Lac St. Cyr Water Quality

Transcription

1 THE KING S UNIVERSITY COLLEGE BIOLOGY DEPARTMENT Lac St. Cyr Water Quality An Exercise in Eutrophication Modeling Chelsea Dyck, Jacinda Johnston, Alyssa Wesselson 4/19/2013

2 1 Abstract Alberta Environment is completing a water quality assessment on Lac St. Cyr in partnership with the North Saskatchewan Watershed Alliance (NSWA). Lac St. Cyr is a small mesotrophic lake that since the 1950 s has supplied water to the town of St. Paul. The result of which was a decrease in water level. In 1978 Lac St. Cyr began receiving water diversions from the North Saskatchewan River during the winter months to re establish historic lake levels. The lake has been modeled individually for the years , 1991 and 1995, using an empirical eutrophication model called BATHTUB, which produces steady state nutrient calculations. The results obtained for 1995 have been set as the standard calibration levels for each subsequent analysis. Model results indicate that both Total Phosphorous and Total Nitrogen levels peaked in 1984 at 41.9 mg/m 3 P and 1109 mg/m 3 N, and have been steadily decreasing since. The water quality of the North Saskatchewan River has improved over this time period resulting in the improvement of the lake s water quality. Empirical data and the model show that the lake s nutrient levels were altered by the diversion. BATHTUB modeled for an increased water demand scenario showing how proposed increases in both lake water withdrawals and river diversions will alter the nutrient levels in the lake by Our research shows that Lac St. Cyr has reached a steady state with the current nutrient levels reflecting that of the river. Lac St. Cyr was one of the first water bodies in Alberta to receive river water diversions. Model projections are important aids for long term planning, especially since water scarcity and demand, as well as lake eutrophication, are increasingly serious concerns in Alberta. 1

3 Table of Contents 1 Abstract Acronyms Introduction Background Lake Eutrophication Lac St. Cyr River and Lake Diversions Trends in Water Quality..7 5 BATHTUB Eutrophication Tool BATHTUB Software BATHTUB Applied to four Albertan lakes Methodology Data Reduction BATHTUB Modeling Model Selections Global Variables Segment Data Tributary Data Export Coefficients (EC) Channels Model Coefficients Model Running Results and Discussion Calibration Year Water quality Predictive scenarios Conclusions Further Studies/Recommendations Acknowledgements References Appendix

4 Table of Figures Figure 1: Bathymetry and shoreline features of Lac St. Cyr... 6 Figure 2: Baptise Lake Restoration Scenarios Figure 3. Lac Ste. Anne and Lake Isle: Potential reduction of phosphorous loads from typical sources based on phosphorous budget for Figure 4: Dissolved oxygen versus depth of sampling for the north basin of Lac St. Cyr in 1995 showing the change in DO with increasing depth from which a hypolimnion is discernible Figure 5: Percentage of total effective drainage area attributed to each land use around Lac St. Cyr Figure 6: 1995 data applied calibration factors for a) total phosphorous, b) total nitrogen, c) chlorophyll a, and d) Secchi disk depth Figure 7: Calibration factors from 1995 applied to years for chlorophyll a Figure 8: Observed TP vs. Predicted TP in Lac St. Cyr Figure 9: Observed TN vs. predicted TN in Lac St. Cyr Figure 10: Change in total phosphorous from and 1995 years Figure 11: Total nitrogen levels predicted in Lac St. Cyr from BATHTUB Figure 12: Total phosphorous from the pumphouse river data Figure 13: Total phosphorous in Lac St. Cyr from Figure 14: Total nitrogen in Lac St. Cyr from Figure 15: Predictive scenario representing the amount of total phosphorous when increasing diversions in and out of the lake Figure 16: Predictive scenario representing the amount of total nitrogen when increasing diversions in and out of the lake List of Tables Table 1: BATHTUB model selection parameters for Lac St. Cyr Table 2: Global variable selections for Lac St. Cyr Table 3: Segment morphometry data for Lac St. Cyr 1995 dividing the lake into north and south basins Table 4: Seasonally averaged observed water quality data for the north and south basins of Lac St. Cyr in 1995 during the open water season of May to October from NSWA Lac St. Cyr unpublished data

5 Table 5: Calibration factors for the Lac St. Cyr BATHTUB model 1995 case file showing which parameters were changed in order to bring the predicted values of the model calculations in line with the observed water quality parameters Table 6: Internal loading variables for Lac St. Cyr Table 7: Tributary monitored inputs for nutrient concentration Total Phosphorous, Orthophosphate, Total Nitrogen, Inorganic Nitrogen, and Conservative Substance in parts per billion (mg/m 3 ) from NSWA Lac St. Cyr data Table 8: Annual flow weighted mean concentrations for Baptiste Lake, averaged from three streams in each land use (Agri, AMN; Forest, EFL) over a three year period ( ) in mg/l Table 9: Tributary land use drainage areas around each tributary and the percentage of land taken up by each in the effective drainage area Table 10: Area of land use type in hectares (ha) and averaged export coefficients for agriculture and forest land types from Baptiste Technical Report in kg/ha/yr Table 11: Total runoff values for the effective drainage area around Lac St. Cyr for each data year Table 12: Export Coefficients for the 3 main land use types agriculture, natural/forest, and cottages Lac St. Cyr Table 13: Future diversions in and out of the lake at a rate of increase of 2% per year Table 14. Total phosphorous loadings from 1995 showing the percentage of phosphorous in Lac St. Cyr entering from external factors Acronyms AFWM C Chl a EC LSC P N NSR NSWA TN TP Annual flow weighted mean Carbon Chlorophyll a Export coefficient Lac St. Cyr Phosphorous Nitrogen North Saskatchewan River North Saskatchewan Watershed Alliance Total Nitrogen Total Phosphorous 4

