There are between 1000 and 2000 species of cyanobacteria (blue-green algae) worldwide that can colonize to form a Harmful Algal Bloom (HAB) (Krogmann 1981). Degraded water quality from increased nutrient pollution promotes the development and persistence of many HABs and is one of the reasons for their expansion and proliferation in aquatic environments. HABs are unfortunately a growing problem in Canada over the last few years. Several cases of blue-green algae blooms have been reported in Manitoba’s Lake Winnipeg over the past few years. Algal blooms in Lake Winnipeg have increased in intensity and frequency with accumulation of phosphorus and nitrogen nutrients in the lake (Manitoba Water Stewardship 2011). Multiple public health advisories
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This includes: a) nutrients in the lake, brooks and tributaries flowing into the lake and b) the TP in soil samples of the watershed, which might contribute mainly to the high TP level in the lake.
2) To observe and determine the biological consequences of these nutrient levels in the lake and related watershed via two important parameters: Chlorophyll-a and Phycocyanin.
3) Based on the data determined, interpolated and validated by mapping on the entire study area with the GIS approach, we try to evaluate the spatial temporal evolution of the above-mentioned parameters for the HAB occurrence.
4) The last goal is a trial to predict the future bloom scenarios by using a mathematical modeling approach. As mentioned previously, the pattern of massive blooms was just recently appearing (since 2014) and we just started a systematic sampling process for data since June 2015. With the data for this summer season from June to October, we try to simulate and predict different bloom scenarios for the future based on an Artificial Neural Network (ANN)
This includes: a) nutrients in the lake, brooks and tributaries flowing into the lake and b) the TP in soil samples of the watershed, which might contribute mainly to the high TP level in the lake.
2) To observe and determine the biological consequences of these nutrient levels in the lake and related watershed via two important parameters: Chlorophyll-a and Phycocyanin.
3) Based on the data determined, interpolated and validated by mapping on the entire study area with the GIS approach, we try to evaluate the spatial temporal evolution of the above-mentioned parameters for the HAB occurrence.
4) The last goal is a trial to predict the future bloom scenarios by using a mathematical modeling approach. As mentioned previously, the pattern of massive blooms was just recently appearing (since 2014) and we just started a systematic sampling process for data since June 2015. With the data for this summer season from June to October, we try to simulate and predict different bloom scenarios for the future based on an Artificial Neural Network (ANN)