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Leggett Labs / Consumer data application

LakeScout

What does that lake level actually mean?

A gauge reading gives you a number. LakeScout adds context: how the level compares with the usual range for the season, how it has changed recently, and which source supports the information.

LakeScout map of western and central Canada with coloured water-level markers and a legend for normal, higher, unusually high or low, lower, and historical-only readings.
Water-level map

Screenshot supplied by Ben on September 22, 2026. A dated interface example, not live water levels or a forecast. Coverage and data availability vary by lake.

Built by
Leggett Labs
Made for
Understanding Canadian lake levels
Format
Web application

The need

A measurement is only the starting point.

A lake’s water level means more when you can compare it with the season and its recent history.

A raw reading alone does not tell you whether the level is typical for that lake at that time of year. LakeScout brings those comparisons into a readable lake-by-lake view.

The application

From gauge readings to useful context.

01

Find a lake

Search by name or browse the available lakes by province and territory.

02

Read the seasonal context

Where sufficient data is available, see how the reported level compares with the lake’s usual seasonal range and review the seven-day direction and change.

03

Look behind the summary

Open technical details for the source station, observation and snapshot dates, historical coverage, and confidence information.

How it works

Find the lake. Read the context. Check the source.

  1. Choose a lake

    Start with a name search or browse the available regional listings.

  2. Read its water-level summary

    Review the seasonal comparison and recent trend, where sufficient data is available.

  3. Explore the supporting detail

    Check the recorded dates, historical coverage, and source station behind the summary.

The Leggett Labs contribution

A clear interface for complex data.

LakeScout brings lake discovery, seasonal comparisons, recent trends, and source information into one consumer-facing web application.

For Leggett Labs, it is an example of the work involved in making data useful: organizing it around a person’s question, explaining the result, and keeping the supporting detail available.

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