6 3 Introduction In December of 2012 the North Saskatchewan Watershed Alliance (NSWA) approached The King s University College to appoint a research team to model Lac St. Cyr, an Albertan lake that is located approximately 12.5 km south east of the town of St Paul. Since 1951 Lac St. Cyr, a small mesotrophic lake, has been serving as a water supply to St Paul (Logan & Trew, 2013, p. 2). By 1978 the lake levels began dropping by almost 3 metres. Since 1978 under recommendation by Alberta Environment, this lake has been receiving water diversions from the North Saskatchewan River to re establish historic water levels. Water is pumped from October to March (typically) to the south basin of the lake, as the water quality in the winter months will have less of an impact compared to the summer river water. Water from the north basin is pumped throughout the year to St Paul s water treatment plant, where it now supplies water to the town of St Paul, Elk Point and the County of St Paul, all of which have increasing populations and increasing water demands. The King s students have used an empirical eutrophication tool called BATHTUB to model the change of nutrients and filed data from , 1991 and This model was developed by the US Army Corps Engineers (Walker 2004) and is a very useful tool for predicting mass nutrient balances and future scenarios. This model incorporates lake morphometry, water quality, external and internal loadings to create a steady state water and nutrient balance of the lake. This project is important for a number of reasons including: impacts to water quality, predicting future scenarios, watershed management, and monitoring of water supply and demand on a water body. 4 Background 4.1 Lake Eutrophication Lakes can be classified as oligotrophic, mesotrophic, or eutrophic depending on rates of primary productivity. Lakes with high rates of productivity are eutrophic and low productivity lakes are oligotrophic with mesotrophic lakes having a primary productivity in between (Dodson, 2005 p.49). Eutrophic lakes generally have poor water quality with water often being turbid and green in colour due to high levels of phosphorous that contribute to algal blooms. There is a positive correlation between levels of phosphorous and chlorophyll a, which is often an indicator of algal biomass and primary productivity (Dodson, 2005, p. 242). Because phosphorous is often the limiting nutrient in lakes, the relationship between carbon (C), nitrogen (N), and phosphorous (P) has been described in average relative proportions of C:N:P as 106:16:1 (Dodson, 2005, p. 250). Another way to estimate clarity of the water is by measuring Secchi depth. This is done by lowering a disk into the lake and measuring the depth at which the disk is just visible (Dodson, 2005, p. 46). For the purpose of determining water quality for Lac St. Cyr, we focused on the levels of total phosphorous, chlorophyll a, total nitrogen and Secchi depth. 5

7 4.2 Lac St. Cyr Lac St. Cyr is a relatively small, mesotrophic lake located approximately 160 kilometres northeast of Edmonton and about 12 kilometres southeast of St. Paul, Alberta (Figliuzzi, 2013, p.1). The lake is home to 30 cottage lots with residents, three of which are permanent dwellers and the rest being seasonal (pers. comm., A. Richard, February 13, 2013). About one third of the drainage basin surrounding the lake is used for agricultural purposes with the rest being typical dry mixedwood forest vegetation (Atlas of Alberta Lakes). The lake itself has three distinct basins (Figure 1). The north basin which is deeper (depth of 22 metres) and stratifies in the summer and the east and west basins (herein referred to as the south basin) which is shallow (< 10 metres) and are unified in temperature throughout the summer (Figliuzzi, 2013, p. 2). The Lake has been used for providing drinking water to the town of St. Paul since In 1978 the water level in the lake had declined by about 3 metres. A pipeline was installed from the North Saskatchewan River (NSR) to divert water to the lake to replace the amount of water being used by St. Paul (Atlas of Alberta Lakes, p. 407). Today the lake is of greater importance because of the increase in water withdrawals in 2012 to supply Elk Point and the southeast sector of the County of St. Paul, as well as the possibility of supplying Bonneyville (Figliuzzi, 2013, p. i). This project was completed to model the change in the water quality in the lake from available data since the diversions were started and to extend into the future with a possible increase in water diversion from the NSR. 4.3 River and Lake Diversions Figure 1: Bathymetry and shoreline features of Lac St. Cyr *Source: Atlas of Alberta Lakes Water started to be diverted from the NSR into Lac St. Cyr in 1978 to restore dropping lake water levels. A pipeline from the NSR enters Lac St. Cyr at the southeast portion of the lake. The pipeline and pumphouse for the water leaving the lake are located at the top of the north basin (see Figure 1). In the 1980s water was diverted from the river during October to 6

