One of the strongest observed climate patterns in the Chesapeake Bay Region has been an increase in the total annual precipitation. This index is one of our most straightforward; total annual precipitation is simply the sum of precipitation over the course of a year!
North vs South Chesapeake Total Annual Precipitation
For this project, we have divided Chesapeake Bay into two regions: North and South (Figure 1). Both these regions have observed a historical increase in total annual precipitation, but at different rates.
North Chesapeake has an observed increase over the last century of +16.8 mm per decade (Figure 2). South Chesapeake has a slower observed increase of +5.2 mm per decade. Both these trends have a strong confidence.
Additionally, future projections suggest that Chesapeake Bay-wide total annual precipitation will continue to increase at a rate of 12.7 to 13.4 mm per decade under different emission scenarios.
While variability is high, it is probable that the annual sum of precipitation will increase as we move into the future.
So what can we do with this information?
Total annual precipitation is significantly correlated to streamflow, the amount of annual wet days, and the mean total nitrogen load.
This will be discussed in a future post!
For today, I am posting an idea that may be pretty neat! Since total annual precipitation is significantly linearly correlated to the parameters listed above, we can get a linear equation of the best fit line.
The equation of a line is a very simple single parameter model.
For example, we can use a linear regression between our time series of total annual precipitation and nitrogen load in the Patuxent River to generate a slope (m) and y-intercept (b) for these two variables over that time frame.
Total Nitrogen=m*(Total Annual Precipitation)+b
So, if we have a value for the total annual precipitation (x) of a given year, we can estimate the corresponding mean total nitrogen load (y). That could be interesting(! or ?)
Implementing the idea
We now have a set of linear equations where total annual precipitation is the independent variable (x). So, with the single value of annual precipitation, we should be able to get a list of estimate dependent variables (y) for all significant relationships.
Running with this idea, I wrote all the equations into R (Figure 3). I have written it in a way that all you have to do is enter a value for the Total Annual Precipitation and instantly you will have an estimate for streamflow discharge for the Susquehanna, Potomac, and James, wet day frequency, and mean annual total nitrogen in 9 tributaries.
For example, now that 2015 has come to a close, we can easily get the sum of precipitation over the year. Friday morning, I downloaded the 2015 meteorological parameters at Jug Bay, MD and Taskinas Creek, VA from the Central Office for Data Management.
It’s easy to sum the ‘TotPrcp’ column in Excel (or another computer program, such as R).
In 2015, Total Annual Precipitation was 1021.7 mm at Jug Bay and 1321.6 mm at Taskinas Creek. So, you can input these two values, run the R script and voila! (Figure 4)!
The interface is rough, and probably confusing. But that is easy to fix, or even write it to export the estimates in an Excel spreadsheet!
Final Thoughts and Caveats
These values are just estimates of annual means, but they can give us some helpful information before it is readily available. (For example, did we have a lot of Very Wet Days in 2015?).
I should also stress the caveat that these linear equations were calculated over fixed time periods. For example, total nitrogen was calculated from 1985-2013 and streamflow from 1937-2014. Thus, these equations will not take into account future changes; for example, we discussed above, it will not take into account the expected future increases of total annual precipitation.
So the values generated do not take into account climate changes or variability, but it still offers insights on nutrient loading and precipitation patterns.
Moving forward, I think it will be possible to include these expected changes in annual precipitation. So stayed tuned!