California Snowpack Levels Visualization

Posted In: Environment | Water

How does the current California snowpack compare with Historical Averages?

Check out the California reservoir dashboard and the California precipitation visualizer and combined reservoir and snowpack storage dashboard.

If you are looking at this it’s probably winter in California and hopefully the snowy in the mountains. In the winter, snow is one of the primary ways that water is stored in California and is on the same order of magnitude as the amount of water in reservoirs.

When I made this graph of California snowpack levels (Jan 2023) we’ve had quite a bit of rain and snow so far and so I wanted to visualize how this year compares with historical levels for this time of year. This graph will provide a constantly updated way to keep tabs on the water content in the Sierra snowpack.

Snow water content is just what it sounds like. It is an estimate of the water content of the snow. Since snow can have be relatively dry or moist, and can be fluffy or compacted, measuring snow depth is not as accurate as measuring the amount of water in the snow. There are multiple ways of measuring the water content of snow, including pads under the snow that measure the weight of the overlying snow, sensors that use sound waves and weighing snow cores.

I used data for California snow water content totals from the California Department of Water Resources. Other California water-related visualizations include reservoir levels in the state as well.

There are three sets of stations (and a state average) that are tracked in the data and these plots:

  • Northern Sierra/Trinity – (32 snow sensors)
  • Central Sierra – (57 snow sensors)
  • Southern Sierra – (36 snow sensors)
  • State-wide average – (125 snow sensors)

These stations are tracked because they provide important information about the state’s water supply (most of which originates from the Sierra Nevada Mountains). Winter and spring snowpack forms an important reservoir of water storage for the state as this melting snow will eventually flow into the state’s rivers and reservoirs to serve domestic and agricultural water needs.

The visualization consists of a graph that shows the range of historical values for snow water content as a function of the day of the year. This range is split into percentiles of snow, spreading out like a cone from the start of the water year (October 1) ramping up to the peak in April and then converging back to zero in summertime. You can see the current water year plotted on this in red to show how it compares to historical values.

My numbers may differ slightly from the numbers reported on the state’s website. The historical percentiles that I calculated are from 1970 until 2022 while I notice the state’s average is between 1990 and 2020.

You can hover (or click) on the graph to audit the data a little more clearly.

Sources and Tools

Data is downloaded from the California Data Exchange Center website of the California Department of Water Resources using a python script. The data is processed in javascript and visualized here using HTML, CSS and javascript and the open source Plotly javascript graphing library.

California Snow Visualization


8 Responses to California Snowpack Levels Visualization

  1. Can you convert this to a total volume of water stored in the snowpack?

  2. Nicolas Lucich says:

    What does the peak of the graph represent… the 100?

    • chris says:

      The 100 refers to the 100th percentile of the historical period, i.e. the maximum reached during that period. So in several regions, the snow water content is higher than it ever was from 1970 to 2022.

  3. Josh says:

    How easy is it to get historical data? I would love to answer this question: Of the years where the SWC on January 15 was more than 100 percent of the median April 1 value, what was the April 1 SWC?

  4. Peter Harrison says:

    I’m a retired numbers guy and this is an absolutely fabulous display of data. It’s perfect. Thank you for this.

  5. Rosemary Plue says:

    Thank you. This gives we less to worry about.

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