8 February, with the diversion extending into March during the 1990s. Water is being withdrawn throughout the entire year from the lake to St. Paul s Water Treatment Plant. The volume of water withdrawn from the lake has not changed much since the diversions into the lake started, with the average amount withdrawn being 936, 835 m 3 (Figliuzzi, FINAL water data). However, it is expected in the next 20 years that the increase in annual diversions could reach 2,220,300 m 3 or 17 percent of the lake volume (Figluizzi, 2013, p. 2). This project is a model of future water quality scenarios that assumes a two percent increase in diversion amount each year for 20 years. 4.4 Trends in Water Quality The increase in water from the NSR in the late 1970s and early 1980s changed the water quality to a more eutrophic state with the increase in phosphorous from the river. In November of 1984 the Capital Region Wastewater Treatment Plant was opened in Fort Saskatchewan. This enhanced the treatment of sewage from Stony Plain, Spruce Grove, St. Albert, northeast Edmonton and Sherwood Park, improving the quality of the downstream water (Logan & Trew, 2013, p. 29). Levels of total phosphorous started to decrease in the lake after 1985 and are currently low in the lake. Levels of total nitrogen, chlorophyll a, and Secchi disk depth in the lake have also been improving with the water quality improvement of the river. Recent interest in water quality has lead to research in levels of pharmaceuticals in the river and how they may transfer into the lake but no data exists for Lac St. Cyr (Logan & Trew, 2013, p. 51). 5 BATHTUB Eutrophication Tool 5.1 BATHTUB Software The software used for the water quality modeling is a program called BATHTUB (version 6.14). This program is designed to replicate eutrophication processes of reservoirs or lakes (Walker, 2006 program description). Water quality information, atmospheric loadings, runoff, evaporation, precipitation and tributary data are put into the model and the outputs given are mass balances and predicted water quality from empirical relationships the model uses (Walker, 2006 program description). The program can also be used for a diagnostic or predictive purpose. One can determine water quality changes over a past period, changes from land use, or determine changes into the future. This modelling program was beneficial for the future scenarios and for visually representing the data given from NSWA. 5.2 BATHTUB applied to four Albertan lakes BATHTUB has been set up and calibrated for a few lakes in Alberta (Pine, Baptiste, Lake Isle and Lac Ste Anne) by Alberta Environment staff during studies in the 1990s and early 2000s. 7

9 5.2.1 Baptiste Lake (May 2007) Baptiste Lake is a highly productive eutrophic lake that is subject to severe algal blooms during the summer months. Since the 1970s, lake users have been concerned about the lake water quality, and this resulted in the implementation of a lake monitoring program (Girhiny, 2007). BATHTUB was calibrated with the 1977 year to develop theoretical development and restoration scenarios. In the development scenarios tributary nutrient loading associated with forested watersheds were converted to represent loading in agricultural areas (Girhiny, p. 10). In the restoration scenarios, the nutrient loadings associated with agriculture was converted to represent forested areas. Figure 2 shows the results of theoretical development/restoration for 5 years (Girhiny, 2007, p. 12). Figure 2: Baptise Lake Restoration Scenarios. *Figure on left the forested scenario and figure on right is the agricultural scenario Lake Isle and Lac Ste. Anne (March 1997) Lake Isle is a long and fairly shallow lake (max. depth 7.5 m) that is located 80 km west of the City of Edmonton in Parkland and Lac Ste. Anne counties. The Sturgeon River enters Lake Isle from the west and exits through the east, where the Sturgeon River then becomes the main inlet into the west of Lac Ste. Anne, and exits at the eastern end. Both of these are recreational lakes that heavily support sport fishing and boating activities. Lake Isle s is a hyper eutrophic lake, meaning it is overly productive which explains why the Secchi depth measurement in 2011 read a shallow depth of 0.66 metres. Along with high total phosphorous values, and chlorophyll a values, this lake is very prone to algal blooms, an indicator of poor water quality. Lac Ste. Anne on the other hand, is classified as a eutrophic lake. BATHTUB was used to calculate a phosphorous budget for both lakes as phosphorous is a key element in governing the growth of plants in lakes, whether they are macrophytes (shoreline plants) or an alga (which causes the water to appear green). In theory, if the amount of phosphorous entering the lake could be reduced, better water quality should result (Mitchell, p.7). Both the lakes, at the time of their study, were having increasing complaints from water users about the decreasing water quality. This study concluded that the 1996 data suggest that the watershed is a significant source of phosphorous. Within watersheds, increased nutrient 8

10 loadings results from various human activities, including clearing of native vegetation, cattle grazing, application of fertilizers and construction of roads and cottages (Mitchell, p. 7). Both of these lakes had recommendations to reduce nutrient loadings from land owners (Figure 3). This could be done by simply controlling the leakage of sewage from malfunctioning septic systems. Figure 3: Lac Ste. Anne and Lake Isle: Potential reduction of phosphorous loads from typical sources based on phosphorous budget for Pine Lake (August 1997) In 1997, Limnologist Al Sosiak reported on the Modeling of the response of Pine Lake to reduced internal and external loadings. BATHTUB 5.3 was used to predict how reductions in various levels of internal and external loadings affect the phosphorous concentrations in Pine Lake. Pine Lake in the 1990s was experiencing algal blooms, which affects the lake quality. Four scenarios were modeled in BATHTUB that showed a decrease algal biomass. A realistic objective for the Pine Lake Restoration Program would be to achieve the conditions modeled in Run 6, which would reduce algal productivity to the borderline between mesotrophic and eutrophic. As described above, the assumptions for this scenario include a reasonable estimate of the decrease in internal phosphorus loading that can be achieved over 10 years, and assumes that phosphorus levels in the critical streams and diffuse runoff can be only improved to levels currently occurring in the least impacted stream in the Pine Lake basin (Stream 7) (Sosaik, p. 8). 9

