Tweeting in the Rain, Part 4: Tweets during the 2013 Colorado flood

In August 2013, we posted two “Tweeting in the Rain” (Part 1 & Part 2) articles that explored important roles social data could play in flood early-warning systems. These two posts focused on determining whether there was a Twitter “signal” that correlated to local rain measurements. We looked at ten rain events from 2009-2012 in six different regions of the country, including San Diego, Las Vegas, Louisville and Boulder. That analysis demonstrated that even early in its history, the Twitter network had become an important broadcast channel during rain and flood events.

Around noon on Wednesday, September 11, 2013, we posted Part 3, which discussed the opportunities and challenges social networks provide to agencies responsible for early warning systems. As that day unfolded, the rainfall steadily intensified enough that it was becoming more clear that this weather event had the potential to become serious. By midnight, the Boulder County region was already in the midst of a flood event driven by a historic amount of rain. When the rain had tapered off 24 hours later, rain gauges in the Boulder area had recorded 12-17 inches. This happened in an area that expects around 20 inches per year on average.

On the evening of September 11, we stayed up late watching the flood and its aftermath unfold on Twitter, 140 characters at a time. As written about here, we witnessed Twitter being used in a variety of ways. Two key opportunities that Twitter provided during the event were:

1. The ability for the public to share photos and videos in real-time.

2. A medium for local emergency and weather agencies to broadcast critical information.

As we approached the one-year anniversary of the flood, we wanted to revisit the “Tweeting in the Rain” blog research and take a similar look at the 2013 flood with respect to the Twitter network. For this round, we wanted to investigate the following questions:

  • How would the Twitter signal compare to these historic rain measurements?
  • How would the Twitter signal compare to river levels?
  • As the event unfolded, did the Twitter audience of our public safety agencies grow? How did official flood updates get shared across the network?

With these questions in mind, we began the process of collecting Tweets about the flood, obtained local rain and water level data, and started building a relational database to host the data for analysis. (Stay tuned over at for a series of articles on building the database schema in support of this research.)

A flood of Tweets

Below are some selected Tweets that illustrate how the 2013 Colorado Flood unfolded on Twitter. A year later, these messages help remind us of the drama and crisis severity that occurred throughout the region.

Earlier in the day, weather followers likely saw the early signs of above-average amounts of moisture in the area:

By that night, all local public safety agencies ramped up to manage a regional natural disaster:

At 10:02 p.m. MT, the Boulder County Office of Emergency Management (@BoulderEOM) posted the following Tweet:

As we approached midnight, this flood event was getting really scary:

A unique role that Twitter and its users played throughout the flood event was the real-time feed of photos and videos from across the region:

By Friday, September 13, the historic amounts of rainfall had affected a wide area of Colorado. In foothill communities like Jamestown and Lyons, the immediate danger were torrential flash floods that scoured through the town centers.

Further downstream the primary problem was steadily rising waters that pooled in the area for days. Contributing to this were several earthen dams that failed, adding their reservoir contents to the already overloaded creeks and rivers.

Compiling ‘flood’ Tweets

As part of the previous round of analysis, we looked at a 2011 summer thunderstorm that dumped almost two inches of rain on the Boulder area in less than an hour. This intense rainfall was especially concerning because it was centered on a forest fire burn area up Fourmile Creek. Flash flood warnings were issued and sirens along Boulder Creek in central Boulder were activated to warn citizens of possible danger.

For that analysis, we collected geo-referenced Tweets containing keywords related to rain and storms (see here for more information on how these filters were designed). During the 48-hours around that event, there were 1,620 Tweets posted from 770 accounts. Here is how that event’s rain correlated with those Tweets.

For this round of analysis, we added a few more types of filters:

  • Hashtags: As the 2013 Colorado flood unfolded, hashtags associated with the event came to life. The most common ones included #ColoradoFlood, #BoulderFlood, #LongmontFlood, and well as references to our local creeks and rivers with #BoulderCreek, #LefthandCreek and #StVrainRiver.
  • Our Profile Geo enrichment had been introduced since the last round of analysis. Instead of needing to parse profile locations ourselves, we were able to let Gnip’s enrichment do the parsing and build simple rules that matched Tweets coming from Colorado-based accounts.
  • Local agencies and media: Since this was such a significant regional event, we collected Tweets for local public agencies and local media accounts.

