Macroinvertebrates are collected using the Stream Waders protocols. After donning rubber boots, students use D-nets to collect invertebrates from the stream bed and carefully pick through leaf litter in search of macroinvertebrates. We transfer what we find to a sample container and preserve them in ethanol to later analyze them in the laboratory. The Stream Waders samples are returned to the Maryland Department of Natural Resources, where they will be analyzed by professional taxonomists. Students may also collect classroom samples that they can analyze themselves.

While we are in the field, we record any observations about fallen trees, high water flow, or difficult access to the stream. We also record the specific location where we took the sample. 
There are many different ways to think about water quality. We are starting with putting out loggers—this is basic and cost effective, but we are hoping to add more! 
Our data loggers track water temperature and salinity in the streams year round. These loggers will collect information about conductivity and temperature, which can point to changes in water quality.

Conductivity is a measure of how easily an electric charge, heat, or sound can pass through a material. In our study, we are measuring electric charge through water. Electrical current is transported by the ions in a solution. A salty solution is full of charged particles that will conduct electricity more easily than pure water. So, conductivity is really a measure of salinity: as conductivity increases, the concentration of ions (a.k.a salt) increases.

Conductivity and temperature can point to the health of an ecosystem. High conductivity can alert researchers to a higher concentration of ions, indicating more salt in the water. In the Anacostia Watershed, and other urban waterways, this is especially problematic because of road salt. In the winter, to prevent roads from freezing over, salt and sand are used on the highways to bring down the freezing temperature of ice and to add traction to icy areas. While this makes roads safer, much of that salt ends up in groundwater and in streams. Plants and animals that live in streams are freshwater organisms, and generally cannot tolerate high salinities or rapid salinity changes. If the water becomes too salty, mobile organisms, like fish or crawfish, may be able to move to less salty areas, but organisms that can’t move or that move slowly will not be able to survive. 
Temperature is also an important aspect of water quality, and in urban streams high water temperature can be a problem. Water that comes off of manmade surfaces (concrete, asphalt, buildings, etc.) is hotter than water that comes off of fields or plains, so in urban areas, much of the water that enters streams, especially during storms, can be much warmer than the natural temperature range for that stream. Some stream organisms can only survive within a certain temperature range and warmer water can be stressful for some organisms, so knowing how the temperature of the stream changes through the year can tell us a lot about overall stream health. 
Measuring temperature and salinity over time can help us monitor the health of these streams, and make judgements about how various environmental stressors affect different waterways.

Remember, conductivity is the measure of how easily an electric charge can pass through water, which is based on the amount of ions. So, the more ionic, or salty, the more conductivity a solution has. What does electrical conductivity have to do with TDS?
TDS stands for Total Dissolved Solids. This is a measure of the total ions in a solution. Conductivity is how easily electrical charge can pass through water, and that is determined by TDS. 
We are using the conversion 1000 (μS/cm)=640 PPM

So what is a micro-Siemen?

A micro-Siemen (μS/cm) is a measure of conductivity. For example, 350.69 μS/cm is a sample measure of tap water.

Parts per million? Parts per thousand? What is this all about?

This is unit to report conductivity. For example, 50 PPM in water means there are 50 milligrams of solids per liter. Conductivity is most commonly reported in literature, press releases, and other public communication in terms of parts per thousand (PPT), so we will be using those units for this experiment. Just so we are all on the same page: 1000 PPM= 1 PPT
So, to review: 
1000 (μS/cm)=640 PPM
1000 PPM= 1 PPT

This graph shows how salinity (measured as conductivity) in the Watts Branch Stream changes over time. The orange line indicates changes in salinity between November 13 and December 9. The closer the point is to the bottom of the graph, the lower the salinity. A higher point in the graph indicates an increase in salinity. Notice the dip on November 19. Do you have any ideas why the salinity may have dropped so suddenly?

One possible explanation for the low salinity on November 19 could be heavy rainfall. First, let’s take a look at another graph.

This graph shows changes in the water temperature of the Watts Branch Stream through time. The higher the point, the higher the temperature. The lower the point, the lower the temperature. Notice the peak in temperature on November 19. The water temperature was 62.74 °F! That’s the same point we saw the lowest salinity (0.09 PPT) in the previous graph.

This makes sense because temperature and salinity are related! If rain comes down on warm areas such as pavement (which holds heat), the water will warm up. The warm water will then run off into a stream, increasing the temperature of the stream, as well as diluting it.