11 6 Methodology 6.1 Data Reduction The water quality data of Lac St. Cyr, which was received from the NSWA, was comprehensive. It often contained parameters that were unnecessary for use in the BATHTUB modeling software. BATHTUB software and other program users were consulted to help simplify the process of implementation. From the data set Inorganics No Metals, a series of parameters were isolated, sorted, and resaved as follows: From Excel File: Sorted Inorganics Data for Modeling (Euphotic Only) o Total Phosphorous (15422 profile or composite ) o Phosphate Dissolved Ortho (15256) o Chloride, AKA Conservative Substance (17203) o Secchi Disk Transparency (2078) o Chlorophyll A (6715) From Excel File: Sorted Nitrogen Data for Modeling o Nitrogen TKN, AKA Organic Nitrogen (7015) o Nitrogen Dissolved NO2 & NO3, AKA Inorganic Nitrogen (7111) o Total Nitrogen was calculated by adding organic and inorganic together From Excel Files: DO Depth Graphs North /South Basin o Dissolved Oxygen (8102) o Depth of Sampling (97251) *Column numbers in data sheets for Lac St. Cyr are in parenthesis The best years possible were obtained from the NSWA summary sheet, and were sorted according to which parameters were needed in the model. The sample data had generally been taken in open water season, which, in Alberta, is April to September/October. After sorting for the needed parameters, only the years , 1991, & were considered to have a sufficient amount of data for modeling purposes. Only the euphotic or upper mixed layer (top 0 1m) samples were taken into consideration from the Profile sites 2 for the isolated parameters. This ensured that we had the consistent data that reflected a vertically integrated sample set (such as the composite data sites) 3. All data, for each year, was averaged and then converted to parts per billion units (ppb mg/m 3 ) where necessary. 1 In the beginning 2012 was considered but later discarded as a data set due to inconsistent monitoring. The data for the pumphouse was usually taken in the winter months during water diversions into the lake. In this case it was taken later in summer. This had implications for model calibration and was invaluable for modeling purposes. 2 Wwwalker.net BATHTUB Online Manual, section Edit Segments 3 Profile data sets were sampled at different depths of the water column, generally top (first 1 3 meters), middle, and bottom (the latter two depending on the depth of the basin). Composite data sets were taken with vertical tubes that were able to take a more integrated sample set, averaging the parameters tested throughout the water. 10

12 6.2 BATHTUB Modeling Once the data was isolated, a case file for each year was created to allow for quick input of data and later calibration. The case files have a number of sections that need to be filled in before implementation and calibration can begin. The following section explains how parameters were chosen and entered in the BATHTUB software. All tables and figures are based on the 1995 case file for Lac St. Cyr which was used for final calibration of the model Model Selections The model selections in the BATHTUB program allow for the model to compute various parameters based on the kind of data put into the model and the data desired for prediction. The various nutrients and inorganic factors have many options for model selection. Each lake the model is applied to will need specific selections based on its unique characteristics. For Lac St. Cyr, the model selections applied were based on data available for another water body, Baptiste Lake. Both lakes have similar morphometry (north and south basin; one basin deeper than the other) and are located within the same eco region of Alberta. We were advised that we could keep many of the selections the same (pers. comm., David Trew). The chlorophyll a parameter was later changed to P,N, Low Turbidity after running the model to reflect Lac St. Cyr s characteristic difference (Table 1). Variables # Description Conservative Substance 1 Compute Mass Balances Total Phosphorous 5 Vollenweider (1976), Northern Lakes Total Nitrogen 4 Bachman (1980), Volumetric Load (Wn/V) 0.59 Chlorophyll A 3 P, N, Low Turbidity B = K 0.2 Xpn 1.25 Transparency 1 Secchi vs. Chl a and Turbidity S = K / (a + b B) Longitudinal Dispersion 2 Constant Numeric Fixed Dispersion Rate D = 1000 KD Phosphorous Calibration 1 Apply Calibration Factors to Predicted Nitrogen Calibration 1 Apply Calibration Factors to Predicted Error Analysis 1 Consider Model Error & Input Error Availability Factors 0 Ignore Mass Balance Tables 1 Use Estimated Segment Concentrations to Calculate Outflow and Storage Terms Output Destination 2 Excel Worksheet T 0.5 Table 1: BATHTUB model selection parameters for Lac St. Cyr 1995 column, which is more representative of the whole lake. This technique was not adapted until the mid 90 s for Lac St. Cyr. 11

13 6.2.2 Global Variables Global variables are fixed values for the year in question and will not change after running the model (Table 2). Averaging period: is the portion of the year in which the best data is available for modeling. In the case of Lac St. Cyr, this is during the open water season, April through September/October, hence an averaging period of half a year, or 0.5. Precipitation, evaporation and increase in storage are all based on the water balance information on the lake, obtained from Sal Figliuzzi and Associates. These parameters are the sum of the open water season, divided by the averaging period. Since this would essentially double the value, a yearly value was applied from Figliuzzi (2013). Atmospheric loads are based on the Baptiste Technical Report. These values are generally obtained from regional estimates, thus using the values in Baptiste was a viable option for this model. Global Variables Mean Temporal scale References Averaging Period (yrs) Months May 1 st to October 31 st Precipitation (m) year Water Balance for Lac Saint Cyr, Alberta (2013) Evaporation (m) year Water Balance for Lac Saint Cyr, Alberta (2013) Increase in Storage (m) year Water Balance for Lac Saint Cyr, Alberta (2013) Atmospheric Loads Total P (mg/m 2 yr) year Baptiste Lake Modelling Project (p. 17) Ortho P (mg/m 2 yr) year Baptiste Lake Modelling Project (p. 17) Total N (mg/m 2 yr) year Baptiste Lake Modelling Project (p. 17) Inorganic N (mg/m 2 yr) year Baptiste Lake Modelling Project (p. 17) Conservative Substance (mg/m Yearly Average Baptiste Lake Modelling Project (p. 17) yr) Table 2: Global variable selections for Lac St. Cyr *Annual and non annual hydrological, meteorological, and nutrient loading data have been applied to a 6 month period for the purpose of this model Segment Data Morphometry Lac St. Cyr is considered to have three segments within the monitoring program, North, East, and West. For this model it was decided that the lake would be divided into two segments, North and South (Table 3). This follows scheme 2 of BATHTUB which is a single reservoir, segmented (Walker, p. 4 17). The segments represent two different areas of the lake, which are distinguished by average depths of the basins, data available, and concentration differences between the two basins. The North basin is much deeper than the South, the 12

