Evaluation of Lake Simcoe watershed rainfall characterisics

Lake Simcoe Region Conservation Authority is monitoring the phosphorous loading in Lake Simcoe and to understand the changes in phosphorous loading due to runoff, it is prudent to characterize the rainfall data of the watershed contributing to Lake Simcoe. In this project, hourly and daily rainfall data from 13 different raingage statistics surrounding Lake Simcoe was analyzed to identify event, monthly, seasonal and annual statistics and their trend and thereby to identify the driest and wettest and average annual rainfall. After initial analysis, daily rainfall data from only 4 stations with consistent data for an approximate period of 20 years were chosen for further analysis. The results showed that hydrological year 1995-1996 was the wettest and hydrologic year 1991-1992 was the driest year. Similarly summer season and the month of June were the wettest and winter season and month of February were the driest for the watershed. No significant trend was observed in the yearly and monthly rainfall data while an increasing trend was observed at 3 stations for the winter season.

Evaluation of Lake Simcoe watershed rainfall characterisics

Lake Simcoe Region Conservation Authority is monitoring the phosphorous loading in Lake Simcoe and to understand the changes in phosphorous loading due to runoff, it is prudent to characterize the rainfall data of the watershed contributing to Lake Simcoe. In this project, hourly and daily rainfall data from 13 different raingage statistics surrounding Lake Simcoe was analyzed to identify event, monthly, seasonal and annual statistics and their trend and thereby to identify the driest and wettest and average annual rainfall. After initial analysis, daily rainfall data from only 4 stations with consistent data for an approximate period of 20 years were chosen for further analysis. The results showed that hydrological year 1995-1996 was the wettest and hydrologic year 1991-1992 was the driest year. Similarly summer season and the month of June were the wettest and winter season and month of February were the driest for the watershed. No significant trend was observed in the yearly and monthly rainfall data while an increasing trend was observed at 3 stations for the winter season.

Long-term water quality monitoring in Lake Simcoe based on the empirical method and machine learning

<p><strong>Abstract:</strong> The traditional water quality monitoring methods are time-consuming and laborious, which can only reflect the water quality status of single point scale, and have some problems such as irregular sampling time and limited sample size. Remote sensing technology provides a new idea for water quality monitoring, and the temporal resolution of MODIS is one day, which is suitable for long-term, continuous real-time large-scale monitoring of lakes. In this study, Lake Simcoe (located in Ontario, Canada) was selected as the research area. The long-term spatiotemporal changes of chlorophyll-a, transparency, total phosphorus and dissolved oxygen were analyzed by comparing the empirical method, multiple linear regression, random forest and neural network with MODIS data. Finally, the water quality condition of Lake Simcoe is evaluated. The results show that the overall retrieval results of two machine learning models are better than that of the empirical method. The optimal retrieval accuracy R² for four water quality parameters are 0.976, 0.988, 0.943, 0.995, and RMSE are 0.13μg/L, 0.3m, 0.002mg/L and 0.14mg/L, respectively. On the annual scale, the annual mean values of the four water quality parameters during the 10-year period from 2009 to 2018 were 1.37μg/L, 6.9m, 0.0112mg/L and 10.17mg/L, respectively. On the monthly scale, chlorophyll a, total phosphorus and dissolved oxygen first decreased and then increased at the time of year. The higher concentrations of chlorophyll a and total phosphorus in the south and east of Lake Simcoe are related to the input of nutrients from the surrounding residents and farmland.</p><p><strong>Key words: </strong>water quality monitoring; MODIS; empirical method; machine learning</p>

Modeling of Low Impact Development Nutrient Reduction Performance in the Lake Simcoe Watershed

<p>Stormwater quality management has evolved from traditional centralized downstream control devices (e.g. ponds and wetlands) to distributed low impact development practices (LID) at the source (e.g. bioretention, porous pavement, greenroof).  In order to develop master LID plans for municipalities in the Lake Simcoe watershed (3576 km<sup>2</sup>), a new modeling approach was developed.  The challenge of modeling small scale LID practices over a watershed scale was resolved using unit response functions (URF) of different types of LID.  The concept of URF is based on the linear assumption of LID performance on a watershed level where routing is not important.  Detailed URF of runoff and nutrient reduction were developed on a lot level using US EPA SWMM models and linked with lot level characteristics such as imperviousness percentage.  The process of modeling include: (1) screening of appropriate LID across the watershed based on identification of unsuitable areas (e.g. wellhead protection area, NaCl concentration, industrial land use) and prioritization suitable lots which maximize environmental benefits and demonstration potential; (2) development of hydrological unit response functions of each type of LID (i.e. average annual runoff and nutrient loading reduction) using US EPA SWMM models; (3) aggregation of the cumulative runoff and nutrient reduction of all appropriate LID at each municipalities; (4) cost-effective analysis of different combinations of LID (i.e. Pareto front); (5) recommendation of the preferred LID combinations for each municipal within the watershed .  Results of the modeling indicate that (1) the average annual runoff volume reduction of implementing LID for the uncontrolled urban areas in Lake Simcoe watershed is estimated to be between 20% and 33%; and (2) the average annual phosphorus reduction of implementing LID for the uncontrolled urban areas in Lake Simcoe watershed is estimated to be between 2.0 to 2.7 tonnes per year.  This study has demonstrated a new modeling approach of small scale LID over watershed scales. </p>