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Dirty Water, Environmental Science Data Nugget Assignment
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Data Nuggets developed by Michigan State University fellows in the NSF BEACON and GK-12 programs Love that dirty water Featured scientists: Jonathan Thompson from Harvard University and Drew Guswa from Smith College. Written by Tara Goodhue and Joshua Plisinski. Research Background: Forests, wetlands, and other green spaces are natural filters for water; water is cleaned as it is used by plants and travels through soils. As green spaces are lost to make room for homes and businesses, ecosystems are less able to provide this service. Without natural filtration from green spaces, humans must build expensive water treatment systems or risk drinking contaminated water. Impervious surfaces , like roads, buildings, and parking lots, do not allow water to pass through. When it rains or snows on an impervious surface, water cannot soak into soil or be used by plants. Instead, it quickly Drew and students measuring river flow rate. flows into nearby streams and rivers. If too much water runs off too quickly, it overwhelms local sewer systems, getting into rivers before it can be filtered. This dirty water may carry human waste and toxic materials. Impervious surfaces have become a major problem for both the health of river ecosystems, and the health of people who depend on them as a clean source of drinking water. How land is used in a watershed , or the network of land and rivers that flow to a single point as they empty out into the ocean, is an issue of great concern. Jonathan is a scientist studying land use. He became interested in science after traveling around the country and working as a wilderness ranger and wildland firefighter. At the Harvard Forest, members of his lab study how land use decisions affect the environment. They used computer simulations to create maps of what New England’s landscape could look like under different possible futures. Their web-tool is called the New England Landscapes Futures Explorer. Jonathan’s lab works with Drew, a civil and environmental engineer who loves biking and hiking. Drew and his lab at Smith College are interested in the relationship between land use and water. Together, Jonathan and Drew’s labs teamed up to study how future increases in impervious
Data Nuggets developed by Michigan State University fellows in the NSF BEACON and GK-12 programs surfaces from new development could affect water quality in New England.
Data Nuggets developed by Michigan State University fellows in the NSF BEACON and GK-12 programs Watershed Map: Watershed 1 , the Charles River watershed, includes the City of Boston. Boston is a very old city, and most of its development happened a long time ago. The Charles River watershed is in the most densely populated part of New England. Watershed 2 , the Cocheco Watershed is in Southern New Hampshire. This is one of the most rapidly developing parts of New England. This means that forests and fields are being cut down for houses, roads, and businesses.
Data Nuggets developed by Michigan State University fellows in the NSF BEACON and GK-12 programs Interpret the figure: These watersheds are similar to the Merrimack in some ways, but different in others. It is up to you to justify which watershed you think is most similar, and use the annual maximum daily flow data from that watershed to make your prediction for the Merrimack. Which Watershed in the map above is most like the Merrimack River Watershed and in what ways is it similar and different? In my opinion, watershed 1 is the most similar to the Merrimack River Watershed. Looking at the map, you can see that they have similar densities of development. Both watersheds have large areas of red. However, they may be different in other ways as Watershed 1’s development is old and is mostly done developing. This may be different from Merrimack watershed but there is not enough information provided. Scientific Data: Use the simulated data from the web-tool below, and the watershed map above, to answer the scientific question: Watershed Land use scenario (2010-2060) Historical 2010 percent impervious surfaces Simulated 2060 percent impervious surfaces Percent change impervious surfaces from 2010 to 2060 Historical 2010 average maximum daily flow (cms) Simulated 2060 average maximum daily flow (cms) Percent change maximum daily flow from 2010 to 2060 Watershed 1 (near Boston) Recent Trends Continue 13.1 18. 41.2% 40.4 41. 3.5% Watershed 1 (near Boston) Low Development Future 13.1 18. 39.7% 40.4 41. 2.5% Watershed 1 (near Boston) High Development Future 13.1 34. 159.5% 40.4 44. 9.4% Watershed 2 (Southern NH) Recent Trends Continue 5.3^ 7. 47.2% 32.0 32. 2.2% Watershed 2 (Southern NH) Low Development Future 5.3 5. 9.4% 32.0 32. .3% Watershed 2 (Southern NH) High Development Future 5.3 27. 409.4% 32.0 34. 8.4% * Percent (%) change is calculated as the rate of change in impervious surfaces and maximum daily flow between
Data Nuggets developed by Michigan State University fellows in the NSF BEACON and GK-12 programs What data will you graph to answer the scientific question? Independent variable(s): Percent Change of Impervious Surfaces Dependent variable(s): Precent change of maximum daily flow Below is a graph of the data : Identify any changes, trends, or differences you see in your graph(s). Draw arrows pointing out what you see, and write one sentence describing what you see next to each arrow. 10 9 8 7 6 5 4 3 2 1 0 0 50 100 150 200 250 300 350 400 450 Percent change impervious surfaces from 2010 to 2060 Watershed 1 Watershed 2 Percent change maximum daily flow from
to
Positive correlation between small change in precent change of impervious surfaces and precent change in maximum daily flow. Positive correlation between increasing precent change of impervious surfaces and precent change in maximum daily flow. Small change in impervious surfaces means small change in maximum daily flow.
Data Nuggets developed by Michigan State University fellows in the NSF BEACON and GK-12 programs Interpret the data: Make a claim that answers the scientific question, how will development decisions impact the future of the Merrimack River and the health of humans that rely on it? Using regular or high levels of development decisions will increase impervious surfaces and thus increase maximum daily flow into the river impacting it’s future due to the toxic waste and materials that are unable to be filtered by green spaces while using low levels of development means less levels of impervious surfaces and thus a smaller change in maximum daily flow, benefiting river health. What evidence was used to write your claim? Reference specific parts of the table, graph, or map. Looking at the positive arrows on the graph, there is a clear positive correlation between precent change in impervious surfaces and maximum daily flow. The data in the chart shows similarities between watershed 1 and 2 which support my written claim. High development future is predicted to have the most percent increase in both impervious surfaces and percent change in maximum daily flow. For watershed 1, this is 159.5% and 9.4% respectively. For watershed 2, this is 409.4% and 8.4% respectively. On the other hand, low development future is predicted to have the least percent increase in both impervious surfaces and precent change in maximum daily flow. For watershed 1, this is 39.7% and 2.5% respectively. For watershed 2, this is 9.4% and 2.3% respectively. Explain your reasoning and why the evidence supports your claim. Connect the data back to what you learned about how development may lead to dirty water getting into rivers, and how this connects to human health. This evidence supports my claim since there is a clear positive correlation between an increase in development, percentage increase of impervious surfaces, and percentage increase in maximum daily flow. This is shown by both the percentages in the chart as well as the graph. This data connects back to what I learned about development since impervious surfaces are built with development (roads, buildings, parking lots). Building these impervious surfaces gets rid of green spaces like forests and wetlands which help filter water. Impervious spaces are unable to let water through, so instead of getting to plants or soil in flows into sewer systems to fast to handle and carries toxic waste and materials into rivers. This can connect to human health since we use rivers as a source for clean drinking water.