Title

SPATIAL PATTERNS OF ALGAL BLOOMS IN LAKE BLOOMINGTON AND EVERGREEN LAKE

Publication Date

4-5-2019

Document Type

Poster

Degree Type

Graduate

Department

Geography, Geology and the Environment

Mentor

Wondwosen Seyoum

Mentor Department

Geography, Geology and the Environment

Co-Mentor

William Perry

Co-Mentor Department

Biological Sciences

Abstract

Fresh water is one of the most important sources of drinking water for the United States population and when our water is polluted, it is not only devastating to the environment but also to human health. Algal blooms can cause harmful effects to freshwater ecosystems such as pollution of beaches, taste and odor problems in drinking water, and depletion of oxygen levels causing fish kills. They can have negative effects on the health of humans as well as other animals who use them for drinking or recreation. Algal blooms have been a growing water pollution problem in the Midwest, causing contamination of major reservoirs from which cities and towns draw drinking water. Algal blooms occur in freshwater when there is a sudden rise in the population of algae found in the water body and it causes the color of the water to change. The objective of this research project is to examine the spatial patterns of algal blooms as well as their effect on water quality in Lake Bloomington and Evergreen Lake - the two reservoirs from which the City of Bloomington draws its water for water supply. The Bloomington water-supply system currently supplies over 80,000 people in the city of Bloomington, Hudson & Towanda Townships and half of the population of Dale and Dry Grove townships. This project explores the effects of algal blooms in water and the environment by using remote sensing and field work data to monitor algal bloom occurrence. Methods that are transferable and will enable the determination of algal bloom occurrence at other locations will be developed. Monitoring of lakes using satellite remote sensing data is useful in estimating and detecting water quality problems that would have gone undetected in lakes. Water samples will be collected from selected locations on the lakes to test for various water properties such as nitrate, phosphate, chlorophyll a, etc. A function derived from regression analysis conducted alongside with models/maps created will be used to predict water quality of the other locations of the lake not sampled. Results have shown that blooms occur at different times of the year in each lake e.g. August for Evergreen Lake, October for Lake Bloomington. Using satellite image reflectance data from Landsat 8 images, we expect to see spatial patterns in water quality.

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