic models of varying degrees of complexity and scale are now available which can use data generated by atmospheric model as part of inputs (Yuri et al., 1999). 10

Modeling Climate Change Impacts The effects of climate change on hydrological regimes have become a priority area, both for water resources researchers and water resources managers. To evaluate the impact of climate change on hydrologic regimes the following steps may be followed. - General Circulation Model (GCM) to provide future climate change scenarios. - Downscaling techniques (both nested regional climate models, RCMs, and statistical methods) for downscaling the GCM output to the scales compatible with hydrological models - Hydrological models to simulate the effects of climate change on quantity and quality of water resources and water quality at different scales. Several attempts has been made on developing hydrological models for assessing the impacts of climate change with a focus on a realistic representation of the physical processes involved (Ma et al., 2000; Engeland et al., 2001; Kunkel and Wendland, 2002; Graham, 2004). Selecting a model for a particular case study depends on the objectives of the study and availability of data (Gleick, 1986, Ng and Marsalek, 1992 and Xu, 1999). It is not surprising that the hydrological literature now abounds with regional-scale hydrological simulations under greenhouse scenarios. Wang et al (2014) proposed a regional climate modeling approach based on the providing Regional Climates for Impacts Studies modeling system for assessing the potential impacts of climate change induced by increased greenhouse gases on the intensity and frequency of extreme rainfall events in Ontario Canada. In this study rainfall projections for future periods are used to develop projected intensity-duration-frequency curves and their plausible changes in 2030s, 2050s and 2080s. the results suggest that intensities of rainfall extreme events versus various durations with different return periods are likely to increase over time which would result in overall uplift in the exceedance values of rainfall intensity of extreme events. Despite the progress achieved in the last few years, there are still many unsolved problems. For example, the scale dilemma in applying hydrological models still exists (Schulze, 1997). Detailed regional climate scenarios that are used as input to hydrological models may be obtained from the coarse-scale output of GCMs by using simple interpolation, statistical downscaling, and high-resolution dynamic modeling (e.g., IPCC, 2001). Due to the difficulties involved in the modeling of hydrological response to the global climate change, various approaches have been adopted by researchers in different studies including the direct use of GCM outputs, regional climate models, macro-scale hydrologic models, global water balance models and hypothesizes scenarios. In another study conducted by Wang et al. (2014), a dynamical statistical downscaling approach was developed by coupling the PRECIS regional modelling system and a statistical method SCADS. The coupled approach performs very well in hind casting the mean temperature of present-day climate, while the performance for precipitation is relatively poor due to its high spatial variability and nonlinear nature but its spatial patterns are well captured. This coupled approach was implemented for projecting the future climate over the province of Ontario, Canada at a fine resolution of 10 km. The results show that there would be a significant warming trend throughout this century for the entire province while less precipitation is projected for most of the selected weather stations. The projections also demonstrate apparent spatial variability in the amount of precipitation but no noticeable changes are found in the spatial patterns. Wang et al., (2014) developed high-resolution projections of near-surface air temperature outcomes including mean, maximum, and minimum daily temperature over Ontario. The PRECIS modeling system is employed to carry out regional climate ensemble simulations driven 11

by the boundary conditions of a five-member HadCM3-based perturbed-physics ensemble (HadCM3Q0, Q3, Q10, Q13, and Q15). The results produced a consistent increasing trend in the near-surface air temperature with time periods from 2030s to 2080s. The most likely mean temperature in next few decades (i.e., 2030s) would be [ 2, 2] C in northern Ontario, [2, 6] C in the middle, and [6, 12] C in the south, afterwards the mean temperature is likely to keep rising by ~ 2 C per 30-years period. The continuous warming across the Province would drive the lowest mean temperature up to 2 C in the north and the highest mean temperature up to 16 C in the south. Wang et al., (2014) present an ensemble of high-resolution regional climate simulations for the province of Ontario, Canada, developed with the Providing Regional Climates for Impacts Studies (PRECIS) modeling system. A Bayesian statistical model is proposed through an advance to the method proposed by Tebaldi et al. for generating probabilistic projections of temperature changes at grid point scale by treating the unknown quantities of interest as random variables to quantify their uncertainties in a statistical way. Detailed analyses at 12 selected weather stations are used to investigate the practical significance of the proposed statistical model. The analysis of this study shows that there is likely to be a significant warming trend throughout the twenty-first century. The last approach, namely the use of hypothesized scenarios as input to catchment-scale hydrological models, is widely used (Nemec and Schaake, 1982; Graham and Jacob, 2000; Engeland et al., 2001; Dayyani et al., 2009; Arnell and Reynard, 1996; Leavesley, 1994; Boorman and Sefton, 1997; Dayyani et al 2012). This is because today s GCM-calculated precipitation is still very uncertain, and hence does not provide a reliable estimate that can be used as a deterministic forecast for hydrological planning. Accordingly, methods of simple alteration of the present conditions are widely used by hydrologists. This approach consists of the four steps (e.g., Loaiciga et al., 1996; Xu, 1999a) including first, determination of parameter values of a hydrological model in the study catchment using current climatic inputs and observed river flows for model (calibration and validation). Second step involves perturbation of historical time series of climatic data according to some climate change scenarios, and third, simulation of hydrological characteristics of the watershed under the hypothesized climate scenario using the calibrated hydrological model and fourth, comparison of the model simulations of the current and possible future hydrological characteristics. Soil and Water Assessment Tool (SWAT) has been applied to several projects in the USA dealing with the impact of climate change on water supplies and reservoir operations (Arnold and Fohrer 2005), including: regional impacts of climate change on the recharge of groundwater to the Ogallala aquifer (Rosenberg et al. 1999); impact of climate change on water yields in a high elevation, mountainous watershed (Stonefelt et al. 2000); impact of climate change on the Missouri River reservoir operation and water supply (Hotchkiss et al. 2000); and surface water irrigation and riparian management influenced by climate change (Wollmuth and Eheart 2000). A watershed scale forest hydrology model, DRAINWAT, has been applied to evaluate potential effects of climate change on the hydrology of a 3,000 ha managed pine forest in coastal North Carolina (Amatya et al. 2006). Quilbé et al. (2008) used the GIBSI model, which is a surface flow model based on the distributed hydrological model HYDROTEL, to assess the effect of climate change on river flow in Quebec, Canada. The simulations were performed for a reference period of 1970-1999 and a short-term future period of 2010-2039. Their results showed that in future watershed may exhibit a statistically significant decrease in annual runoff, an increase in runoff during winter, a decrease in spring peak flow, and no effect on summer low flows (Quilbé et al., 2008). 12

Few studies have focused to use the hydrologic models to predict future change in water quantity and quality in response to climate change at the regional level under the cold climatic conditions in Canada. Dayyani et al (2012) used the projection from the Canadian Regional Climate Model (CRCM) to evaluate the effects of climate change (under assumption of no changes in land cover and land management) on flows and nitrogen loads at the outlet of a 24.3 km 2 agricultural watershed in Quebec using the DRAIN-WARMF model. Bicknell et al., (2011) used a hydrological Simulation Program-FORTRAN (HSPF) in a Credit River Watershed in Ontario with two climate change scenarios. The results of their study indicated that the warmer future climate would result in significantly less snowpack accumulation and significantly reduced spring freshets. For both scenarios, the annual hydrograph with the climate change displayed much higher fall and winter streamflow (CVS, 2008). The impact of a future climate on the hydrology of the Grand River watershed was investigated using the two GCMs scenarios to adjust a historical (1961-1999) climate dataset to 2030s, 2060s and 2090s conditions (Belamy et al., 2002). The adjusted climate datasets were used as primary input into a continuous, physically based, hydrologic model, GAWSER (Guelph All-weather Sequential Events Runoff Model) (Schroeter&Associates, 2004). The results of this study showed that recharge and streamflow were largely dependent on the GCM used. While both scenarios resulted in increases in annual streamflow volume, summer stream flow decreased for GCM1 scenario and increased for the HadCM2 scenario. Also, due to the increase in precipitation, water budget parameters all experiences significant increase by the 2090s. Hydrological Models Currently available hydrologic models are developed for specific tasks. Hydrologic models are categorized as continuous or event simulation models (Singh, 1995). They can also be classified as distributed parameters or lumped parameter models. In scope, they range from small field size application models to large watershed models. Continuous simulation models are used to analyze the long-term effects of hydrological changes and agricultural management practices. Eventbased models are useful for analysis of storm events and may also be used to evaluate structural best management practices (Borah et al., 2003). In this project literature review of several widely used hydrologic models was completed. This included watershed scale models, such as Hydrological Simulation Program-Fortran or HSPF (Bicknell et al., 1996); the European Hydrological System Model or MIKE SHE (Refsgaard and Storm, 1995); Areal Non-point Source Watershed Environment Response Simulation or ANSWERS (Dillaha, 2001); Annualized Agricultural Non-Point Source model or AnnAGNPS (Bingner et al., 1998); Watershed Analysis Risk Management Framework or WARMF (Chen et al., 1998); The Agricultural Policy/environmental extender (APEX) model (Williams and Izaurralde, 2006; Williams et al., 2006; Williams et al., 2008; Gassman et al., 2009); and Soil and Water Assessment Tool or SWAT (Arnold et al., 1998). Watershed Scale Models AnnAGNPS The Annualized Agricultural Non-Point Source Model (Bingner et al, 1998) was developed at the USDA-ARS North Central Soil Conservation Research Laboratory in Morris, Minnesota. This model was developed to simulate surface runoff as well as sediment, nutrient and pesticide movement within an agricultural watershed. It can be used to simulate the impact on the 13