14 former being approximately 22m and the latter being approximately 10m deep (Figliuzzi, 2013, p. 2). The South basin is a combined average of the South East and West basins. There was very little data available for the West basin, thus the East basin data was used for most of the combined South basin segment of the model. Each segment is given a name (North Basin & South Basin), and the outflow is defined. For the South Basin, its outflow is to the North Basin, thus the North Basin flows out of reservoir. As well, the segments are given group numbers. These are defined by which segment is first; South Basin group 1 as it receives diversions and then flows to the North Basin, which is defined as group 2 because it flows out of the reservoir via a pipe and pump system. Other morphometry characteristics were applied to the segments (Table 3). Surface area and mean depth were provided by Figliuzzi (2013, p. 3) and The Atlas of Alberta Lakes (1990, p ) respectively, while length was calculated using GoogleEarth measurement tools. Mixed layer depth was estimated by the model, thus we used the model s values, and hypolimnetic thickness was calculated based on dissolved oxygen (DO) and depth graphs. From the DO v. Depth graphs (Figure 4), the hypolimnion is readily discernible by the sudden depletion of oxygen at a certain depth in the water table, and the depth at which it appears to begin can easily be averaged from the points on the graph. Once the points are averaged, they are subtracted from the maximum depth of each basin (found in Figliuzzi Table 1, 2013, p. 3), giving a hypolimnetic thickness value. Hypolimnetic Thickness = Maximum Depth of Basin Average Depth of Hypolimnion Variables South Basin North Basin Surface Area (km 2 ) Reference Water Balance For Lac Saint Cyr, Alberta Table 2 (p. 6) Mean Depth (m) Atlas of Alberta Lakes (p.407) Length (km) GoogleEarth Measurement Mixed Layer Depth (m) Used Estimated Mixed Depth from Model Hypolimnetic Thickness (m) Lac St. Cyr Unpublished Data Table 3: Segment morphometry data for Lac St. Cyr 1995 dividing the lake into North and South Basins 13

15 0 Dissolved Oxygen vs Depth in North Basin DO (mg/l) Depth (m) May June July Aug Aug Sept Oct Figure 4: Dissolved oxygen versus depth of sampling for the North basin of Lac St. Cyr in 1995 showing the change in DO with increasing depth from which a hypolimnion is discernible Water Quality Data Observed water quality data are the averaged euphotic values from the data provided on Lac St. Cyr (Table 4). Each basin requires an input of at least total phosphorous. For all other values where there is no data, a value of 0 can be applied and the model will ignore this parameter. The temporal scale of the nutrient data is based on the open water season. 14

16 Observed WQ South Basin North Basin Temporal scale Non Algal Turbidity (1/m) Months Turbidity Est. From Ch a + Secchi (1/m) N/A N/A N/A Reference Lac St. Cyr Unpublished Data (calculated) Total Phosphorous (ppb) Months Lac St. Cyr Unpublished Data Total Nitrogen (ppb) Months Lac St. Cyr Unpublished Data Chlorophyll A (ppb) Months Lac St. Cyr Unpublished Data Secchi Depth (m) Months Lac St. Cyr Unpublished Data Organic Nitrogen (ppb) Months Lac St. Cyr Unpublished Data Total P Ortho P (ppb) Months Lac St. Cyr Unpublished Data Hypolimnetic O 2 Depletion Lac St. Cyr Unpublished Data Months (ppb/day) (calculated) Metalimnetic O 2 Depletion (ppb/day) N/A N/A N/A Conservative Substance (ppb) Chloride Months Lac St. Cyr Unpublished Data Table 4: Seasonally averaged observed water quality data for the North and South basins of Lac St. Cyr in 1995 during the open water season of May to October from NSWA Lac St. Cyr unpublished data Segment Calibration The calibration factors are found and applied based on the full input of data into the model. Running it shows how the model predicts the nutrient concentrations and then these values are compared to the predicted and observed values. This is easily seen after running the model and choosing to see the plots, which will show the values and how close together or far apart they are. This is the last step of model implementation before developing phosphorous budgets and future scenarios. However, it is included here for the sake of keeping it within the segment data area. The calibration of the model is a process of choosing first which parameters need to be changed (such as total phosphorous, total nitrogen, chlorophyll a and Secchi depth) and then changing the calibration factor by a little bit each time to nudge the predicted values closer to the observed ones (Table 5). Each basin, or segment, has its own set of calibration factors, so each basin can be changed independently. However, changing the values in one basin can have an effect on the other, and changing any parameters that may be linked, such as chlorophyll a, Secchi, or total phosphorous, can affect the other as well. 15

