Great Lakes Water Level Data Dive
Long-term trend analysis using real NOAA water level records.
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Standards
HS-ESS3-6 — Use a computational representation to illustrate the relationships among Earth systems and how those relationships are being modified due to human activity.
Why Does This Matter?
The Detroit River links Lake St. Clair and Lake Erie, carrying the outflow of the upper Great Lakes, so Detroit's shoreline infrastructure, shipping, and flood risk all depend on Great Lakes water levels — levels that swing through natural cycles but are increasingly affected by climate patterns.
Objective
Graph and interpret decades of real Great Lakes water level data to identify trends and connect them to climate patterns and infrastructure planning.
Materials
- Dataset: detroit_river_water_level_annual_1970_2024.csv (annual mean water level in meters, NOAA CO-OPS Fort Wayne gauge on the Detroit River). A monthly version is also available: detroit_river_water_level_monthly_1970_2024.csv
- Graph paper or graphing/spreadsheet software
- Ruler
- Calculator
Note: This is a data analysis lab — no physical safety hazards.
Procedure
- Open the annual-mean dataset (1970–2024) for the Detroit River at Fort Wayne in a spreadsheet. This gauge records the water level of the river that connects Lake St. Clair and Lake Erie.
- Plot water level (y-axis) vs. year (x-axis).
- Identify the highest and lowest water level years in your dataset and calculate the total range of fluctuation.
- Calculate a simple multi-year moving average (e.g. 5-year average) to smooth out short-term swings and reveal the longer-term trend.
- Compare your graph's pattern to provided notes on regional precipitation and temperature trends over the same period.
Data Table
| Year range | Average water level | 5-year moving average | Notable event/trend |
|---|---|---|---|
Analysis Questions
- Describe the overall pattern in your graph. Do water levels rise and fall in cycles, trend steadily in one direction, or both?
- What is the total range between the highest and lowest water levels in your dataset? Why does that range matter for shoreline construction?
- How did your 5-year moving average change the shape of the graph compared to the raw yearly data? Why is a moving average useful for spotting long-term trends?
- Based on your graph and the provided climate notes, what risks might rising or falling Great Lakes water levels pose to Detroit's riverfront infrastructure?