environment of nonpoint-source pollutants from predominantly agricultural watersheds. The runoff volume and rate are calculated using the SCS-Curve number and TR-55 methods, respectively, where the simulated direct runoff is due to storm events only. The input data are on a daily basis, while the model output is on an event, monthly, or annual basis (Young et al., 1995; Bosch et al., 2001). The major components of AnnAGNPS include hydrology, and transport of sediments, nutrients, and pesticides resulting from snowmelt, precipitation and irrigation. Kliment et al. (2008) compared AnnAGNPS with SWAT, and concluded that AnnAGNPS may not be suitable for base flow dominant watersheds. Yuan et al. (2001) and Suttles et al. (2003) reported that AnnAGNPS was able to adequately predict long-term monthly and annual runoff, but the model s overland flow did not properly represent the riparian areas and over-estimated the nutrient and sediment loads. Sarangi et al., (2007) used AnnAGNPS on a watershed on an island in the Caribbean. The model estimated runoff volume reasonably well for days with high precipitation, but was less accurate in estimating runoff for days with low precipitation amounts. Das et al. (2007) used AnnAGNPS for Canagagigue Creek watershed in the Grand River basin in southern Ontario, and obtained acceptable flow simulation. The major limitation of the AnnAGNPS model is that runoff and associated sediment, nutrient, and pesticide loads for a single day are routed to the watershed outlet before the next day s simulation. ANSWERS ANSWERS-2000 (Dillaha et al., 2001), the current version of the ANSWERS model (Areal Non-point Source Watershed Environment Response Simulation), was developed at Purdue University to study the impact of the management practices on sediment and nutrient transport. The hydrology component of ANSWERS-2000 model addresses interception, surface retention/detention, infiltration, evapotranspiration percolation and surface runoff (overland and channel flow). The ANSWERS model has been applied to different watersheds to assess surface runoff, nitrate pollution risks, and sediment loads. Connolly et al., (1997) reported that ANSWERS was able to accurately simulate different surface cover conditions; however, runoff prediction for low intensity rainfall events was less accurate than for high intensity events. Bai et al. (2004) applied ANSWERS-2000 to the Canagagigue Creek Watershed for assessment of runoff and sediment, and concluded that ANSWERS-2000 performed satisfactory for no-snow periods, but further improvements are needed for winter seasonal simulation. The model is limited to medium-size watersheds (500 to 3000 ha) where surface hydrologic processes dominate. Other limitations associated with the model are: the absence of proper fertilization inputs, poor snowmelt simulations and non-significant base flow simulations (Dillaha et al., 2001). MIKE SHE MIKE SHE (Refsgaard and Storm, 1995) is one of the few hydrologic models that were initially developed to integrate surface water and groundwater (DHI, 2004). MIKE SHE is a physically based, distributed, integrated hydrological and water quality modeling tool which consists of a water movement and several water quality modules. The water movement module of the model simulates the hydrological components including evapotranspiration, soil water movement, overland flow, channel flow, and groundwater flow. The water movement module uses a finite difference approach to solve the partial differential equations describing the processes of interception; evapotranspiration (Rutter model/penman- 14

Monteith Model or Kristensen-Jensen model); overland flow (two-dimensional, kinematic wave, Saint-Venant equation) and channel flow (one-dimensional, diffusive wave, Saint-Venant equation); flow in the saturated (two- or three- dimensional, Boussinesq equation) and unsaturated (one-dimensional, Richards equation) zones; and exchange between aquifers and rivers (DHI, 2004). MIKE SHE is capable of simulating flow and transport of solutes and sediments in both surface water and groundwater, and has both continuous long-term and single-event simulation capabilities. The model does not have limitations regarding watershed size. Watershed is horizontally divided into an orthogonal network of grid squares; hence, the spatial variability in parameters such as elevation, soil type, land cover, and precipitation, can be represented. Lateral flow between grid squares occurs as either overland flow or subsurface, saturated zone flow. The one-dimensional Richards equation employed for the unsaturated zone assumes that horizontal flow is negligible compared to vertical flow (Refsgaard et al., 1995). Oogathoo (2006) used MIKE SHE for runoff simulation at the Canagagigue Creek Watershed for different land management scenarios concluding that MIKE SHE could be used to simulate various management scenarios to solve hydrologic problems under the southern Ontario climatic conditions. The MIKE SHE model was used to evaluate a overland flow dominant watershed in northwestern China (Zhang, et al., 2008. Model calibration and validation suggested that the model could capture the dominant runoff process of the small watershed. It was also concluded that the model was useful for understanding the rainfall-runoff mechanisms. MIKE SHE model makes predictions that are distributed in space, with state variables that represent local averages of storage, flow depths or hydraulic potential. Because of the distributed nature of the model, the amount of input data required to run the model is rather large (Abu El-Nasr et al., 2005). The model assumes that flow in the unsaturated zone is one-dimensional and vertical. In addition, the codes are not available and this is the main limitation for its lack of common use. It also needs large amount of input data which are not available in most watersheds. HSPF The Hydrological Simulation Program-Fortran HSPF (Bicknell et al., 1996) is a continuous watershed simulation model that produces the time history of water quantity and quality at any point in a watershed (ASCE, 2007). It was specifically developed to evaluate the impact of land use changes on water, sediment and pollutant movement. This mathematical, continuoussimulation, lumped-catchments, conceptual model is used to simulate water movement as overland flow, interflow, and groundwater flow. The model employs hydrological response units (HRUs) based on uniform climate and storage capacity factors. The flow from each HRU is routed downstream using the storage routing kinematic wave method (Johnson et al., 2003). The model provides a water budget and considers snow accumulation and melt. Johnson et al. (2003) compared the performance of the HSPF and Soil Moisture Routing (SMR) models on a watershed in the United States and observed that both models simulate stream flow with almost equal accuracy. The HSPF model provided better simulation of winter stream flow than SMR. Albek et al. (2004) conducted a study on a Turkish watershed to examine the effect of land use and climate change on watershed response. Their results demonstrated that the model is in agreement with the observed data. Singh et al. (2005) evaluated HSPF and SWAT for stream flow simulation of the Iroquois River Watershed in east central Illinois and concluded that both the models provided accurate predictions of the daily, average monthly, and annual stream flows. The limitation of the HSPF model is that it is not fully distributed, and it lumps the watershed characteristics and climatic parameters into several units. Also, HSPF has many parameters to 15

calibrate and therefore, it is cumbersome to use. WARMF The Watershed Analysis Risk Management Framework (WARMF) (Chen et al., 1998) is classified as a watershed decision support system (DSS). It provides information and tools that facilitate collaborative decision making among interested stakeholders (EPRI, 2001). WARMF is a user-friendly tool, organized into five linked modules, with a GIS-based graphical user interface (GUI). It was developed under the sponsorship of the Electric Power Research Institute (EPRI) as a decision support system for watershed management (EPRI, 2001). The scientific basis of the model has undergone several peer reviews by independent experts under US EPA guidelines (EPRI, 2000). The model can simulate surface flow and also variables such as PH, temperature, dissolved oxygen, ammonia, nitrate, phosphate, suspended sediments, E.coli, major cations and anions, pesticides (up to three), and three algal types. The spatial distributions of point and non-point loadings can be displayed in a graphical manner. Furthermore, the water quality status of a river or lake in terms of its suitability for water supply, swimming, fish habitat, recreation or other uses (based on users or stakeholders' water quality criteria) can be presented (EPRI, 2000). WARMF has been applied to over 15 watersheds in the United States and internationally (Chen et al., 2001a; Weintraub et al., 2001b, 2004,; Herr et al., 2002; Keller et al., 2004; Geza and McCray, 2007; Rambow et al., 2008). The focus of these studies has varied from TMDL calculation (nutrients, sediment, fecal coliform, metals) to more research-oriented applications such as modeling the fate and transport of mercury in a watershed and the impact of onsite wastewater systems on a watershed scale. There is no limit on the size or scale of a potential WARMF application as long as adequate topography data are available (USEPA, 2009b). Although WARMF can simulate subsurface flow/chemical transport, tile drainage systems are not taken into consideration by the model. For example, if the moisture content of a soil layer is below field capacity, the hydraulic conductivity of the said layer is set at zero. Also if the soil moisture is at saturation, the infiltration rate is equal to the hydraulic conductivity. In between, WARMF interpolates the infiltration rate. In addition, the codes are not available and this is the main limitation that hinders its common use. APEX The Agricultural Policy/environmental extender (APEX) model (Williams and Izaurralde, 2006; Williams et al., 2006; Williams et al., 2008; Gassman et al., 2009) is a flexible and dynamic tool that is capable of simulating management and land use impacts for whole farms and small watersheds. APEX is essentially a multi-field version of the predecessor Environmental Policy Impact Climate (EPIC) model (Williams, 1995), which has been extensively tested and applied for a wide variety conditions in the U.S. and Canada (Gassman et al., 2005). APEX has been tested and applied at the field or watershed level in several different cropland, pasture, or forest based studies, primarily in the U.S., as chronicled by Gassman et al. (2010). Wang et al. (2006) also conducted an extensive sensitivity test of 15 APEX parameters for 159 sites representative of agricultural conditions across the U.S. However, ongoing testing of APEX is needed to further improve its accuracy and to expand the overall climatic, management, landscape, and vegetation conditions that the model can be applied to in both the U.S. and other regions (Gassman et al., 2010). In China, APEX erosion 16

tests were reported for the Loess Plateau region (Wang et al., 2006), which is characterized by much different conditions as compared to the Huaihe River Watershed. SWAT The Soil and Water Assessment Tool, SWAT, is a conceptual, physically-based, continuous simulation, watershed model, developed by Arnold et al., (1998); and improved by Arnold and Fohrer, (2005). The SWAT model operates on a daily time step. The objective in model development was to predict the impact of management practices on water, sediment and agricultural chemical yields in large un-gauged watersheds. SWAT requires specific information on weather, soil properties, topography, vegetation, ponds or reservoirs, groundwater, the main channel, and land management practices to simulate water quality and quantity (Neitsch et al., 2005). The model has eight major components: hydrology, weather, sedimentation, soil temperature, crop growth, nutrients, pesticides, and agricultural management. SWAT model allows a number of different physical processes to be simulated in a watershed. These processes will be briefly summarized in this section. For modeling purposes, a watershed may be divided into a number of subwatersheds or subbasins. The use of subbasins in a simulation is particularly important when different areas of the watershed are dominated by different land uses or soils. Input information for each subbasin are: climate; hydrologic response units or HRUs; pond/wetlands; groundwater; and the main channel or reach, draining the subbasin. Water balance is the driving force behind all the process in the watershed (Neitsch et al., 2009). Simulation of the hydrology of a watershed can be separated into two major parts: Landing phase and routing phase. The land phase of hydrologic cycle includes the amount of water, sediment, nutrient and pesticide loading to the main channel in each subbasin. The second phase which is the water or routing phase of the hydrologic cycle is the movement of water, and chemicals, etc. through the channel network of the watershed to the outlet of the watershed. Since the present study was modifying the SWAT model for subsurface drained watersheds, this model is described in greater detail in the following pages. Land Phase of the Hydrologic Cycle The hydrology model of SWAT is based on the water balance equation: t SW t = SW 0 + (R day Q surf E a W seep Q gw ) (1) t=1 Where SW t is the final soil water content (mm), SW 0 is the initial soil water content on day i (mm), t is time (days), R day is the amount of precipitation on day i (mm), Q surf is the amount of surface runoff in day i (mm), E a is the amount of evapotranspiration on day i (mm), W seep is the amount of water entering the vadose zone from the soil profile on day i (mm), and Q gw is the amount of return flow on day i (mm). Since the model maintains a continuous water balance, complex basins are subdivided to reflect differences in ET for various crops and soils. Thus, runoff is predicted separately for each HRU and routed to obtain the total runoff for the basins. This increases the accuracy and gives a better 17