17 Variables South Basin North Basin Dispersion Rate 1 1 Total Phosphorous Total Nitrogen Chlorophyll a Secchi Depth Organic Nitrogen 1 1 Total P Ortho P 1 1 Hypolimnetic O 2 Depletion 1 1 Metalimnetic O 2 Depletion 1 1 Table 5: Calibration factors for the Lac St. Cyr BATHTUB model 1995 case file showing which parameters were changed in order to bring the predicted values of the model calculations in line with the observed water quality parameters. *This allows for the model to be used to predict phosphorous budgets and future scenarios Internal Loadings In this part of the model, there is an option to account for how much of the nutrients may be loading into the lake from the bottom sediments. Usually this occurs because of a disruption of the sediments at the bottom of the lake, such as wind mixing and spring/fall turnovers. In the Lac St. Cyr model, a factor of 4.5 was applied to total phosphorous which would have been similar to the internal loading of Mayatan Lake 4. It was found however, that this factor was loading in over 500mg/m 3 of phosphorous, on top of the predicted values, making the model overestimate the amount of phosphorous in the lake. It was assumed then, that the sedimentation rate (the rate at which nutrients were accumulating at the bottom of the lake) was enough to offset the internal loading, reflected by the value 0. After removing the internal loading factor, the model was able to predict a more accurate value of total phosphorous (Table 6). It may be noted here that an internal loading for nitrogen may help to account for the underestimation of total nitrogen by the model. Before this was considered, the model was simply calibrated by applying a factor to the segment calibrations. 4 Mayatan State of the Watershed Report, NSWA. A similar lake to Baptiste and Lac St. Cyr that has values that could have been used for modeling Lac St. Cyr. It was decided however, that Baptiste Lake had sufficient data to use and keep calculations consistent. 16

18 Variables South Basin North Basin Conservative Substance 0 0 Total Phosphorous 0 0 Total Nitrogen 0 0 Table 6: Internal loading variables for Lac St. Cyr *No variables were used for 1995 as it was assumed that internal loading is negated by sedimentation rates Tributary Data Monitored Inputs Although Lac St. Cyr has only one natural stream flowing into the lake, it is intermittent and flows only during flood years. There are no natural outflows. The diversion pipes and watershed areas were considered to be tributaries. Five tributaries were thus created: The inflow diversion (NSR Inflow Pipe), the outflow diversion to St. Paul (LSC outflow to St. Paul), South Basin Runoff, North Basin Runoff, and Pond A Runoff landscapes that are draining into significant water bodies. Each tributary was assigned a type based on the description of each tributary (Appendix, section Tributaries, p ). The LSC outflow diversion was labeled a type 4 tributary, withdrawal or outflow, while the rest were deemed type 1, gauged tributaries. In type 1, inflow volumes and concentrations are directly measured or independently estimated by the model (Walker, 2006). Each tributary needs at least a Total Phosphorous and Flow Rate applied. The rest are optional if data does not exist or cannot be calculated (Walker Manual, ). If data is not available, a value of 0 can be applied. Flow rates and watershed areas were obtained from Figliuzzi Lac St. Cyr Water Balance (2013) (Table 7). The flow rates from Figliuzzi (2013) are in m 3, and therefore must be converted to hm 3 for the model. This is a simple calculation, as there are 1,000,000m 3 in 1hm 3. Therefore: X hm 3 = flow rate m 3 /1,000,000 5 Wwwalker.net BATHTUB Online Manual, Section Edit Tributaries. 17

19 Monitored Inputs Total Flow Watershed Rate Area (km 2 ) (hm 3 /yr) Total P Conc (ppb) Ortho P Conc (ppb) Total N Conc (ppb) Inorganic N Conc (ppb) Conservative Subs. Conc (ppb) Tributary Name Segment Type Mean Mean Mean Mean Mean Mean Mean NSR Inflow Pipe SB LSC Outflow to St. Paul NB Surface runoff to SB SB Surface runoff to NB NB Pond A Runoff NB Table 7: Tributary monitored inputs for nutrient concentration Total Phosphorous, Ortho phosphate, Total Nitrogen, Inorganic Nitrogen, and Conservative Substance in parts per billion (mg/m 3 ) from NSWA Lac St. Cyr data. *Total watershed area in km 2 and the flow rate (converted from m 3 to hm 3 ) are from Figliuzzi data. Values for the NSR inflow pipe were applied from the Lac St. Cyr pumphouse data. This data is taken from the pumphouse located just north of the river, before it is piped to Lac St. Cyr. These readings are usually taken in the winter months, at the same time that the water is pumped from the river to the lake (November to March). Since there is no spring pumping, the data from the pumphouse is assumed to be considered early spring values and applied to the model as the six month data regardless of the timing of sampling. The outflow pipe from Lac St. Cyr to the town of St. Paul is considered to be North basin water quality values with an outflow rate applied which the model will use to calculate how much of the nutrients are leaving the system and predict an overall lake balance. The surface runoff nutrient concentrations had to be calculated by hand by first obtaining land use area numbers and annual flow weighted mean (AFWM) values. This step occurred after land use data was obtained and input into the following Land use section, but will be explained here. The AFWM s were taken from the Baptiste Technical Report (Girhiny, 2007) 6 for total phosphorous, total nitrogen, ortho phosphate, inorganic nitrogen, and conservative substance (chlorine) (Table 8). These were averaged values from three yearly values ( ) three streams 7 in each land use type. 6 Total P and Total N AFWM values are from pages Ortho and inorganic AFWM values are from Appendix, A57 A59. 7 Agriculture streams: A,M, & N; forested streams: E, F, & L. No cottage AFWM values for Baptiste. 18