So, our possible explanation is that there was heavy rainfall on November 19, but we didn’t collect any information about rainfall. Fortunately, there are lots of other groups who collect data, and we could look up data about rainfall from somewhere like NOAA’s Climate Data Tool to see if there is evidence to support our explanation. By checking the rainfall data, we can see that indeed, it did rain on November 19, 2015 in the area of Washington DC around Watts Branch.

Let’s look and see if there is any evidence of the November 19th rain event in the air temperature data. 

This graph shows the air temperature at Watts Branch Stream through time. When we look at November 19th, we see that there was neither a peak nor a dip in air temperature, so we don’t see a signal from the rain event in the air temperature. But, we do see a lot of peaks and dips throughout the graph, so we need to come up with another explanation for this pattern. One possible explanation could be that the air temperatures change on a daily cycle, with higher air temperatures during the day and cooler temperatures at night

We can test this explanation, too. In this graph, we’ve shaded the hours when it was dark outside, so that grey areas are data points that were collected during the night and white areas are data points that were collected during the day time. We can see that the temperatures drop at night and rise during the day, which explains the peaks and valleys that we see.

So, based on our data, we proposed some explanations for the patterns that we see. We think that a rain event caused an increase in water temperature and a decrease in salinity, but did not have an impact on air temperature. Instead, we see a daily cycle in the air temperature, with cooler air temperatures at night and warmer temperatures during the day.

We can probably be pretty confident in our explanation of the daily cycle for the changes in air temperature because that is the pattern that we see repeated through the graph. We can’t be as confident about our hypothesis that rain events lead to lower salinity and higher water temperature because we only have one occurrence of this. To be more confident, we would need to have more data to see if the same odd occurrences occur with other rain events. We could also look at additional data sources, like the NOAA climate Data tool that we use to determine if it rained near Watts Branch on November 19, to see if there are times when there were rain events, but we don’t see changes in the salinity or water temperature. If we have changes in water temperature and salinity without a rain event or rain events that don’t cause changes in salinity and water temperature, we would become less confident in our explanation, and would likely need to come up with a new explanation or add some more details to our existing explanation. An example of more details might be something like, changes in salinity and water temperature are only seen if it rains at least 2 inches. This is the same basic explanation, but with more details about when that explanation may or may not apply.

It is also important to consider whether or not our explanations make sense. It makes sense that rain would cause a decrease in salinity because we are adding more fresh water, so the stream is more diluted. It would also make sense that water that had come off of the warm land (especially in urban areas with lots of pavement) would heat up the stream, so we would see increased water temperature. And, we know that in general, air temperatures are warmer during the day than during the night, so our explanation for the pattern in the air temperature makes sense too. But, analyzing our data also leads to some more questions, which is great because doing science is all about asking good questions! So, why didn’t we see a change in the air temperature with the November 19 rain event like we saw changes in water temperature in salinity? We can use our data and some outside information to explore this further.

A good way to start exploring this question might be to look at air temperature and water temperature on the same graph.

We can see that water temperature is more consistent than air temperature. We see a strong daily pattern in the air temperature, but we don’t really see that change in the water temperature. Over our study period, the air temperature ranged from 31.04 °F to 66.26 °F, but the water temperature had a much smaller range from 42.62 °F to 62.75 °F. That is a 53% change in air temperature, but only a 32% change in water temperature. Water temperature doesn’t change as quickly or easily as air temperature does, making it more stable. All of this means short-term temperature changes will be more obvious in the water temperature data than in the air temperature data because air temperature data bounces around so much that there would really need to be a change in order for us to notice it. On the other hand, the water temperature data is more stable (and less bouncy), so it is easier to see changes. This is another explanation that we can continue to test as we get more data.

Looking at these two temperature data sets together also gives us more evidence to support the explanation that the increase in the water temperature on November 19th was caused by a rain event. Rain would cause an increase runoff from surrounding areas into this stream. Water hitting warm pavements and hard surfaces would travel into the stream, causing the temperature in the stream to go up. Adding fresh water to salty water dilutes the amount of salt, causing salinity in the stream to go down. When we look at our graph, we can see that the air temperature had been warmer than the water temperature before the rain event, but that the air and water temperatures were similar after the rain event. This makes sense if the rise in temperature in the stream was caused by adding water that was heated up from the land.

Please check back as more data become available.