physical description of the water balance. Climate Daily precipitation, maximum/minimum air temperature, solar radiation, wind speed, and relative humidity are the climatic variables required by SWAT model. These variables can be incorporated into input files from records of observed data from the climate stations. They also, can be generated during the simulation process. A simple, uniform snow cover model which has been updated to a more complex model allows non-uniform cover due to shading, topography and land cover (Neitsch et al., 2009) is used for snow cover component of the SWAT model (Neitsch et al., 2009). Snow melt is calculated by the air and snow pack temperature, the melting rate, and the areal coverage of snow. Snow is melted when the maximum temperature exceeds 0 o C using a linear function of the difference between the average snow pack maximum air temperature and the threshold temperature for snow melt. Surface Runoff and Infiltration Surface runoff or overland flow volume is computed either from the modified SCS curve number method (Soil Conservation Service, 1972) or the Green and Ampt infiltration method (Green and Ampt, 1911). Percolation A storage routing technique combined with a crack-flow model to predict flow through each layer is used to calculate the percolation component uses a (Neitsch et al., 2009). Once water percolates below the root zone, becomes return flow or appears as return flow in downstream basins, so it is lost from the watershed. When a lower layer exceeds field capacity then upward flow may occur. If the temperature in a particular layer is below 0 o C, no percolation is allowed from that layer (Neitsch et al., 2009). Evapotranspiration Evapotranspiration is referred to all processes by which water in the liquid or solid phase at or near the earth s surface become atmospheric water vapor. The SWAT model computes evaporation from soils and plants separately as described by Ritchie (1972). Potential soil water evaporation is estimated as a function of potential evapotranspiration and leaf area index. The model offers three options for estimating potential evapotranspiration: Hargreaves (Hargreaves et al., 1985), Priestley-Taylor (Priestley and Taylor, 1972) and Penman-Monteith (Montheith, 1965). The Actual soil evaporation is estimated by exponential functions of soil depth and water content. Plant transpiration is simulated as a linear function of potential evapotranspiration and leaf area index. 18

Figure 1. Schematic pathways available for water movement in SWAT (adopted from Neitsch et al., 2005) 19

Routing Phase of the Hydrologic Cycle SWAT determines the loadings of water, sediment, nutrients and pesticides to the main channel (Neitsch et al., 2009), and then, the loadings are routed through the stream network of the watershed using a command structure similar to that of HYMO (Williams and Hann, 1972). In addition to keeping track of mass flow in the channel, SWAT simulates the transformation of chemicals in the stream and streambed (Neitsch et al., 2009). Due to evaporation and transmission through the bed of the channel, a portion of water may be lost as it flows downstream. Another potential loss of water is its removal of from the channel for agricultural or human use. Flow may be supplemented by the rainfall directly on the channel and/or the addition of water from point source discharges. Flow routing through the channel can be calculated using a variable storage coefficient method developed by Williams (1969) or the Muskingum routing method. Geographic Information System (GIS) and other interface tools to support the input (topographic, land use, soil, and other digital data) into SWAT are important trends that have paralleled the historical development of SWAT. Various statistical indices have been used to evaluate SWAT hydrologic simulations. Moriasi et al., (2007b) provided guidelines for statistical evaluation methods. It has been recommended that three quantitative statistical parameters, which is Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the root mean square error to the standard deviation of measured data (RSR), in addition to the graphical techniques, be used in model evaluation. In general, model simulation can be judged as satisfactory if NSE > 0.50 and RSR 0.70, and if PBIAS ± 25% for streamflow, PBIAS ± 55% for sediment, and PBIAS 70% for N and P. Bingner (1996) applied SWAT to the Goodwin Creek Watershed in northern Mississippi and reported an NSE value of 0.80 for monthly stream flow. SWAT was also successfully validated for streamflow for the Mill Creek Watershed in Texas (Srinivasan et al., 1998). Monthly streamflows were well simulated (NSE = 0.77 and R 2 = 0.87 for calibration period; NSE = 0.52 and R 2 = 0.65 for validation period) but the model over-estimated streamflows in a few years during the spring/summer months. The over-estimation may be accounted for by variable rainfall during those months. Arnold et al. (2000) applied SWAT for regional estimation of base flow and groundwater recharge in the Upper Mississippi River Basin. The report revealed a general tendency for SWAT to under-estimate spring peaks and to over-estimate fall monthly stream flow. Annual simulated base flow suggested that SWAT tends to over-estimate base flow in high runoff regions with deep soils. Still the Nash-Sutcliffe coefficient (NSE) value of 0.65 was reported for monthly stream flow simulations during the validation period (Arnold et al., 2000). Spruill et al. (2000) used the SWAT model to simulate daily stream flow in a small central Kentucky watershed for a two-year period. Results showed that SWAT adequately predicted the trends in daily stream flow. Eckhardt and Arnold (2001) used a stochastic global optimization algorithm to perform the automatic calibration of SWAT simulation on a low mountain range catchment in central Germany. Results showed a good correlation between measured and simulated daily stream flow with E of 0.70 and a correlation coefficient of 0.84. They concluded that the mean annual stream flow was under-estimated by 4%. Van Liew et al., (2003) evaluated SWAT s ability to predict streamflow under varying climatic conditions for three nested watersheds in the 610 km2 Little Washita River Watershed in south western Oklahoma. They found that SWAT could adequately simulate runoff for dry, average and wet climatic conditions in one sub-watershed, following calibration for relatively wet years in two of the sub-watersheds. 20

Field scale Models RZWQM Root Zone Water Quality Model (RZWQM) (Ahuja, et al., 2000) is a comprehensive simulation model designed to predict the hydrologic response, including surface and groundwater contamination of alternative crop-management systems. The RZWQM simulates the major physical, chemical and biological processes in an agricultural crop production system. It is a onedimensional (vertical in the soil profile) process-based model that simulates the growth of plants and the movement of water, nutrients, and pesticides over a range of common management practices. The model includes the simulation of a tile drainage system. RZWQM consists of six major scientific sub-modules or processes that define the simulation program, a Numerical Grid Generator, and an Output Report Generator (Ahuja, et al., 1999). The first version of RZWQM was released in 1992 and was adopted as the model for the Management System Evaluation Areas (MSEA) project (Watts et al., 1999). In 2007, an updated version of RZWQM was released as RZWQM2, which contains surface energy balance from the SHAW (Simultaneous Heat and Water) model (Flerchinger and Saxton, 1989; Flerchinger and Pierson, 1991) and the crop growth modules from Decision Support System for Agro-technology Transfer (DSSAT) (Jones et al., 2003). Singh et al. (2001) compared the RZWQM and DRAINMOD for annual NO 3 -N losses to tile outflows, and found that both models have the capability to simulate the effect of crop rotation under different climatic conditions. Results indicated that both models simulated tile flow within an acceptable range. However, DRAINMOD simulated results were closer to the actual observed values. Detailed results are presented in this paper. The limitations of this model are that the crops parameterizations are limited to corn, soybean, and wheat. Frozen soil dynamics are not considered. Rainfall is entered as break point increments, and fairly detailed descriptions of the soil profile and initial state have to be known to give a good simulation response to the system. In general, RZWQM is a complex model and needs data which are not normally available. Overland flow and sediment routing are not available in the currently released version, but are available in a test version. Pesticide uptake by plants is not simulated in the currently released version, but is available in a test version. SWAP Soil-Water-Atmosphere-Plant, or SWAP, model (Van Dam et al., 1997) is the successor of the agro hydrological model SWATR (Feddes et al., 1978) and some of its numerous derivatives. SWAP simulates the transport of water, solutes and heat in unsaturated/saturated soils. The model is designed to simulate flow and transport processes at the field scale during seasons and for long term time series. The model employs Richard s equation and includes root water extraction to simulate soil moisture movement in variably saturated zones (Kroes et al., 2008). The SWAP model has been applied to compute the effects of land drainage (12 combinations of drain depth and spacing) on soil moisture conditions in the root zone and their effect on crop yield and soil salinization in Pakistan, (Sarwar and Feddes, 2000). The optimum drain depth for the multiple cropping systems of the FDP-area was found to be 2.2 m. The main limitation of the SWAP model is that, at low values of saturated hydraulic conductivity, the model did not succeed in completing the simulations. DRAINMOD DRAINMOD (Skaggs, 1980) is a field-scale, hydrologic model was developed to describe the hydrology of poorly, or artificially, drained lands. DRAINMOD is the hydrology component of the soil carbon and nitrogen dynamics model DRAINMOD-N II (Youssef et al., 2005), the soil salinity model DRAINMOD-S (Kandil et al., 1995), the recent whole-system models 21

DRAINMOD-Forest (Tian et al., 2012), and DRAINMOD-DSSAT (Negm, 2011), which simulates the hydrology, biogeochemistry, and plant growth for drained forested and agricultural lands. This model is described in the following paragraphs. DRAINMOD is a computer simulation program that characterizes the response of the soil water regime to various combinations of the surface and subsurface water management. DRAINMOD simulates the response of the water table and the soil water above the water table to the other hydrologic components, such as infiltration and evapotranspiration (ET), as well as to surface and subsurface drainage.. Surface irrigation can also be considered. Climatological data are used in the model to simulate the performance of a given water management system across several years. The rates of infiltration, ET, drainage, and distribution of soil water in the profile are calculated by various methods, (Skaggs, 1980). The Green and Ampt (Green and Ampt, 1911) equation is used to describe the infiltration component in DRAINMOD. The model calculates daily potential ET using the Thornthwaite method, although ET can be computed by the method of the user s choice (e.g., Penman Monteith or Hargreaves). Surface runoff is characterized by the average depth of surface depression storage and begins when surface depressions are filled out (Skaggs, 1999). The Hooghoudt s steady state equation, with a correction for convergence near the drains (Schilfgaarde, 1974), is used to calculate drain outflow, according to the Dupuit Forchheimer (D F) assumptions and flow is considered in the saturated zone only. The model also calculates the subsurface drainage flux from a pond surface using Kirkham s steady state flow equation. Deep seepage rates are calculated with an application of Darcy's Law. Approximate methods were used to characterize the water movement processes in DRAINMOD. A summary outputs are available on a daily, monthly, yearly, and ranked bases, at the option of the user (Skaggs et al., 2012). Water Balance DRAINMOD model is based on water balance for a section of soil of unit surface area that extends from the impermeable layer to the ground surface which is located midway between parallel drains (Skaggs, 1980). The water balance for a time increment may be expressed as: Va a = D + ET + DS F (2) Where V a is the change in the air volume (cm), D is the lateral drainage (cm) from (or subirrigation) ET is the evapotranspiration (cm), DS is the deep seepage (cm), and F is the infiltration (cm) during the time increment t. The water table depth is actually relatively flat in the broad center portion of the field, even during wet periods. During dry periods, when the water table is close to, or below the drain, it becomes essentially flat (Skaggs, 1980). Thus, equation 2.14 approximates a relatively wide area in the center of the field. The water balance can be conducted for the cross-section, from drain to drain, by expressing the drainage and seepage rates in terms of the average water table depth, rather than the depth at the midpoint (McCarthy et al., 1992), but the standard version conducts the water balance at the midpoint between drains. A water balance is also computed at the soil surface for each time increment t and may be written as: P = F + S RO (3) Where P is precipitation (cm), S is change in the volume of water stored on the surface (cm), 22

