California’s Snowpack and Reservoirs

Recent headlines told a dramatic story. By the end of May, California’s snowpack was “zero percent of normal,” and another paper reported “California Snowpack Survey Canceled” due to a lack of snow. I knew it had been a bad snow year, and that the Sierra Nevada usually has some skiable slopes into June and July. But zero percent of normal? What does that mean? How much snow is there usually?

To answer this question, I used a computer model provided by NOAA’s National Operational Hydrological Remote Sensing Center — the Snow Data Assimilation System (SNODAS), which takes weather data and combines it with satellite observations to develop a detailed estimate for how much snow is on the ground everywhere in the country, every day of the year.

The model isn’t perfect. It’s only available for the past 12 winters, and the SNODAS website cautions against using the model to estimate snowfall from specific storms. Moreover, according to Dr. Jeff Dozier, a University of California Santa Barbara professor who researches snow, the model can underestimate snowpack in higher elevations by as much as 20 percent. Nonetheless, he and other researchers I spoke with agreed it was good enough for a statewide estimate of snowpack.

With these caveats in mind, I used the model data to calculate the amount of snow on the ground in California each day for the past twelve years. The results are striking but not too surprising. The winter of 2010-2011 was awesome. The past two have been horrible.

The y axes is measured in “acre-feet” – an acre-foot is the amount of water needed to flood one acre of land in one foot of water, and this graph shows the volume of water we’d have if you melted all of the mountains’ snow on any given day. At the end of March in 2011, there was more than 37 million acre-feet of snow in the mountains – enough liquid to flood all of LA county in 12 feet of water. This year, at the end of March, there was only about one million acre-feet – less than one 30th the amount of snow as 2011.

Snowpack serves a vital function for California’s water supply. The vast majority of California’s precipitation falls as rain and snow in the winter months. The rain fills the reservoir, while the snow accumulates in the mountains, effectively acting as another reservoir. In the spring and summer months, this snowpack gradually releases the water to the rest of California.

How does the water in snowpack compare to our reservoirs? To answer this, I downloaded daily water levels of California’s major reservoirs (I did this with a small team at a water-focused hack-a-thon). Below is a graph of 39 reservoirs, representing more than 95 percent of California’s total reservoir capacity. (Each shade of blue is a different reservoir — roll your mouse over the chart to see the names of each reservoir.)

Each winter and spring, the reservoirs fill because of rain and snowmelt. And then each summer and fall they are drawn down to provide water for our cities and farms. But in successive years of drought, the reservoirs don’t fill, and you can see that we’re currently on a downward sloping staircase. This year we have less than half as much water as we did in 2011.

Combining snowpack and reservoirs gives us a sense of the total water we have “stored” at any given time.
The result is a bit scary. The total water stored in our reservoirs and snowpack peaked at nearly 60 million acre-feet at the end of March in 2011. Currently, it’s less than 10 million.

In a good year, the snowpack stores as much water as all of California’s reservoirs combined. You can also see how melting snow fills the reservoirs every year — in most years, the reservoir levels climb as snowpack decreases in the spring. In a normal year, melting snow fills our man-made lakes until the beginning summer. In 2011, the reservoir levels kept rising until July. This year, there was almost no snow and we have none of that water. Reservoir levels peaked at the end of March and have been declining ever since.

These charts tell only a part of California’s water story. A significant amount of southern California’s water comes from the Colorado River, which is fed by rain and snow falling many thousands of miles away. Also, the state obtains a much of its water from groundwater, with groundwater accounting for the majority in drier years. This groundwater is “mined” — that is, it’s being used faster than it’s being replenished. Stanford’sWater in the West, a collaboration between the Woods Institute for the Environment and the Bill Lane Center for the American West, has a series of excellent articles and infographics on just how fast we are depleting our groundwater.

It’s possible that a few years of good rain and snow could replenish our water supplies, much as happened between 2008 and 2011. And it’s possible that an El Niño this year could give us another year like 2011 (but that could bring its own problems). Regardless, we’ll likely need a few years of above average precipitation to refill our state’s water storage.

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All The World’s Power Plants

When traveling across China by bicycle, I was amazed by how we seemed to pass a new power plant almost every day. I found myself wanting a map of where the power plants were in the country. Are they everywhere, or only where we were riding?

I found an online database of power plants from While on a train ride in western China, I wrote a Python script to download the entire database and then make a map of where the power plants were located — and I scaled them both by the amount of carbon and electricity they produced. Go to the interactive map of our trip and click on the link “See Power Plants in China.” One reason we passed a lot of power plants is that we followed the Yellow River, and power plants are often located along rivers. Another reason we passed so many power plants is that there are a lot of power plants in China.

Screen Shot 2015-05-17 at 1.36.59 PM

The Carma datset is global, so I also made a map of the entire world. The following two maps show the Carma dataset. The first map (blue markers) shows power plants scaled by the amount of electricity they produce. The second map shows power plants scaled by their carbon emissions. A few things jump out at me from these maps. The biggest difference between the two maps is in hydro-power plants in the world. South America has a number of hydropower plants that produce very large amounts of electricity (and thus are big blue dots), but almost no carbon (and thus are absent from the carbon emissions map). It is also amazing how much of the world produces very little energy.

Power Plants Scaled by Energy Production

Power Plants Scaled by Energy Production – From

Power Plants Scaled by Carbon Emissions

Power Plants Scaled by Carbon Emissions – From

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Interactive Map of Ride for Climate Asia

On Lindsey’s and my recent journey across Asia, I was sure to record every day on our Garmin GPS. This not only helped me get a few “King of the Mountains” on Strava, but more importantly, it allowed us to build a detailed map of our route.