We applied these filters to six months of data – from August 10, 2013 to February 10, 2014 – beginning with a period that started before the flood to establish the ‘baseline’ level of postings.

Between September 1-7, 2013, there were less than 8,800 Tweets, from 4,900 accounts, matching our filters. During the first week of the flood, September 10-16, we found over 237,000 Tweets from nearly 63,000 Twitter accounts. (And in the following five months of recovery, there were nearly another 300,000 Tweets from 45,000 more accounts).

Comparing Twitter signals with weather data

As before, we wanted to compare the Twitter signal with a local rain gauge. We again turned to OneRain for local rain and stage data recorded during the event.  (OneRain maintains critical early-warning equipment in the Boulder and Denver metropolitan areas, including the foothills in that region). This time we also wanted to compare the Twitter signal to local river levels. Figure 1 represents hourly rainfall (at the Boulder Justice Center) and maximum Boulder Creek water levels (at Broadway St.) along with hourly number of ‘flood’ Tweets.

Boulder Flood Tweets
Figure 1 – Hourly rainfall, Boulder Creek Levels and Tweets during the Colorado Flood 2013, September 10-17. Tweets matching the flood filters during this period equals over 237,000 Tweets. Those same filters matched less than 8,800 during the September 1-8 “baseline” period.

Twitter users finding information when it is most needed

You can see from the information above that our local public agencies played a critical role during the 2013 Colorado flood. Between September 10-17, the Boulder County Office of Emergency Management (@BoulderOEM) and the Boulder National Weather Service office (@NWSBoulder) posted a combined 431 Tweets. These Tweets included updates on current weather and flash flood conditions, information for those needing shelter and evacuation and details on the state of our regional infrastructure. These Tweets were also shared (Retweeted) over 8,600 times by over 4,300 accounts. The total amount of followers of the Twitter accounts that shared these Tweets was more than 9.5 million.

Twitter offers users the ability to actively update the accounts they want to follow. Knowing this, we assumed that the number of followers of these two local agencies would grow during the flood. To examine that type of Twitter signal, we compared the hourly data new followers and rain accumulation at the Boulder Justice Center. The results of that comparison are shown in Figure 2. These two agencies gained over 5,600 new followers, more than doubling their amount during September 10-16.

Figure 2: Boulder Flood Tweets
Figure 2 – Comparing new followers of @BoulderOEM and @NWSBoulder with rain accumulation. Rain was measured at Boulder Justice Center in central Boulder.

One interesting finding in Figure 2 is there seems to be a threshold of accumulated rainfall at which point Twitter users turn their attention to local agencies broadcasting about the flood. In this case it was around midnight on September 11, after five inches of rain and the start of local flooding. As the event worsened and it became more and more difficult to move around the region, more Twitter users tuned directly into the broadcasts from their local Office of Emergency Management and National Weather Service Twitter accounts.

Even as the region shifted its attention to flood recovery, the information being shared on Twitter was vital to the community. Just as the Twitter network was used in a variety of ways during the flood, it provided a critical broadcast channel as communities grappled with widespread damage. The major themes of Tweets posted immediately after the flood included:

  • Information about the evacuated communities of Jamestown, Lyons and Longmont.
  • Details on shelters and other support mechanisms for displaced residents.
  • Organization of volunteers for cleanup activities.
  • Promotion of charitable organization funds.
  • Regional infrastructure conditions and updates. This article discusses how Tweets helped identify road and bridge damages in closed-off areas.

Based on all of this data, it’s very clear that the Twitter network played an important role during and after the 2013 Colorado flood. The combination of real-time eye-witness accounts and updates from our public agencies made Twitter a go-to source for critical emergency information.

In recognition of this important role, Twitter has introduced Twitter Alerts. This service provides the ability for Twitter users to sign up for mobile push notifications from their local public safety agencies. For any public agency with a mission of providing early-warning alerts, this service can help the public find the information they need during emergencies and natural disasters.

See these resources for more information on the 2013 Colorado Flood