and RO is runoff (cm) during t. Evapotranspiration The determination of the ET rate is a two-step process in DRAINMOD. First, the daily potential ET (PET) is determined and distributed on an hourly basis. After PET is calculated, it is been checked if ET is limited by soil water availability. If ET is not limited by available soil water, it is set equal to the PET; otherwise, ET is set to the smaller amount that can be supplied from the soil system. Daily PET may be determined by the method of the user s choice or it can be read into the model as input an input file. PET in DRAINMOD may be calculated by Thornthwaite (1948) method. Inputs used to determine whether soil water conditions limit ET are the soil water characteristic, the relationship of maximum steady upward flux and water table depth, effective depth of the root zone, and soil water content at the lower limit (Skaggs, 1980). Soil Temperature, Freezing, Thawing and Snowmelt The DRAINMOD model includes freezing, thawing, and snowmelt components, which allows, DRAINMOD to simulate the drainage phenomena in cold regions. DRAINMOD solves the water flow and heat flow equations simultaneously based on the principles of mass and energy conservation. It uses soil temperature to simulate processes controlling field hydrology under cold conditions such as freezing, thawing, and snowmelt (Luo et al., 2000). During the freezing conditions, the model modifies soil properties, infiltration and drainage rates according to the ice content in the profile and (Skaggs et al, 2012). Over the past three decades, DRAINMOD has been extensively tested for a wide range of soils, crops, and climatological conditions and proven to be a reliable model for simulating water table fluctuations and drainage volumes in artificially drained, high water table soils (Skaggs, 1982; Gayle et al., 1985; Fouss et al., 1987; Sanoja et al., 1990; Cox et al., 1994; Singh et al., 1994; Madramootoo et al., 1999; Luo et al., 2000; Luo et al., 2001; Helwig et al., 2002; Zwierschke et al., 2002; Youssef et al., 2003; Youssef et al., 2006). MODEL SELECTION A clear understanding of the capability of the available model is important for appropriate of the model and to avoid any misuse (Borah and Bera, 2003). In order to select the most appropriate model to address the requirements of the study, it is important to have a clear understanding of the objectives of the study, the capabilities, strength and limitations of the available model, conditions under which it performs well and the conditions under which it will be used, degree of accuracy, and data requirements and data availability (Parsons et al., 2004). It is also essential to pay attention to the needs of the water resource project before developing, choosing, or applying a model (Parsons et al., 2004). Based on the detailed literature review, it appears that between all the watershed scale models SWAT is a good candidate as an analysis tool for this research because of the continuous development of its user-friendly interface from GIS, complete documentation support on its theoretical interpretation, a user manual and tutorial explanations, and even open source code is available with a free download, physically based analysis functions, flexible input modifications and extensive applications around the world. Moreover, it can be applied to small and large watersheds. DRAINMOD (Skaggs 1980) was the first comprehensive computer model developed to aid in the design and evaluation of agricultural drainage and water table 23

management systems for poorly drained soils with high water table. The model includes freezing, thawing, and snowmelt components and thus, it is capable of simulating drainage phenomenon in cold regions. Agricultural tile drainage is a common water management practice in agricultural regions such as southern Ontario and coastal regions with high water tables. Artificial tile drainage systems are installed in many agricultural fields in humid regions, such as eastern Canada, for crop production. Ontario has a cool and wet spring and fall seasons, and a cold winter, and thus experiences freezing, thawing, and snowmelt. DRAINMOD has been used and tested worldwide and it is proven to be an efficient model in simulating flows from poorly drained high water table soils experiencing freeze-thaw cycles (Skaggs, 1982; Singh et al., 1994; Lou et al., 2000, 2001; Youssef et al., 2006; Yang et al., 2007; Wang et al., 2006; Dayyani et al., 2009, 2010a; Skaggs, 2012). In this respect, DRAINMOD appears to be a good candidate for subsurface flow simulation. Therefore, in terms of field scales models DRAINMOD was selected for this project. SELECTION OF WATERSHED AND FIELD In consultation with Dr. Pradeep Goel from the Ontario Ministry of the Environment and Climate Change two watersheds, one (Canagagigue Creek Watershed) in the Grand River basin was selected to evaluate the impact of climate change on quantity and quality of water resources. The Canagagigue Creek Watershed was extensively studied during PLUARG (Pollution due to Land use Activities Reference Group) period and extensive data are available for the calibration and validation of model before used to evaluate the impact of climate change on water resources. A 14 ha field at the Green Belt farm Agriculture and Agri-Food Canada in Ottawa, Ontario was selected to assess the impact of climate change on hydrology and water quality at the field scale. Canagagigue Creek Watershed With almost 7000 km 2 in drainage area, southwestern Ontario s Grand River basin contributes about 10% to the water received by Lake Erie. A upland tributary of the Grand River, the Canagagigue Creek has a drainage area that extends over 143 km 2 (43.60 43.70 N, 80.55 80.63 W) and covers the Peel and Pilkington townships of Wellington County and Woolwich Township of Waterloo County, ON (Figure 2). Climatic conditions vary across the Grand River Watershed, covering four different climatic zones. The mean annual precipitation ranges from 750-1000 mm, including 100-200 mm of snowfall (Das et al., 2004). July and August are the warmest months of the year. Most of the evapotranspiration occurs during summer months, and represents 65% of the annual precipitation. The months of January and February are the coldest and driest months of the year. The outlet of entire watershed is close to the town of Floradale (Figure 2). In addition, there are two flow and sediment measurement stations located within the studied watershed on the west and east side of the watershed. The area of the west is 18 km 2 and the major landuse is agriculture. The area of the east watershed is 28 km 2 with wide the major landuse of forest (Figure 2). 24

GA 036 (West) GA 035 (East) Floradale Figure 2. Location of the Canagagigue Creek Watershed in the Grand River Basin Figure 3. Soil map of Canagagigue Creek Watershed in Grand River Basin 25

Figure 4. Land use map of Canagagigue Creek Watershed in Grand River Basin. The land use map in the watershed is shown in Figure 4. Corn and soybean are the most common land use covering 24 and 38% of the watershed area. Other row crops cover 4% of the area, forest 11%, hay and pasture 10% and urban area less than 1%. Winter wheat is cultivated in about 14% of the watershed, the most of the row crop system is corn and soybean, the hay and pasture system are alfalfa. The woodlot is mostly boreal forest. The mixed system includes crop rotation: two years grain/silage corn, one year soybean, two years alfalfa forage, then back to corn. The distribution of different land uses with respect to area is shown in Table 1. Table 1. Area covered by various land uses in the KettleCreek Watershed Land use Area (%) Corn 23.71 Soybean 38.01 Agricultural row crops 3.87 Forest 11.04 Hay 7.18 Pasture 0.86 Idle agricultural lands 0.1 Water 0.06 Urban area 0.25 Winter wheat 13.77 Bermuda grass 1.13 26

The soil classification across the watershed was defined by polygon shape files, provided by the Ontario Ministry of Agriculture and Food (Figure 3). Soil surveys of Waterloo County (Presant and Wicklund, 1971) and Wellington County (Hoffman and Mathews, 1963) indicate that most of the watershed bears 0.2 to 0.6 m of loam or silty loam of the Huron and Harriston series over a loam till. In the northern part of the watershed, clay loam is predominant, while loam is the main soil type in the central portion of the watershed. In the south and southeastern sections, the soil types can be characterized as moraine deposits of very fine sand and fine sandy loam, with occasional layers of other material. The percentage distribution of soil by soil type is given in Table 2. Table 2. Distribution of soil in the Canagagigue Creek Watershed Soil Type Texture Area (%) Burford loam/ loam gravel/ sand clay gravel/ sand gravel 30.37 Brady sandy loam/ sandy loam/ sand 10.08 Brookstone loam/clay loam/clay loam 8.09 Colwood loam fins sand/ Loam fins sand/ sandy clay loam 8.79 Conestogo silt loam/ loam/ silt loam/ loam 8.37 Berrin sandy loam/ loamy sand/ sandy loam/ clay/ clay 3.74 Birsbane loam/loam/loamy gravel/gravel 0.16 Caledone sandy loam/ sandy loam/ sandy loam/ sandy loam/ sand/ loamy sand 8.56 Donnybrook sandy loam/ sandy loam/ loamy sand/ sandy loam/ gravel 4.61 Elmira silt loam/ loam/ loam/ loam 5.04 Floradle silt loam/ silt loam/ loam/ loamy sand/ loam 1.04 Freeport fine sandy loam/ sandy loam/ sandy loam/ sandy loam/ loam 1.65 Fox loamy sand/ loamy sand/ loamy sand/ loamy sand 5.15 Granby sandy loam/ Sandy loam/ loamy sand/ sand 1.10 Grand loam/ loam/ loam/ loam/ sandy loam 0.65 Guelph silt loam/ loam/ clay loam/ loam 2.00 Heidelburg very fine sandy loam/ fine sandy loam/ loamy sand/ fine sandy loam/ fine sandy 0.13 loam/ fine sandy loam Harriston loam/ loam/ loam/ loam/ loam 0.07 27

