Piney run
39°22’20”N 76°54’35”W
Weather Underground: The total accumulation of precipitation during the recording of this experiment in 4.93 inches of rain. Sunday, September 17th, had the most downpour with 1.21 inches of rain.
“- Lower Sensor: The deepest part of the stream, equivalent to knee-deep height. Most stable banks with tree roots exposed to hold sediment. While the sensor was knee deep there was slight debris and leaves caught by the rebar.
-Upper Sensor: placed in a shallow headwater stream surrounded by a riparian zone. Here is the most shallow section of the stream with highly visible stream bed. Low flow rate, especially with stones in the middle of stream. We can also tell the stones here tend to grow moss as well.”
Banks are generally unstable, there is a highers sediment load at upstream location but gradually becomes more secure as you get down the stream. The stream is the most stable at the lower sensor area right before the stream converges into the main run.
The Stream had an overall low flow speed. Through the photos you will be able to tell that there is minimal turbulance along with an extremely visible streambed.
## # A tibble: 12 × 3
## Location Classification Days
## <chr> <chr> <int>
## 1 Air Dry 46
## 2 Air Buffered 9
## 3 Air Wet 1
## 4 Downstream Buffered 30
## 5 Downstream Dry 20
## 6 Downstream Wet 6
## 7 Middle Stream Buffered 42
## 8 Middle Stream Wet 10
## 9 Middle Stream Dry 4
## 10 Upstream Buffered 35
## 11 Upstream Wet 13
## 12 Upstream Dry 8
## Sensor Start End Start_Temp End_Temp
## 1 Upstream 2025-09-01 06:00:00 2025-09-01 18:00:00 55.510 60.017
## 2 Middle Stream 2025-09-01 06:00:00 2025-09-01 18:00:00 59.205 62.810
## 3 Downstream 2025-09-01 06:00:00 2025-09-01 18:00:00 59.178 65.483
## 4 Air 2025-09-01 06:00:00 2025-09-01 18:00:00 53.813 66.431
## 5 Upstream 2025-09-01 18:00:00 2025-09-02 06:00:00 60.017 56.412
## 6 Middle Stream 2025-09-01 18:00:00 2025-09-02 06:00:00 62.810 60.107
## 7 Downstream 2025-09-01 18:00:00 2025-09-02 06:00:00 65.483 60.079
## 8 Air 2025-09-01 18:00:00 2025-09-02 06:00:00 66.431 54.715
## Slope_C_per_hr
## 1 0.376
## 2 0.300
## 3 0.525
## 4 1.051
## 5 -0.300
## 6 -0.225
## 7 -0.450
## 8 -0.976
## Sensor Start End Start_Temp End_Temp
## 1 Upstream 2025-09-17 06:00:00 2025-09-17 18:00:00 60.017 61.820
## 2 Middle Stream 2025-09-17 06:00:00 2025-09-17 18:00:00 61.008 63.711
## 3 Downstream 2025-09-17 06:00:00 2025-09-17 18:00:00 60.980 63.682
## 4 Air 2025-09-17 06:00:00 2025-09-17 18:00:00 60.124 64.629
## 5 Upstream 2025-09-17 18:00:00 2025-09-18 06:00:00 61.820 60.017
## 6 Middle Stream 2025-09-17 18:00:00 2025-09-18 06:00:00 63.711 62.810
## 7 Downstream 2025-09-17 18:00:00 2025-09-18 06:00:00 63.682 63.682
## 8 Air 2025-09-17 18:00:00 2025-09-18 06:00:00 64.629 63.728
## Slope_C_per_hr
## 1 0.150
## 2 0.225
## 3 0.225
## 4 0.375
## 5 -0.150
## 6 -0.075
## 7 0.000
## 8 -0.075
From the data we can tell that the air sensor experiences large daily fluctuatuions experienceing bothe sharp daytime peaks and nightime lows. Overall all three sensor sights experienced a delayed thermal response with smaller diurnal changes. Intrestingly enough, the upstream location had the coolest temperature and the downstream location had the warmest temperature. With shaded headwaters it’s understandable how diurnal temperature would be cooler upstream compared to the non shaded, still waters of downstream. I’ve added statistic of to figure out the frequency of wet vs. Dry days be creating code using the following stipulations: - < 3 °F Temperature range (Smaller Swings) are deemed wet - 3-7°F Temperature range are considered buffered - > 7 °F Temperature range is considered dry.
From the data we can conclude that the upstream location is statistically wet for the most amount of time of all the sensors (13 days). THis indicates the headwater conditions has strong thermal stability. There is a shallow but consistent groundwater influence with a lot of limited exposure to direct sunlight.
To examine how our variable of interest responded to different conditions, we compared the rate of change (slope) during the day with the most precipitation to the day without. Statistics show that the midstream exhibited the least amount of change during non precipitated events compared to upstream and downstream. From the sensor picture it’s evident that the midstream sensore has a mix of shading, and flow that protects it from fast heating and cooling. The Downstream location is open and more stagnant which could promote more warming and cooling. During the precipitation events air and stream temperatures showed flatter slopes compared to the non precipitation days. This shows that rainfall dampend the diurnal heating and cooling cycles. Also interesting to note that stream exhibited no nightime cooling on the rainy day.