20 Land use Total Phosphorous (mg/l) Orthophosphate (mg/l) Total Nitrogen (mg/l) Inorganic Nitrogen (mg/l) Conservative Substance Cl (mg/l) Agriculture Forested Table 8: Annual flow weighted mean concentrations for Baptiste Lake, averaged from three streams in each land use (Agri, AMN; Forest, EFL) over a three year period ( ) in mg/l. The AFWM values were converted to mg/m 3 by multiplying the mg/l value by mg/l (ppm) x 1000 = mg/m 3 (ppb) These values were then multiplied by the percentage of land use in each basin to obtain concentration values for the above parameters. AFWM in ppb x land use % = ppb concentration Land use The watershed basins have specific land use types within them that have an effect on lake quality in regards to the external nutrient loadings into the lake from the land each year. Data was provided from NSWA in which the effective drainage area was divided into 11 subcategories of land uses (Figure 5). These ranged from the water body itself to different agriculture uses, different natural areas, and cottage land uses. The 10 different land types were combined into three main categories for the Lac St. Cyr model: Agriculture, Natural/Forest, and Cottages 8. The area was totaled and then compared to the total effective drainage area of 13.15km 2 (Figliuzzi, 2013, p. 6) to get a percentage of the total attributed to each type (Table 9). 8 Agriculture = Perennial crops/pasture + Barley + Wheat + Canola/Rapeseed; Natural/Forest = Shrubland + Wetlands + Grassland + Forest Conifers + Forest Deciduous; Cottages = Developed 19

21 Areal Extent of Land Cover Classes in the Effective Drainage Area of Lac Saint Cyr Watershed in % 15% 2% Water Developed Shrubland Wetlands 9% Grassland 1% 0% Perennial crops/pasture Barley Wheat Canola/Rapeseed 1% 7% 4% 26% Forest Conifers Forest Deciduous 1% Figure 5: Percentage of total effective drainage area attributed to each land use around Lac St. Cyr Tributary Drainage Area (km 2 ) Agriculture Natural/Forest Cottages Runoff to North Basin N/A Runoff to South Basin Pond A to North Basin N/A TOTAL Total Percentage of DA 37.9% 45.1% 1.8% Table 9: Tributary land use drainage areas around each tributary and the percentage of land taken up by each in the effective drainage area. *Data is from NSWA in which land use was reduced to three main components: agriculture, natural/forest, and cottages. 20

22 6.2.5 Export Coefficients The export coefficients (ECs) are used to estimate flow and concentrations for tributaries (Walker Online Manual 9 ). These parameters were calculated in excel using land use area data from the NSWA 10 and averaged EC values from the Baptiste Technical Report (1987, p. 264). Area was converted to hectares (ha) from km 2 for the purpose of easy unit conversion (Table 10). Once the ECs were charted in excel, the runoff values from each tributary were obtained from Figliuzzi and totaled together for each year in question to obtain a total runoff value for the effective drainage basin (Table 11). Parameter Agriculture Forest Cottages 11 Area (ha) Total Phosphorous (kg/ha/yr) N/A Orthophosphate (kg/ha/yr) N/A Total Nitrogen (kg/ha/yr) N/A Inorganic Nitrogen (kg/ha/yr) N/A Conservative Substance, Cl (kg/ha/yr) N/A N/A N/A Table 10: Area of land use type in hectares (ha) and averaged export coefficients for agriculture and forest land types from Baptiste Technical Report in kg/ha/yr. Year Runoff Total (m 3 ) Table 11: Total runoff values for the effective drainage area around Lac St. Cyr for each data year. 9 Wwwalker.net BATHTUB Online Manual, Section: Edit Export Coefficients. 10 Student data, Land use Excel Worksheet, tab Export Coefficients. 11 Cottages and conservative substance parameters were calculated separately due to a lack of data in Baptiste 21

23 After this, an excel formula was used to calculate the ECs for Lac St. Cyr for each year in question. EC Lac St. Cy yearx = ((Avg Baptiste EC x land use area LSC) x 1,000,000)/Runoff Total yearx *multiplying the value by 1,000,000 converts kg to mg To calculate the ECs for conservative substance and cottages, Whole Lake composite data was used from the NSWA to get the average nutrient concentrations in Table 12 for each year. These values were then multiplied by the area percentages of each land use type. For example: Total Phosphorous for 1995 whole lake = 23.7 mg/m 3 Land use area percentage for Cottage = 1.8% or TP * Cottage area = EC 23.7mg/m 3 * = mg/m 3 For conservative substance, the same was done. The total average for the whole lake was taken and multiplied by each land use area percentage to receive an EC value in mg/m 3. Runoff in m/yr was calculated by taking the total runoff values per year and cube rooting them to convert m 3 to m while multiplying them to the land use area percentage (Table 12). For example: 1995 Total runoff = 63498m 3 Percentage of Agriculture Land use = 37.9% or Runoff (m/yr) = (0.379 x 63498m 3 ) 1/3 = m/yr Land use Runoff (m/yr) Total P (mg/m 3 ) Ortho P (mg/m 3 ) Total N (mg/m 3 ) Inorganic N (mg/m 3 ) Conservative Substance, Cl (mg/m 3 ) Agriculture Natural/Forest Cottages Table 12: Export Coefficients for the 3 land use types: agriculture, natural/forest, and cottages Lac St. Cyr Channels Lac St. Cyr, although divided into two basins, was not considered to have a transport channel connecting them. Generally, channels are considered when additional flow and exchange between pairs of segments (i.e., if a segment discharged/exchanged with two or more segments) 12. As this was not the case, it was considered to be unnecessary Model Coefficients Model coefficients are like calibration factors, in that changing one of the parameters here by a factor will either increase or decrease that factor in the model. The difference however, is that these factors in the Model Coefficients section would be applied to the whole 12 Wwwalker.net BATHTUB Online Manual, Section Edit Channels. 22