Figure 5. Topography map of Canagagigue Creek watershed. The topographic map of the study watershed shown in Figure 5 was obtained from the Grand River Conservation Authority as a digital elevation model (DEM) with a 10 m x 10 m spatial resolution. The DEM was converted into a point shape file to be imported into the model. Climatic data The climate data used in this project were provided by CCDP (Climate Change Data Prortal) of Regina University. The climatic parameters which are used in this project to run the SWAT model are: precipitation, min/max temperature, wind speed, relative humidity, all provided by CCDP for the period of 1960-1990 and 2015-2095. The available climate modeling data are based on the PRECIS climate modeling system (Wang and Gordon, 2013). The climate data required for modeling by SWAT model is available for both Kettle Creek and Canagagigue Creek Watershed for the period 2015-2095 (Wang and Gordon, 2013). The dataset used in this project were provided by CCDP (Climate Change Data Prortal) of Regina University which were derived from the high resolution (25km 25km) climate projections developed by the IEESC at University of Regina using the PRECIS modeling system. There are 17 sets of boundary data from a perturbed physics ensemble (QUMP) which is based on Hadley centre s HadCM3 model under SRES A1B emissions for use with PRECIS in order to allow users to generate an ensemble of high resolution regional simulations (McSweeny and Jones 2010, McSweeny et al., 2012). In current study all climate projections are derived from a 5-member PRECIS ensemble 28

generated by Wang et al., (2012, 2014) at University of Regina. The PRECIS ensemble were ran at 25 km x 25 km resolution and driven by different boundary conditions (i.e. HadCM3Q0, Q3, Q10, Q13, and Q15) from the QUMP ensemble developed by the Hadley Centre. PRECIS model outputs were extracted and derived into four 31 year periods, one baseline period (1960-1990), and three future periods (2015-2045, 2035-2065, and 2065-2095), representing the simulations for province of Ontario under current and future climate forcings. SWAT HYDROLOGIC SIMULATION The impact of climate-change on hydrology of the Canagagigue Creek watershed was evaluated by running the SWAT model using climate data from 1965 to 2095. SWAT model was calibrated and validated for 1990-1998 at the same watershed. Figure 6 shows the scatter plot of measured and predicted monthly precipitation values for a part of the historical period (1960-1990). The observed data are taken from the Fergus station close to the study area. A coefficient of determination of 0.83 indicates a good correspondence between predicted and observed precipitation values. Figure 6. Scatter plot of measured and PRECIS-predicted precipitation values for 1960-1990 Evaluation of SWAT Model Performance The evaluation, calibrate and validate, of a hydrological model requires some output response. In this study, the streamflow data measured at the outlet of the watershed was used to assess the model performance. The performance assessment was based on the water balance closure of the watershed, the agreement of the overall shape of the time series of streamflow together, and the value of the statistical performance indices, such as the root mean square error (RMSE), the Nash-Sutcliffe modeling efficiency (NSE) and the goodness of fit (R 2 ) (Nash and Sutcliffe, 1970; Legate and Mc Cabe, 1999; Singh et al., 2004). The RMSE (Equation (1)) indicates a perfect match between observed and predicted values when it equals 0.0 (zero), with increasing RMSE values indicating an increasingly poor match. Singh et al. (1999) stated that RMSE values less than half the standard deviation of the observed (measured) data might be considered low and indicative of a good model prediction. The Nash Sutcliffe efficiency coefficient (NSE) ranges between and 1. It indicates a perfect match 29

between observed and predicted values when NSE = 1 (Equation (2)). Values between 0.0 and 1.0 are generally viewed as acceptable levels of performance, whereas values less than 0.0 indicate that the mean observed value is better than the simulated value, which indicates unacceptable performance. The coefficient of determination, R 2, (Equation (3)), which ranges between 0 to 1, describes the proportion of the variance in the measured data, which is explained by the model, with higher values indicating less error variance. Typically, R 2 > 0.5 is considered acceptable (Santhi et al., 2001;Van Liew et al., 2003). The percentage of bias (PBIAS) measures the average tendency of the simulated data to be larger or smaller than their observed counterparts (Gupta et al., 1999). The optimal value of PBIAS is 0.0, with low magnitude values indicating an accurate model simulation. Positive values indicate under-estimation bias, and negative values indicate over-estimation bias (Gupta et al., 1999). The RMSE-observations standard deviation ratio (RSR) is calculated as the ratio of the RMSE and standard deviation of measured data. RSR varies from the optimal value of 0, to a large positive value. The lower RSR, the lower the RMSE and the better the model simulation performance. RMSE n i 1 P O i n i 2 (4) 2 n O i O P i 1 i Oi n 2 O i O n 2 i 1 NSE (5) i 1 R 2 n O O P P i 1 i i n 2 n 2 O i O Pi P i 1 i 1 0 R 2 1 (6) PBIAS n i 1 Oi Pi *100 n O i 1 i n 2 RMSE O P i 1 i i RSR STDEV n 2 obs O i 1 i O (7) (8) Where, n is the number of observations in the period under consideration, O i is the i th observed value, O is the mean observed value, P i is the i th model-predicted value and P is the mean modelpredicted value. SWAT Model Calibration and Validation In order to evaluate the SWAT model, the first year of the data (1989) was used to initialize the model, and the following two groups of four years of data were used to validate and calibrate the model, respectively. Calibration of SWAT was performed in two steps by first calibrating the average annual water budget and then the calibration of the hydrograph shapes for the daily 30

streamflow. Calibration was performed in a logical order according to the most sensitive parameters obtained from sensitivity analysis. To obtain more realistic and physically meaningful results, the observed total flow was separated into two components, surface runoff and baseflow, using digital filter baseflow separation technique (Oogathoo, 2006; Rong, 2009). The Base Flow Index (BFI) is defined as the ratio of the baseflow to the total water yield (stream flow). The annual baseflow index for this watershed was estimated to be 40%. Surface runoff was calibrated by adjusting the curve numbers for the different soils in the watershed under the conditions prevailing in the region and, then, using the soil available water (SAW) and soil evaporation compensation factor. In the next step, the baseflow component was calibrated by changing the revap coefficient (water in shallow aquifer returning to root zone), which controls the water movement from shallow aquifer into the unsaturated zone. The temporal flow was then calibrated by changing the transmission losses for the channel hydraulic conductivity and the baseflow alpha factor, which is a direct index of groundwater flow response to changes in recharge (Smedema and Rycroft, 1982). Since the Canagagigue Creek Watershed is subject to significant snowfall and snowmelt during the winter and early spring period, some parameters related to the snow water mass balance were investigated with regard to their sensitivity to surface runoff, base flow, actual evapotranspiration and streamflow, through a review of the pertinent literature. For SWAT, these parameters were ESCO (soil evaporation compensation factor), SMTMP (snow fall temperature), TIMP (snow pack temperature lag factor), SMFMN (melt factor for snow on 21 December) and SMFMX (melt factor for snow on 21 June). The range of values for calibration of the SWAT model is listed in Table 3. All calibration steps applied to the SWAT model were in line with the recommended calibration steps listed in the SWAT User Manual 2000 (Neitsch et al., 2000). Table 3. Calibrated values of the adjusted parameters for streamflow calibration of the Soil and Water Assessment Tool (SWAT) model for the Canagagigue Creek Watershed. Default Calibrated Description Values Value Soil evaporation compensation factor 0.01 1.00 1.00 Initial soil water storage expressed as a fraction of FC water content 0.01 1.00 0.95 Snowfall temperature ( C) 1.00-2.00 Melt factor for sow on June 21 (mm H 2 O/ C-day) 4.5 6.90 Melt factor for snow on December 21(mm H 2 O/ C-day) 4.5 1.40 Snow pack temperature lag factor 1.0 0.20 Minimum snow water content that corresponds to 100% snow cover 1.00 10.00 Snowmelt base temperature ( C) 0.5 0.00 Surface runoff lag coefficient (d) 0.00 4.00 0.20 Curve number coefficient 0.00 2.00 1.5 Manning s n value for overland flow 0.014 0.15, 0.5 Manning s n value for main channel 0.014 0.014 Surface runoff/infiltration approach Curve Number Evapotranspiration approach Penman Monteith 31

Results and Discussions The scatter plots of the observed and simulated monthly discharges (mm) for the calibration and the validation periods are shown in Figure 7. On the basis of the visual analysis of the observed and predicted runoff, the overall simulation appears to be reasonably good. Observed and simulated daily and monthly average streamflow for the calibration and validation periods are presented in Figure 8 and 9. Figure 7. Scatter plot of measured and predicted streamflow Figure 8. Observed and simulated monthly streamflow during calibration and validation period 32

The quantitative performance of the models to simulate stream flow for the calibration and validation was further examined using statistical criteria. The statistical indices for the comparison of monthly and daily observed and simulated flows are presented in Tables 4. These statistics indices indicate that the model performed well. For better performance of the values of RMSE should be close to zero, R 2 and EF close to unity and PBIAS and RSR should have small values. According to Moriasi et al. (2007b), a model is considered calibrated for flow if monthly NSE 0.65, PBIAS ±10% and RSR 0.60. The statistical coefficients (Tables 4) show that the model performed slightly better in the calibration period than in the validation period. Based on RMSE and R 2 values, the model performed better for monthly simulation than daily simulation. Figure 9. Observed and simulated daily streamflow during the calibration and validation period In the next step, SWAT model was calibrated and validated at two different West and East branches of the watershed separately (Figure 2). The statistical indices for the comparison of monthly and daily observed and simulated flows at two eastern and western parts of the watershed are presented in Tables 5 and 6. These statistics indices indicate that the model performed well at both watersheds. The statistical coefficients (Tables 4) show that the model performed better in the west part. 33

Table 4. Monthly calibration and validation statistics at Canagagigue Creek Watershed. RSR, RMSE-observations standard deviation ratio; NSE, Nash Sutcliffe efficiency; PBIAS, percentage of bias. Statistical Monthly Daily Index Calibration Validation Calibration Validation R 2 0.74 0.64 0.57 0.41 RMSE 9.89 12.04 1.03 2.00 RSR 0.27 0.54 0.47 0.61 NSE 0.74 0.74 0.53 0.59 PBIAS -3.14-12.50-1.52-7.8 Table 5. Monthly and daily calibration and validation statistics at Canagagigue West. RSR, RMSE-observations standard deviation ratio; NSE, Nash Sutcliffe efficiency; PBIAS, percentage of bias. Statistical Monthly Daily Index Calibration Validation Calibration Validation R 2 0.85 0.72 0.60 0.61 RMSE 11.00 15.04 2.03 3.00 RSR 0.37 0.44 0.44 0.49 NSE 0.79 0.77 0.58 0.57 PBIAS -4.14-10.50-1.52-12.8 Table 6. Monthly and daily calibration and validation statistics at Canagagigue East. RSR, RMSE-observations standard deviation ratio; NSE, Nash Sutcliffe efficiency; PBIAS, percentage of bias. Statistical Monthly Daily Index Calibration Validation Calibration Validation R 2 0.74 0.60 0.57 0.52 RMSE 14.44 13.80 1.03 2.00 RSR 0.36 0.24 0.47 0.61 NSE 0.53 0.51 0.51 0.43 PBIAS -5.14-14.50-21.53-17.5 Climate Change Scenarios The climate dataset used in this project were provided by CCDP (Climate Change Data Portal) of Regina University which were derived from the high resolution (25km 25km) climate projections developed by the IEESC at University of Regina using the PRECIS modeling system (Wang and Gordon, 2013). The climatic parameters used in this project to run the SWAT model include precipitation, min/max temperature, wind speed, relative humidity, all provided by CCDP for the period of 1960-1990 and 2015-2095. The climate data required for modeling by SWAT model is available for the period 2015-2095 (Wang and Gordon, 2013). In order to assess the impacts of climate change, all the simulations were performed under three different percentiles of 70%, 80% and 90% of PRECIS projections. The results of percentile 80% are discussed in this report and the results of percentiles 70% and 90% are presented in the Appendix. 34

