First Peak of Precipitation Data

Precipitation is ecologically and environmentally important to understand. Just ask anyone living in the Boston area right now! Both too much precipitation (flooding) and too little precipitation (drought) can have serious ecological impacts! Obviously, we are extremely interested in understanding the extreme patterns of precipitation in the Chesapeake Bay region.

Figure 1: Ever see a rainstorm in the distance while you’re under a sunny sky? Rain can be crazy like that! Credit: Trip Advisor

Figure 1: Ever see a rainstorm in the distance while you’re under a sunny sky? Rain can be crazy like that!                Credit: Trip Advisor

Unfortunately, unlike temperature, precipitation can be much harder to work with. While temperature mostly changes little from place to place, precipitation can vary greatly even in a small area. Think of it like this: the temperature driving from Baltimore to Annapolis is likely to be the same, maybe only a degree or two of a difference. But, we all have driven through a rain storm that seems to only have down-poured in one place, while the next town over appears completely dry!

Everyday has a temperature, but precipitation is more of a present/absent question. In other words, it is either raining or not raining, and when it does rain, the question is how much and for how long?

Figure 2: General distribution shape for A) Temperature and B) Precipitation. Credit: Zhang et al., 2011 and the IPCC.

Figure 2: General distribution shape for A) Temperature and B) Precipitation. Credit: Zhang et al., 2011 and the IPCC.

Logically, the first thing I did was look at the actual data. Rain, distribution-wise, also behaves differently than temperature. In most cases, a location’s temperature follows a Gaussian distribution (Figure 2) were there is an average temperature and two tails of the rarer maximum and minimum temperatures.

Precipitation, however, cannot have a negative value! We either have a rain event or we don’t, so the amount of precipitation is often denoted a having a single tail. While the distribution frequency of rainfall amounts is still Gaussian (more on this next week!), the general distribution of precipitation looks more like a decay.

Figure 3: The daily sum of precipitation, the daily mean precipitation, and the maximum 15-minute precipitation amount at Jug Bay. Note that, in general, the 15 minute window was too small to be visible on the graph.

Figure 3: The daily sum of precipitation, the daily mean precipitation, and the maximum 15-minute precipitation amount at Jug Bay. Note that, in general, the 15 minute window was too small to be visible on the graph.

So what does the daily precipitation look like? What you can immediately see in Figures 3 and 4, the precipitation time series is optically driven by a few large events. Most notably at Jug Bay and Taskinas Creek, there was a huge rain event in 2011 were >150mm of precipitation fell! That’s close to 6 inches of liquid! This is event is most likely Hurricane Irene!

In Figures 3 and 4, the black lines are the cumulative daily precipitation in millimeters. Precipitation at the NERRS sites are recorded every 15 minutes, so this is the sum of the 96 precipitation measurements taken daily.

Figure 4: The daily sum of precipitation, the daily mean precipitation, and the maximum 15-minute precipitation amount at Taskinas Creek.

Figure 4: The daily sum of precipitation, the daily mean precipitation, and the maximum 15-minute precipitation amount at Taskinas Creek.

Out of my own curiosity, I also took the mean of the precipitation for each day. Hopefully, this helps demonstrate why averages in climate change are not as ecologically useful as extremes!

Let’s use Hurricane Irene as an example. The heart of Irene hit the Chesapeake Bay region on August 27th, 2011 and its heavy precipitation and winds caused millions of dollars’ worth of damage. At Taskinas Creek, VA, the mean precipitation for August 27th, 2011 was 1.6 mm. But, the cumulative sum was 153.4 mm! Even more extreme, the maximum 15 minute precipitation amount was 8.6 mm. What this says is that the rainfall came in pulses, where there were a few periods of incredibly heavy rainfall even though the average was fairly low.

Figure 5: The daily precipitation at Jug Bay (gray) and a Maryland weather station located in Marlboro. The geographical location of Jug Bay and Upper Marlboro is on the right.

Figure 5: The daily precipitation at Jug Bay (gray) and a Maryland weather station located in Marlboro (red). The geographical location of Jug Bay and Upper Marlboro is on the right.

So how does the daily precipitation at Jug Bay and Taskinas Creek compare to nearby NCDC-Daily weather stations? As warned from my introduction, the relationships are not as clean as temperature, but they are still there! In Figure 5, we can see that rain events sometimes differed in magnitude, and occasionally it rained in one location and not the other. (I must note that this could also be a sampling difference!) Similarly, we can see in a log-log relationship, that there is a relationship between Taskinas Creek and West Point (Figure 6)…but it’s more complex and “messy” than the previous temperature investigation.

Figure 5: The daily precipitation at Jug Bay (gray) and a Maryland weather station located in Marlboro. The geographical location of Taskinas Creek and West Point is on the right.

Figure 6: The daily precipitation at Taskinas Creek (gray) and a Virginia weather station located at West Point (red).

Because rainfall and snowfall can greatly vary over the same region, we expected these trends to be correlated weaker between the NCDC stations and the NERRS sites. What is important to note, is that the extreme signals, such as Hurricane Irene, show up in all the sites!

 

 

In conclusion, for us to assess precipitation extremes in the Chesapeake Bay region (and not at a single location), we need use multiple datasets in order to have the most confidence in our results.

To be continued…..

Kari Pohl

About Kari Pohl

I am a post-doctoral researcher at NOAA and the University of Maryland (Center for Environmental Science at Horn Point Laboratory). My work investigates how climate variability and extremes affect the diverse ecosystems in Chesapeake Bay. I received a Ph.D. in oceanography from the University of Rhode Island (2014) and received a B.S. in Environmental Science and a B.A. in Chemistry from Roger Williams University (2009). When I am not busy being a scientist, my hobbies include running, watching (and often yelling at) the Boston Bruins, and taking photos of my cat.
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