24 lake, instead of the independent segments. This would be the area one would use to predict changes in whole lake inorganic parameters and observe their effect. For example, changing the chlorophyll a coefficient from default (1.0) to 0.6 would decrease the predicted value by 60% of the original estimates, regardless of which model selection is used (Walker Online Manual 13 ). 6.3 Model Running The last step in implementing the BATHTUB model is to run the model and compare the predicted and observed results (see Segment Calibration). Once each year has been calibrated, it is usually apparent which year would be best for modeling budgets and future scenarios. After deciding on a year, the calibration factors from that year are applied to each other case file. For Lac St. Cyr, it was decided that 1995 was the best year to use for calibration because, while it underestimates the nutrient loadings for the years prior, it is the closest reflection of current conditions (lower phosphorous loadings and diversion rates). Thus, 1995 calibration factors were then applied to each case file so observe the changes in nutrient balances over time. The next step was to take the most recent five years of water and phosphorous data from Figliuzzi and Pakan Data Station ( ) 14. By averaging the five most recent years of diversions in and out of the lake and the TP data, we can input that into a new case file with all the other information from 1995 in order to establish a current baseline file 15. Once that was established, case files were created for future scenario predictions. The NSWA asked for a future projection over the next 20 years with increases of 2% diversions in and out of the lake to reflect a growing population base and subsequently and increase in water usage, while holding the baseline TP data. Case files for every five years until 2033 were created, and 2% increase each year in diversion and withdrawal were reflected in each case (Table 13). Year Diversions into Lake (m 3 ) Diversions out of Lake (m 3 ) ,394, , ,539, , ,700,028 1,098, ,876,969 1,212, ,072,325 1,339,050 Table 13: Future diversions in and out of the lake at a rate of increase of 2% per year 13 Wwalker.net BATHTUB Online Manual, Section Edit Model Coefficients 14 Provided by the NSWA 15 Lac St. Cyr BATHTUB case file

25 7 Results and Discussion Calibration Year The model was run and calibrated for each of the years , 1991 and 1995 to see which would calibrate the most accurately. The 1985 and 1995 years seemed to be the best. We decided to use 1995 as the calibration year as 1995 was the most recent data year that would best reflect the current state of Lac St. Cyr (Figure 6). After calibrating the model for 1995, the same calibration factors were applied to all years, and also to the years which represent future scenarios, in order to obtain nutrient budgets. When the calibration factors were applied to each year, the predicted and observed values were generally close. Looking at the chlorophyll a concentrations, we see that the 1995 calibration factors when applied showed the most discrepancy between predicted and observed values, as seen in Figure 7. The difference in these values may be explained by the fact that the observed chlorophyll a values for 1984, approximately 18 mg/m 3, were quite a bit higher than the predicted values of 8 mg/m 3. This would mean that the 1995 calibration factors are generally underestimating the predicted values for the years prior except total phosphorous in 1984 (Figures 8 and 9). This is not a troublesome result but shows that applying a calibration factor from a stable year to a higher nutrient loading year will not be able to produce a perfect model. BATHTUB was also unable to produce results for the year The error message from the model states there was a negative concentration balance for segment 1 North Basin. However we were unable to determine the cause of this error due to limited time. Results for & 1995 were obtained from BATHTUB. 24

26 A) B) C) D) Figure 6: 1995 data applied calibration factors for a) total phosphorous, b) total nitrogen, c) chlorophyll a, and d) Secchi disk depth Figure 7: Calibration factors from 1995 applied to years for chlorophyll a. *In the year 1984, the observed and predicted values where the farthest apart, as 1984 have the highest nutrient levels due to the accumulated effect on the lake from diverted river water. 25

27 Total Phosporous (ppb) Observed Total Phosporous vs. Predicted Total Phosporous predicted TP observed TP Years Figure 8: Observed TP vs. Predicted TP in Lac St. Cyr. *Since 1995 was used as a calibration year because of its more stable conditions TP from was underestimated with the exception of Observed Total Nitrogen vs. Predicted Total Nitrogen Total Nitrogen (ppb) predicted TN observed TN Years Figure 9: Observed TN vs. predicted TN in Lac St. Cyr. *Since 1995 was used as a calibration year because of its more stable conditions, TN from was underestimated. 26

28 7.2 Water quality After obtaining results from the model, it was noted that there was some fluctuation in phosphorous levels during the 80 s. We can see this in the total phosphorous/total nitrogen figures below. The 1984 year had the highest predicted nutrient loadings during this period but has since been decreasing in both TP and TN (Figures 10 & 11). The increase in predicted TP from 1983 to 1984 was 13 ppb, showing 1984 as the most impacted year. This could be attributed to accumulative effects of TP on the lake as well as an increase in the amount of TP in the river from ppb in 1983 to ppb in 1984 (Figure 12). In Table 14 below, it can be seen that the North Saskatchewan River is the highest contributor of phosphorous to Lac St. Cyr. This affirms that river water quality would have a significant impact on the water quality of the lake. However, observed values for TP indicated that 1985 was the most impacted year in the lake. Because 1984 had the largest nutrient loadings in the North Saskatchewan River, the effects would have been seen during the open water season in The nutrient data that was applied to the model from the pumphouse may not have been averaged correctly. In the model, for the year 1984, we included January, February, and November, December data of that year. Instead we perhaps should have only included January and February of 1984 as well as October to December of This would have better reflected the amount of nutrients that were loaded into the lake at the same time water diversions were occurring. 40 Total Phosporous concentrations from and 1995 Total Phosporous (ppb) Years Figure 10: Change in total phosphorous from and 1995 years. *These values were the predicted output values from BATHTUB. 27