The impact of climate change scenario was evaluated and analyzed first for the Canagagigue Creek watershed. Next, to assess the effect of climate change on water balance, hydrology and water quality of smaller scale the whole watershed (Canagagigue Creek) divided into two subwatersheds, east and west branch), and climate change scenario was implemented on these two sub-watersheds separately. PRECIS climate change projections indicate an increase in the average annual evapotranspiration and temperature and precipitation (Figure 14) for Canagagigue Watershed. In this study the, all the simulations over the historical period were performed using the predicted data by the PRECIS climate change scenario, since measured data were not available for all the climatic parameters required by the model. Figure 10. Historical (1960-1990) and future (2015-2095) predicted annual and seasonal average precipitation, evapotranspiration and max/min temperature The climate change impact on hydrology of Canagagigue Creek watershed at Floradale, the outlet of watershed, was examined by running the SWAT model using climate data from 1960-1990 and 2015-2095. Climate Change Simulations in Canagagigue Creek Watershed (Floradale) Water Balance Analysis Water balance components (precipitation, evapotranspiration and streamflow) over 30-year historical period (1960-1990) and 80-year future period (2015-2095) are presented in Figure 11. As can be seen from these data all the components present an increasing rate. 35

Figure 11. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2095 periods. Figure 12 presents the changes of the water balance components during the historical period (1960-1990), and for the future, early (2015-2044), mid (2045-2074) and late (2075-2095) of the part of thecentury. Figure 12. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. 36

The the comparison of the 2075-2095 and 1960-1990 results show that the precipitaion, streamflow and ET will increase by 41%, 27% and 12%, respectively. The data given in Figures 11 and 12 show a wetter and warmer climate for the future, higher temperature, higher amount of ET and streamflows. The model simulation results demostrate that for the driest year (2022) annual precipitation during the future period (2015-2095) will increase by 9% compared to the driest year (1966) over the historical period (1960-1990). While the annual flow will increase by 18%. Figure 13 shows an inceasing annual trend of the precipitation and simulated flow for both the historical and future data. The increasing trend of annual precipitation and stream flow are significant over the future period and non-significant over the historical periods (Figure 13). Figure 13. Annual comparison of the historical (1960-1990) and future ( 2015-2095) predicted streamflow and precipitation. Figure 14. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average total streamflows. 37

Figure 14. presents the annual and seasonal averages of streamflow for the historical and three different future periods. Figure 15 presents the monthly averages of streamflow for the historical and three different future periods. The results of model simulations show increase in annual flow mainly due to the increase during spring season, the months of March, April and May (Figure 14 and 15). Figure 15. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflows. The increase of the flow during the winter is mainly during the month of March. The min/max temperatures are increasing in winter causing an increase in rainfall, and decrease in snowfall. During spring months of April and May, the slight increase in precipitation results significant increase in stream flow. An increase of flow during March and April, could be due to earlier and more snowmelt in future compared to historical period, which causes an increase in flow during the moths of March and April. The increase in flow during the month of May can be due to more rainfall in spring season compared to historical period. The increase in min/max temperatures in winter leads to more rainfall-dominated regime and less snow accumulation. This might lengthen the growing season. Therefore, the climate change seems to alter both magnitude and seasonality of flow. In general climate change effects more spring and winter hydrology than summer hydrology. Sediment Load Simulations The impact of changed hydrology on sediment loads in the Canagagigue Creek watershed was assessed using the SWAT model. The annual, seasonal and monthly results of the SWAT model simulations are presented in Figures 15 to 17. The analysis of the data in given in Figure 15 using moving average approach show that the annual increasing trend of sediment load for both historical and future data. 38

Figure 15. Annual comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load The model simulation results of flow show that annual streamflow is expected to increase for the future data.as a result annual sediment load also will be increased in future (Figure 15 and 16). Figure 16. Seasonal comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. The annual sediment loads increased mainly during the spring season (Figure 17) which is mainly due to increased streamflow as a result of snowmelt and more precipitation respectively. Figure 17 presents the monthly sediment loads simulated during historical (1960-1990) and future (2015-2095) period. The monthly comparison of historical and future simulation results show that the sediment load increased during the months of March, April and May in future compared to historical period, which is due to increased simulated flow during these in months. 39

Figure 17. Monthly comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. Climate Change Simulations in Canagagigue West Watershed (GA036) The climate change impact on hydrology of Canagagigue Creek West watershed at GA036 station was examined by running the SWAT model using climate data from 1960-1990 and 2015-2095 and the results are presented in Figures 18 through 25. Water Balance Analysis Water balance components (precipitation, evapotranspiration and streamflow) over 30-year historical period (1960-1990) and 80-year future period (2015-2095) are presented in Figure 18. Figure 18. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2095 periods. Figure 19 presents the changes of the water balance components during the historical period (1960-1990), and for the future, early (2015-2044), mid (2045-2074) and late (2075-2095) of the part of the century. The comparison of the 2075-2095 and 1960-1990 results show that the 40

2015 2021 2027 2033 2039 2045 2051 2057 2063 2069 2075 2081 2087 2093 Annual Flow (mm) Precipitation (cm) precipitaion, streamflow and ET will increase by 41%, 7% and 32%, respectively. 22.5 1960-1990 2015-2044 20.7 58.9 119.4 63.2 125.6 37.9 41.7 2045-2074 2075-2095 61.9 24.7 132.2 62.5 31.2 142.6 45.6 48.9 Precipitation (cm) ET (cm) Surface Flow (cm) Chang in Storage (cm) Figure 19. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. Figure 20 shows the annual trend of the precipitation and simulated flow for both the historical and future data. The annual precipitation showed an increasing trend while no trend can be observed for the annual flow (Figure 20). West- Future data: 2015-2095 200 180 160 140 120 100 80 60 40 20 0 0 100 200 300 400 500 600 700 800 Prec (cm) 5-year Mov. Avg. (Flow) Flow (cm) 5-year Mov. Avg. (Prec.) Figure 20. Annual comparison of the historical (1960-1990) and future (2015-2095) predicted streamflow and precipitation. 41

Figure 21 presents the annual and seasonal averages of streamflow for the historical and future periods. The results of model simulations show that the annual flow slightly increased during the future period compare to historical period. Figure 21. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average total streamflows. The seasonal simulations show that although flow increased during the winter and spring period, but the annual flow remains almost the same because of the decrease during summer period. Figure 22 presents the monthly averages of streamflow for the historical and three different future periods. Figure 22. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflows. 42

Sediment Load Simulations The impact of changed hydrology on sediment loads in the Canagagigue West watershed was assessed using the SWAT model. The annual, seasonal and monthly sediment load results of the SWAT model simulations are presented in Figures 23 to 25. The analysis of the data given in Figure 23 using moving average approach shows an increasing trend for the sediment load during the future period. Figure 23. Annual comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load The annual sediment loads increased. The increase in Annual sediment load is mainly during the spring season (Figure 24) which is because of the increased streamflow as a result of snowmelt and more precipitation respectively. Figure 24. Seasonal comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. 43

Figure 25 presents the monthly sediment loads simulated during historical (1960-1990) and future (2015-2095) period. The monthly comparison of historical and future simulation results show that the sediment load increased during the months of March, April and May in future compared to historical period, which is due to increased simulated flow during these in months. Figure 25. Monthly comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. Climate Change Simulations in Canagagigue East Watershed (GA035) The impact of climate change on hydrology of Canagagigue Creek East watershed at GA035 station, was examined by running the SWAT model using climate data from 1960-1990 and 2015-2095 and the results are presented in Figures 26 through 33. Water Balance Analysis Water balance components (precipitation, evapotranspiration and streamflow) over 30-year historical period (1960-1990) and 80-year future period (2015-2095) are presented in Figure 26. As can be seen from these data all the components show an increasing trend. Figure 27 presents the changes of the water balance components during the historical period (1960-1990), and for the early future (2015-2044), m future id (2045-2074) and late future (2075-2095) part of the century. The comparison of the 2075-2095 and 1960-1990 results show that the precipitaion, streamflow and ET will increase by 41%, 7% and 34%, respectively. 44

Figure 26. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2095 periods. 1960-1990 8.0 73.5 37.9 119.4 2015-2044 3.8 78.9 125.6 42.9 2045-2074 -10.4 2075-2095 -6.4 95.6 132.2 98.6 142.6 46.9 50.4 Precipitation (cm) ET (cm) Stream Flow (cm) Chang in Storage (cm) Figure 27. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. Figure 28 shows the annual trend of the precipitation and simulated flow for both the historical and future periods. These results show an increasing trend of the annual precipitation and stream flow significant over the future period and non-significant over the historical periods (Figure 28). 45

Figure 28. Annual comparison of the historical (1960-1990) and future ( 2015-2095) predicted streamflow and precipitation. Figure 29 presents the annual and seasonal averages of streamflow for the historical and three different future periods. The results of model simulations show increase in annual flow mainly due to increase during spring and winter seasons (Figure 29). Figure 29. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflows. Figure 30 presents the monthly averages of streamflow for the historical and three different future periods. The results of model simulations show increase in annual flow mainly due to the increase during spring season, the months of March, April and May (Figure 30). 46

Figure 30. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflows. An analysis of the monthly streamflow simulations results indicates that the increase of the future annual flow is mainly because of an increase in flow during months of March, April and May. The increase of the flow during the winter is mainly during March which is because of more rainfall, and less snow.. During the months of April and May in spring season, although the precipitation is slightly increased, the increase in flow is more compared to precipitation. The increase in flow during March and April is due to earlier and more snowmelt in future compared to historical period, which causes an increase in flow during the March and April. The increase in flow during the month of May can be due to more rainfall in spring season compared to historical period. Sediment Load Simulations The impact of change in hydrology on sediment loads in the Canagagigue East watershed was assessed using the SWAT model. The annual, seasonal and monthly simulated results are presented in Figures 31 to 33. The analysis of the data, given in Figure 31, using moving average approach show that an increasing trend of annual sediment load for both historical and future data. The model simulation results of flow show that annual streamflow is expected to increase for the future period. As a result annual sediment load will also increase in future (Figure 32 and 33). 47

Figure 31. Annual comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load Figure 32. Seasonal and annual comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. The annual sediment loads increased dramatically during the spring season (Figure 32) which is mainly due to increased streamflow as a result of snowmelt and more precipitation respectively. Figure 33 presents the monthly sediment loads simulated during historical (1960-1990) and future (2015-2095) period. The monthly comparison of historical and future simulation results show that the sediment load increased during the months of March, April and May in future compared to historical period, which is due to increased simulated flow during these in months. 48

