Imagine you bought solar PVC arrays today, sold the power generated to your local utility, and used the proceeds to add new solar arrays each year.  What generating capacity would you expect in 100 years?  What would be the impact of uncertainties in inflation and power generation?  I've been obsessed with this idea for a while.  I developed a new Shiny App available here to help play around with these questions.

There is a link in the app to obtain the R script.  Since this is not my area, I am hoping that people with domain specific knowledge will tinker and improve.

Opportunities for Improvement  There are several areas where I think some improvement is possible.  The app assumes a uniform distribution (defined by the user) of annual power generation over the 100 years.
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WASP and other flow and water quality models ask users to input multiplier and exponent values relating velocity and depth to discharge. These relationships take the form of V=aQ^b and depth=cQ^d, where the values a, b, c, and d describe the curve that approximates paired points from other sources. When paired field measurements are lacking, Manning's equation provides an estimate of open channel flow based on channel characteristics.
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In 2014, EPA documented the relative lack of nutrient data from waste water treatment plant effluents, even though development of surface water quality standards for nitrogen and phosphorus has been a stated priority for more than a decade.

A new shiny app lets users explore effluent nutrient concentrations from an existing data set by waste water treatment plant type, and by nutrient of interest.
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Monte Carlo analysis is a great way to explore the impact of input variable uncertainty on the results of engineering equations, and with vector variables and distribution and sampling functions at its core, R is a natural platform for this analysis.

During a recent rainy vacation, I built a Shiny app that applies Monte Carlo analysis to Manning's Equation for open channel flow.  You can play with the app here.
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For as long as I have been working with water data, I wanted to construct a line graph superimposed on a box and whisker plot where the boxes show the distribution of values and the line shows some current condition.  One of my favorite things about ggplot2 is that it allows users to construct complex combinations of graphs in ways that make sense.

This plot shows daily mean flow values in the Schuylkill River (the blue line) against box plots of the rolling 20-year set of daily mean values.
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High winds in the Delaware Estuary region caused a "blowout" tide in early April 2016, where observed water surface elevations were much lower than those predicted via harmonic constituents.  Extreme low blowout tides can hamper navigation due insufficient depth.

This animated graph was created in R using data obtained from the NOAA PORTS system for the Delaware Estuary, using the NOAA API and the animation library.

The Delaware River experienced some high flow in late February 2016, providing an opportunity for an interesting animated graph of river response.

This plot was developed using data from the USGS NWIS system for gauges on the Delaware River, retrieved with the excellent dataRetrieval library for R from USGS and the also great animation library.  The plot shows the discharge per drainage area (cfs/square mile) responding to rainfall.
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In this earlier post, I analyzed tidal water surface elevation data from the NOAA PORTS system from both the Delaware Estuary and the Chesapeake Bay, showing how the two systems react very differently to the tidal forcing at their Atlantic Ocean boundaries.  Animated plots may be even more effective at demonstrating this difference in response.  In the Delaware, the tidal fluctuation is amplified as it is translated upstream, but in the Chesapeake it is dampened.

In a previous post I showed an animated age structure diagram depicting output from a simple population model written in Excel.  Here is another version of that model written in R.  One of the things I like about the R version is that I can post the animated .gif files directly into presentations without having to link to a video hosting site.

In class, I use the changed fertility and death rates to demonstrate the impacts on a population.

After I completed the animated tidal water surface elevation plots for the Delaware Estuary, I looked for other systems with a good set of tidal observation stations.  The Lower Columbia River near Portland, Oregon fit the bill.  Using the NOAA PORTS stations, I set up near real-time animated plots of the Columbia.  Throughout December 2015, this plot showed some interesting results.

At the beginning of December, the observed and predicted water surface elevations are in pretty good agreement.
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I am an engineer working in water resources and the environmental field. On the side, I work with small businesses to help automate their data processing functions. I offer reasonable rates and am very efficient. Send me an e-mail at JYagecic@gmail.com
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