Using a Python library that parses Garmin .fit files, I was able to extract the information from each day’s ride and then plot it on an interactive map using OpenLayers. I then built an elevation profile using d3. Click on the image below to explore! You can access blog posts about any part of the trip by clicking on the yellow markers. I also added overlays of population density, water stress (taken from WRI’s Aqueduct), and the location of power plants in China — just click on the links above the map on the right.

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There are a few things I’d like to improve with this map. For one, it includes a lot of data points — about one per kilometer, which is more than 13,000. As a result, the map runs slowly on most people’s computers. It would be nice if I could show a lower-resolution version of our route when zoomed out, and then dynamically show higher resolution as you zoom in. Secondly, the elevation profile is a bit unintuitive — I show the profile for the sections that we biked, not the parts that we bussed, trained, or hitchhiked. As a result, when we took a train to Tibet and then started biking, it looks like we went straight uphill 16,000 feet. Needless to say, we didn’t do that.

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All the World’s Reservoirs

Here’s a little project I’ve been working on: All of the world’s reservoirs, as seen by the GRanD database. I think it still needs a few tweaks — let me know if anything looks off.

Reservoir capacity is proportional to the size of each dot. Note that capacity is measured as the extra capacity created by a manmade dam. For instance, Lake Victoria in Africa is here because there is a dam on it — but the “capacity” includes only the few feet at the top of the reservoir that can be controlled by the dam.

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Fun with Python and Basemap

I recently re-edited my film Ten Tips for Biking Eastern Europe to submit to the Banff Film Festival (yes, a reach, but why not?). I had to alter the film because the old version uses Google Earth to show our bike route, and this is, of course, copyrighted. So I had to make a non-copyrighted map.

To make a new map, I took the KML file of my bike route (which I produced by combining all of the GPS files from the route), and using the Python library matplotlib and the toolkit extension basemap, plotted the route on NASA’s “blue marble” image of the earth, which is an image of the entire earth created by combining cloud-free satellite images. According to my sources, I can use this NASA image as my map background as long as I attribute it. (Note: the image used in the map background below is brought to you by NASA).


Pretty, eh? I wish the blue marble background image were a bit higher resolution, but I think it looks good enough.

This took a long time, not because the coding was difficult (I borrowed code from here and here), but because it took forever to get the Python libraries installed correctly. And, when I did get the libraries installed, the blue marble background image, for some reason, showed up backwards. To right everything, I had to change the “backend” rendering of the matplotlib library by installing WxAgg (As with most Python library challenges, I just followed advice from the Internet until I got it to work).

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Where My Friends Have Traveled

A few months ago, we had a housewarming party and about 30 guests, mostly people roughly my age living in San Francisco, attended. I put a map of the world on the wall, and we asked people to mark each country that they had visited. I then entered the data into excel and used Google Charts Tools Geomap to make the following map of where people I know, who showed up to a party in San Francisco, have traveled.

I think it is absolutely fascinating that not one of the 30 partygoers had been to many places in central Africa, most of the Middle East, or a string of Eastern European countries stretching from the Baltic Sea to the Black Sea. Also, it’s interesting that only one person at the party has visited Russia, and that my friends were just as likely to have visited France and the UK as they were to have visited Mexico.

There’s a lot to say here. I will be revisiting this map.

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Eastern Europe Bike Trip in Google Earth

I recently combined all of the GPS files from our ~1000-mile 26-day bike trip this past summer and uploaded them into Google Earth. The result is a path that you can “fly over” and follow. Google Earth also allowed me to plot an elevation profile of the trip.

Unsurprisingly, our favorite parts are almost exactly correlated with the hilliest sections–the Tatras mountains of Slovakia at the beginning of our journey, and the many mountains of Bosnia at the end.

You can download the KML file from our trip here and plot it yourself on Google Earth. I took a screen capture video of the route, which you can see below.

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U.S. State Populations with Circles

This weekend I taught myself how to use this javascript library MooCirclePack, which allows one to tightly arrange circles of various radii (yes, it was an exciting weekend). Below are the U.S. states, with the area of each circle proportional to the state’s population.

California is, of course, number one.

Moocirclepack only lets you put an image on each circle, and not text, so I had to use the Python pil library (which was a major pain to install) to turn the state abbreviations into images.

The most interesting thing I saw on this graphic is Puerto Rico (PR). Puerto Rico, with a population of 3.7 million, is more populous than 21 other states. It’s almost strange that it isn’t a state (based on population alone).

I do have some things I don’t like about this visualization. The spatial placement of the circles doesn’t mean anything (Oregon is next to Florida). Secondly, our eyes are not very good at comparing area. For instance, it’s hard to tell, by looking at this graphic, that California has a population 10 times that of Puerto Rico (38 million versus 3.7 million).

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More Coal?

This following graphic comes courtesy of WRI, which shows where new coal power plants have been proposed. According to a new report by WRI, there are 1,199 planned around the globe, more than half of which will be built in India and China.

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Religion in America

A few weeks back I made the following map to show the religious population in each state, drawing on statistics from Pew Forum on Religion & Public Life. The data is from 2008.

A few things jump out at you: Evangelicals are concentrated in southern states; Catholics are found in the Northeast, North Central, and Southwest regions of the country; Mormons dominate Utah, and almost nowhere else. You’re most likely to find unaffiliated — people who claim no religion — in the far northeast or west of the Rockies. It is also surprising, in comparison to Christians, how few Jewish or Muslim people live in the U.S.

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