Figure 33. Monthly comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. Field Scale Simulations with DRAINMOD Green Belt Field Site The experimental site was a 14 ha (approximately 450 315 m) field located at the Greenbelt Research Farm of Agriculture and Agri-Food Canada, Ottawa, ON. It has a Typic Haplaquent (Dalhousie Association, Brandon series) soil with loamy-textured in the Ap and B horizons underlain by silty clay at a depth of approximately 60 cm. Physical, chemical, and hydraulic properties of the soil at the study site (given Table 9) reported by Gupta et al., (1992, 1993) and Thooko et al., (1994) were used in this study. Figure 34. Layout of the experimental field of the two tillage treatments (from Patni et al., 1996) In 1996, this field was tile drained with drain depth of 1 m and latera spacing ofs 15 m. The field is nearly level with an average slope of about 0.2%. The field was divided into four adjacent plots with two experimental treatments, CT and NT. About 20 cm high berms were built around the periphery of the plots to prevent surface water flow across plots. Each plot was about 3 ha and was drained by four or more tile laterals. 49

Table 7. Soil properties at Green Belt field site in Ontario (Patni et al., 1996) Soil properties NT CT Soil Texture 0-15 15-30 30-60 60-120 0-15 15-30 30-60 60-120 Sand (%) 56.74 43.66 19.11 15.07 61.70 54.66 36.06 14.92 Clay (%) 12.15 13.42 26.20 26.28 10.11 11.81 18.90 27.43 Silt (%) 31.11 42.92 54.69 58.65 28.19 33.53 54.04 57.65 Hydraulic conductivity (cm/h) 29.95 31.75 2.36 61.43 49.39 2.36 Soil ph 5.51 5.50 5.68 6.0 5.54 5.59 5.58 6.0 Organic matter 3.97 3.30 2.37 2.13 3.77 3.13 2.38 2.01 All flows from 1991 to May 1994 was assigned to 23 consecutive flow events. The dates of these flow events are given in Table 10 and Figure 35. Table 8. Tile flow events (Patni et al., 1996) Event No. Crop year Event Event Crop Event Start date End date No. year Start date End date 1 90-91 91-02-21 91-03-14 13 92-93 92-11-23 92-12-05 2 91-03-14 91-03-22 14 93-01-04 93-01-07 3 91-03-22 91-04-03 15 93-04-10 93-04-17 4 91-04-03 91-04-15 16 93-04-17 93-04-20 5 91-04-15 91-04-30 17 93-04-20 93-05-09 6 91-92 92-03-27 92-04-05 18 93-94 93-05-31 93-06-09 7 92-04-05 92-04-14 19 93-06-15 93-06-27 8 92-04-14 92-05-10 20 93-11-28 93-12-15 21 94-02-20 94-03-29 9 92-93 92-07-17 92-07-24 22 94-03-29 94-04-08 10 92-08-04 92-08-09 23 94-04-08 94-05-06 11 92-11-03 92-11-09 12 92-11-11 92-11-19 50

Figure 35. Observed tile flow during 23 precipitation events_ Figure 35. presents tile flow during 23 events between January 1991 and May 1994. The solid vertical lines indicate the crop year and the dashed vertical lines indicate the season (SM:snowmelt; SP: spring; GS: growing season; FA: fall) during which flow occurred. Model Inputs Model inputs include soil properties, a drainage volume-water table depth relationship, upward flux, infiltration parameters, crop data drainage system parameters, surface drainage and weather data. Soil water characteristics data, rooting depth and saturated hydraulic conductivities required for each layer of soil profile were taken from Patni (1998). With this data DRAINMOD was used to calculate the relationships between water table depth (WTD) and drained volume and between WTD and maximum steady upward flux. The Green-Ampt equation was used to simulate infiltration. The infiltration parameters are calculated in the model, as a function of water table depth using the soil moisture retention curve of the soil above the tile. Figure 36. Monthly precipitation at Green Belt field during the study period. 51

Monthly rainfall during used in this study is given in Figure 36. The annual precipitation was close to the 75-year regional average of 864 mm, except in 1991 which was relatively dry year and precipitation was below the average and water table depth was more than 3 meters below the soil surface during most of the growing season. The 1992 growing season was wet. The model gives user an option of using observed ET data or applying daily maximum and minimum temperatures to calculate ET using the Thornthwaite equation. Different management practices applied in Green Belt field are listed in Table 9. Table 9. Management practices at Green Belt site Operation year 1990 1991 1992 1993 Disking or cultivating date 7 May 8 May 25 Apr 5 May Planting date 10 May 10 May 11 May 11 May Corn (seeds/ha) 66700 66700 66700 66700 Starter fertilizer 8-32-16 (kg N/ha) 12 9 9 9 Anhydrous ammonia injection date 22 Jun 13 Jun 15 Jun 25 Jun Application rate (kg N/ha) 100 140 120 115 Total N applied (kg N/ha/year) 112 149 129 124 Herbicide application date (post emergent) 31 May 24 May 26 May 7 Jun Metolachlor (kg active ingredient/ha) 2.6 2.6 2.6 2.4 Atrazine (kg active ingredient/ha) 2.2 1.5 1.8 1.9 Harvest date 3 Oct 19 Sep 6 Oct 21 Sep Plowing date for conventional tillage plots 5 Nov 25 Sep 6 Oct 3 Oct RESULTS AND DISCUSSIONS DRAINMOD Model Calibration and Validation Water Table Depth Time series plots of daily WTDs during calibration and validation periods for the no tillage and conventional tillage conditions are shown in Figure 37 and 38. Figure 37. Measured and simulated daily precipitation and water table depth in the Green Belt field with no tillage 52

Figure 38. Measured and simulated daily precipitation and water table depth in the Green Belt field with conventional tillage The calibration and validation model performance results for the daily time steps are presented in Table 10. The statistics given in Table 11 indicate that the DRAINMOD model simulated WTD adequately well during the calibration and validation periods. Table 10. Monthly and daily calibration and validation statistics at Green Belt field with no tillage Statistical Monthly Daily Index Calibration Validation Calibration Validation R 2 0.86 0.82 0.75 0.72 NSE 0.88 0.80 0.78 0.72 PBIAS -3.00 12.83-3.50 9.83 Table11. Monthly and daily calibration and validation statistics at Green Belt field with conventional tillage Statistical Monthly Daily Index Calibration Validation Calibration Validation R 2 0.87 0.84 0.70 0.74 NSE 0.84 0.80 0.73 0.70 PBIAS -5.84 10.00-8.50 12.05 Tile Drainage Flow Time series plots of observed and simulated daily tile flow during calibration and validation periods (Figure 39 and 40) show that observed and simulated daily drain outflows are in a good agreement. The calibration and validation model performance results for the daily and monthly time steps presented in Table 12 and 13. 53

Figure 39. Measured and simulated daily precipitation and tile drainage in the Green Belt field with conventional tillage Figure 40. Measured and simulated daily precipitation and tile drainage in the Green Belt field with conventional tillage Table 12. Monthly and daily calibration and validation statistics at Green Belt field with no tillage Statistical Monthly Daily Index Calibration Validation Calibration Validation R 2 0.86 0.82 0.75 0.72 NSE 0.88 0.80 0.78 0.72 PBIAS -3.00 12.83-3.50 9.83 54

Table 13. Calibration and validation statistics at Green Belt field with conventional tillage Statistical Monthly Daily Index Calibration Validation Calibration Validation R 2 0.87 0.84 0.70 0.74 NSE 0.84 0.80 0.73 0.70 PBIAS -5.84 10.00-8.50 12.05 Figure 41 presents the observed and simulated tile flow for 23 precipitation events for no tillage and conventional tillage practices (Figure 41 and 42). The results show that observed and simulated daily drain outflows are in a good agreement. Figure41. Measured and simulated tile flow during precipitation events in the Green Belt watershed Figure42. Measured and simulated tile flow during precipitation events in the Green Belt watershed 55

Nitrogen Simulations Once DRAINMOD was calibrated and validated to adequately simulate water table depth and drain outflow, a set of nitrogen simulations was conducted to calibrate the nitrogen component of the model. The results of nitrogen simulations are presented in figures 43 to 52. Figure 43 and 44 presents the observed and simulated nitrogen losses for 23 precipitation events for no tillage and conventional tillage practices. The results show that observed and simulated daily drain outflows are in a good agreement. Figure 43. Measured and simulated No3 loss during precipitation events in the Green Belt watershed with no tillage Figure 44. Measured and simulated No3 loss during precipitation events in the Green Belt watershed with conventional tillage Figure 45 through 48 presents the calibration and validation results for daily and cumulative nitrogen losses for no tillage and conventional tillage practices. 56

Jan-93 Feb-93 Mar-93 Apr-93 May-93 Jun-93 Jul-93 Aug-93 Sep-93 Oct-93 Nov-93 Dec-93 Jan-94 Feb-94 Mar-94 Apr-94 May-94 Rate of NO3 loss (kg ha -1 ) Cumulative NO3 los (kg ha -1 ) Rate of NO3 loss (kg ha -1 ) Cumulative NO3 los (kg ha -1 ) 8 6 4 2 Claibration 100 80 60 40 20 0 0 Obs. NO3-NT Sim. NO3-NT Obs. Cum. No3 NT Sim. Cum. No3 NT Figure 45. Calibration of daily and cumulative No3 loss in the Green Belt field with no tillage 8 6 4 2 Validation 100 80 60 40 20 0 0 Obs. NO3-NT Sim. NO3-NT Obs. Cum. No3 NT Sim. Cum. No3 NT Figure 46. Validation of daily and cumulative No3 loss in the Green Belt field with no tillage 57

Rate of NO3 loss (kg ha -1 ) Cumulative NO3 los (kg ha -1 ) Rate of NO3 loss (kg ha -1 ) Cumulative NO3 los (kg ha -1 ) 8 6 4 2 Calibration 100 80 60 40 20 0 0 Obs. NO3-CT Sim. NO3-CT Obs. Cum. No3 CT Sim. Cum. No3 CT Figure 47. Calibration of daily and cumulative No3 loss during precipitation events in the Green Belt watershed with conventional tillage 8 6 4 2 0 Validation 100 80 60 40 20 0 Sim. NO3-NT Obs. NO3-CT Obs. Cum. No3 NT Sim. Cum. No3 NT Figure 48. Validation of daily and cumulative No3 loss during precipitation events in the Green Belt watershed with conventional tillage Climate Change Simulations in Green Belt Watershed Future climate dataset used for field scale simulation in this project provided by CCDP (Climate Change Data Portal) of Regina University were derived from the high resolution (25km 25km) climate projections developed by the IEESC at University of Regina using the PRECIS modeling system (Wang and Gordon, 2013). The impact of climate change on drainable water quantity and quality was evaluated for the Green Belt field using DRAINMOD model. Data required to run 58

DRAINMOD model for simulation include precipitation, min/max temperature, wind speed, relative humidity for period 1960-1990 and 2015-2095 were provided by CCDP (Wang and Gordon, 2013). PRECIS climate change projections indicate an increase in the average annual evapotranspiration and temperature, and slight increase in annual precipitation. Precipitation remained unchanged during summer and fall and increased slightly during winter and spring. The climate change impact on hydrology of Green Belt field was examined by running the DRAINMOD using climate data from 1960-1990 and 2015-2095 periods. The model was used to evaluate the impact of climate change for two different plots under conventional tillage and no tillage. Climate Change Simulations under Conventional Tillage Water Balance Analysis The components of the simulated water balance, precipitation, evapotranspiration and streamflow, for 30-year historical (1960-1990) and 80-year future (2015-2095) are presented in Figure 49. These data indicates that evapotranspiration has an increasing trend due to increase in precipitation while tile flow remains almost unchanged. The components of water balance during the historical period (1960-1990), and for the future, early century (2015-2044), mid century (2045-2074) and late century (2075-2095) are presented in Figure 50. The comparison of simulated results for the 2075-2095 period with 1960-1990 period show that the precipitaion, streamflow and ET will increase by 10%, 14% and 12%, respectively. Figure 49. DRAINMOD simulated water balance components (precipitation, ET and tile flow) for 1960-1990, and 2015-2095 periods 59

Figure 50. DRAINMOD simulated water balance components (precipitation, ET and tile flow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. Figure 51 shows an inceasing annual trend of the precipitation and simulated tile drainage outflow for both the historical and future period. The annual precipitation showed an increasing trend while no trend can be observed for the annual tile flow (Figure 51). Figure 51. Annual comparison of the historical (1960-1990) and future ( 2015-2095) predicted tile flow and precipitation Figure 52. presents the annual and seasonal averages of tile flow for the historical and future periods. The results of model simulations show that the annual tile flow slightly increased during 60

the future period compared to historical period. Figure 52. Historical (1960-1990) and future (2015-2095) simulated seasonal and annual average total tile flows. Figure 53 presents the monthly averages of tile flow for four different historical future periods. Figure 53. Historical (1960-1990) and future (2015-2095) simulated monthly average total tile flows. Nitrogen Simulations The impact of change in hydrology on nitrogen losses in the Green Belt Field under conventional tillage was assessed using the DRAINMOD model. The annual, seasonal and monthly simulated results are presented in Figures 54 and 55. 61

Figure 54. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average nitrate nitrogen losses The annual No 3 -N loss will increase (Figure 54). The increase in annual nitrogen loss is mainly during the spring season (Figure 54 and 55) which is because of the increased streamflow as a result of snowmelt and more precipitation. Figure 55. Historical (1960-1990) and future (2015-2095) simulated monthly average nitrate nitrogen losses Effect of Climate Change under No Tillage Water Balance Analysis The components of the simulated water balance, precipitation, evapotranspiration and tile flow, for 30-year historical (1960-1990) and 80-year future (2015-2095) are presented in Figure 56. These data indicates that all the components produce an increasing annual trend. The components of water balance during the historical period (1960-1990), and for the future, early 62

century (2015-2044), mid century (2045-2074) and late century (2075-2095) are presented in Figure 57. The comparison of simulated results for the 2075-2095 period with 1960-1990 period show that the precipitaion, tile flow and ET will increase by 10%, 44% and 11%, respectively. Figure 56. DRAINMOD simulated water balance components (precipitation, ET and tile flow) for 1960-1990, and 2015-2095 periods Figure 57. DRAINMOD simulated water balance components (precipitation, ET and tile flow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. 63

Figure 58 shows the annual trend of the precipitation and simulated tile flow for both the historical and future periods. These results show an increasing trend of the annual precipitation and stream flow significant over the future period and non-significant over the historical periods (Figure 58). Figure 58. Annual comparison of the historical (1960-1990) and future ( 2015-2095) predicted tile flow and precipitation Figure 59 presents the annual and seasonal averages of tile flow for the historical and future periods. The results of model simulations show an increase in annual flow mainly due to increase during spring and winter seasons (Figure 59). Figure 59. Historical (1960-1990) and future (2015-2095) simulated seasonal and annual average total tile flows. Figure 60 presents the monthly averages of tile flow for the historical and future periods. The results of model simulations show increase in annual tile flow mainly due to the increase during spring and winter seasons (Figure 59 and 60). 64

Figure 60. Historical (1960-1990) and future (2015-2095) simulated monthly average total tile flows. Nitrogen Simulations The impact of change in hydrology on nitrogen losses in the Green Belt Field under no tillage was assessed using the DRAINMOD model. The annual, seasonal and monthly simulated results are presented in Figures 61and 62. Figure 61. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average nitrate nitrogen losses The annual NO 3 -N loss will increase (Figure 61). The increase in Annual Nitrogen losses is mainly during the winter and spring seasons (Figure 61) which is because of the increased streamflow as a result of snowmelt and more precipitation. 65

Figure 62. Historical (1960-1990) and future (2015-2095) simulated monthly average nitrate nitrogen losses Conclusions This study presents the results of : - Evaluation of a watershed scale model, SWAT for three watershed (Canagagigue Creek, Canagagigue West and Canagagigue East) located in Ontario. - Evaluation of a field scale model, DRAINMOD, for a 14 ha field Green Belt, located near ottawa, Ontario. - Application of SWAT model in predicting the effects of climate change on water resources and sediment load for three watershed. - Application of DRAINMOD model in evalating the impacts of climate change on hydrology and nitrogen losses for Green Belt field. The results demonstrated that SWAT model adequtey simulated the hydrology and water quality of the watersheds and performed a satisfactory results. DRAINMOD also performed very well in simulating the hydrology and water quality of the Green Belt Field. In order to assess the impacts of climate change, the simulations were performed based on the climate dataset provided by CCDP (Climate Change Data Portal) of Regina University which were derived from the high resolution (25km 25km) climate projections developed by the IEESC at University of Regina using the PRECIS modeling system (Wang and Gordon, 2013). The climatic parameters used in this project to run the SWAT model include precipitation, min/max temperature, wind speed, relative humidity, all provided by CCDP for the period of 1960-1990 and 2015-2095. The climate data required for modeling by SWAT model is available for the period 2015-2095 (Wang and Gordon, 2013). SWAT simulations results indicate that for the Canagigue watershed, in genera, the climate of the study area will be warmer and wetter. The increase in min/max temperatures in winter leads 66

to more rainfall-dominated regime and less snowfall. This will result an increase in length of the growing season. Therefore, the climate change seems to alter both magnitude and seasonality of flow. In general climate change effects more spring and winter hydrology than summer hydrology. The results show that the annual flow is expected to increase significantly in future, which leads to an increase in the sediment load at the in the stream. DRAINMOD simulations also predict more tile drainage outflow in future. As a result, annual NO 3 -N loss will also increase during the future period. This is important to understand that future flow conditions could not be projected due to the uncertainty in climate change scenarios and the outputs from the climate change models. However the results of this analysis could serve as a guideline for planning water resource in order to improve more sustainable water use in the watersheds. 67

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APPENDIX 76

Canagagigue Creek (Floradale) PRECIS 70% Figure A1. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2095 periods. Figure A2. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. 77

Figure A3. Annual comparison of the historical (1960-1990) and future (2015-2095) predicted streamflow and precipitation. Figure A4. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average total streamflow. 78

Figure A5. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflow. Figure A6. Seasonal comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. Figure A7. Monthly comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. 79

Canagagigue Creek (Floradale) PRECIS 90% Figure A8. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2095 periods. Figure A9. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. 80

Figure A10. Annual comparison of the historical (1960-1990) and future (2015-2095) predicted streamflow and precipitation. Figure A11. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average total streamflow. 81

Figure A12. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflow. Figure A13. Seasonal comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. Figure A14. Monthly comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. 82

Canagagigue West (GA036) PRECIS 70% Figure A15. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2095 periods. Figure A16. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. 83

Figure A17. Annual comparison of the historical (1960-1990) and future (2015-2095) predicted streamflow and precipitation. Figure A18. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average total streamflow. 84

Figure A19. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflow. Figure A20. Seasonal comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. Figure A21. Monthly comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. 85

Canagagigue West (GA036) PRECIS 90% Figure A22. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2095 periods. Figure A23. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. 86

Figure A24. Annual comparison of the historical (1960-1990) and future (2015-2095) predicted streamflow and precipitation. Figure A25. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average total streamflow. 87

Figure A26. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflow. Figure A27. Seasonal comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. Figure A28. Monthly comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. 88

Canagagigue East (GA035) PRECIS 70% Figure A29. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2095 periods. Figure A30. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. 89

Figure A31. Annual comparison of the historical (1960-1990) and future (2015-2095) predicted streamflow and precipitation. Figure A32. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average total streamflow. 90

Figure A33. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflow. Figure A34. Seasonal comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. Figure A35. Monthly comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. 91

Canagagigue East (GA035) PRECIS 90% Figure A36. SWAT simulated water balance components (precipitation, ET and streamflow) for 1960-1990, and 2015-2095 periods. Figure A37. Annual comparison of the historical (1960-1990) and future (2015-2095) predicted streamflow and precipitation. 92

Total Flow (cm) 200 180 160 140 120 100 80 60 40 20 0 1960-1990 2015-2044 2045-2074 2075-2096 Spring Summer Fall Winter Annual Figure A38. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average total streamflow. Figure A39. Historical (1960-1990) and future (2015-2095) simulated monthly average total streamflow. 93

Figure A40. Seasonal comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. Figure A41. Monthly comparison of the historical (1960-1990) and future (2015-2095) simulated sediment load. 94

Green Belt, PRECIS 70% Figure A42. SWAT simulated water balance components (precipitation, ET and tile flow) for 1960-1990, and 2015-2095 periods. Figure A43. SWAT simulated water balance components (precipitation, ET and tile flow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. 95

Figure A44. Annual comparison of the historical (1960-1990) and future (2015-2095) predicted tile flow and precipitation. Figure A45. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average tile flow. 96

Figure A46. Historical (1960-1990) and future (2015-2095) simulated monthly average tile tile flow. Figure 47. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average nitrate nitrogen losses Figure 48. Historical (1960-1990) and future (2015-2095) simulated monthly average nitrate nitrogen losses 97

Green Belt, PRECIS 90% Figure A49. SWAT simulated water balance components (precipitation, ET and tile flow) for 1960-1990, and 2015-2095 periods. Figure A50. Figure 16. SWAT simulated water balance components (precipitation, ET and tile flow) for 1960-1990, and 2015-2044, 2045-2075 and 2075-2095 periods. 98

Figure A51. Annual comparison of the historical (1960-1990) and future (2015-2095) predicted tile flow and precipitation. Figure A52. Historical (1960-1990) and future (2015-2095) simulated annual and seasonal average total tile flow. 99