My project focused on a set of grain size and cross-sectional change data from a long term research project in the Andrews Forest. (Maps and study description from Blog Post 1)

I wanted to explore two questions:

• How do stream channel erosion, deposition, and particle size vary over time and space?
• How do these changes relate to adjacent changes in the across-stream or along-stream direction?

Hypotheses:

I tried to address these nested hypotheses about the system:

1. Existing stream bed morphology drives spatial patterns in cross-sectional change.
   1. Unit-scale stream morphology should lead to patchy transport and different levels of autocorrelation of the change at different spatial scales
   2. Either hillslope or hydrodynamic processes or both should result in relatively high cross sectional change near banks.
2. Extreme flow events alter the size distribution of in-channel sediment
   1. High energy flows associated with high peak discharge events should increase median grain size
   2. Transport of hillslope material (also associated with high peak discharge events) should decrease sorting
3. Extreme flow events reduce the relative impact of bed morphology in cross-sectional change.

 

Here’s a conceptual model to investigate the patterns of change

Overall, I expected that different processes would drive change at different scales, so I expected the spatial and temporal patterns of change to vary based on scale as well.

Spatially:

• At reach scales: patterns of change are driven by reach level features including slope, watershed size, and land use history.
• At unit scales and smaller, patterns of change are driven by unit-scale bed morphology and positions of adjacent features.

Temporally:

• During time periods that include extreme flows, patterns of change are driven by the magnitude of the flow.
• During time periods of less extreme flows, patterns of change are more dependent on local features as described above

 

Approaches:

Due to the study design and to limits in data availability, it made sense for me to use one-dimensional analysis methods and correlation or autocorrelation tools on much of the data. I mostly worked in R. I also started but didn’t finish work on a network model using a tool specifically designed for stream network analysis.

Technical limitations:

1-dimensional models don’t perfectly capture the complex realities of stream networks and irregularly spaced spatial data, so I ran into a few hurdles trying to parse analysis results. I also couldn’t do some of my desired analyses due to the lack of shared coordinate systems for different data sets or for adjacent cross sections. However, thinking about these analyses helped me better understand exactly which data I would need to collect in order to do them in the future.

I am still having some trouble with a stream network analysis add-on in ArcGIS, and I am in the process of reaching out to experienced users who I hope know the way around my particular error.

 

Results:

• Results from Exercise #1 generally supported hypothesis 1.a.
• Results from Exercise # 2 might somewhat support hypothesis 1.b., but I think I need a more robust way to test this.
• Results from Exercise # 3 don’t appear to support hypothesis 2.a, but they might show some support for hypotheses 2.b. if the effect is lagged by one year.
• Patterns in Exercise 1 might show some support for hypothesis 3, but I really need a more robust analysis to pull apart the effects.

I started the stream network analysis partly because I wanted more context for the results of exercise 3. Some 2017 REU students collected grain size data at sites that overlapped with the long term cross sectional study sites. I haven’t finished the network analysis, but I’ve mapped the REU student data below.

Here is a map of the median grain size (D50) at the REU student sites:

Here is a map of half the difference between the 84^th and 16^th percentile grain size at the REU student sites. This value is one metric of sorting, and it would equal the standard deviation if the sizes were normally distributed.

These results indicate that grain sizes and sorting in the grain sizes display more complex patterns than a simple fining of grain size downstream or consistent gradations in sorting.

For all of the analyses, I need to spend a little more time figuring out which of these results are useable and might reflect reality in the field and which ones are more likely showing artifacts of data collection or processing

Significance:

I hope to use these results to add more depth to my research and place my research in a broader context. I personally learned more about strengths and limitations of data set

The broader purpose of entire project is to see how streams like these respond over time. How much of their changes are influenced by hydrologic rather than hillslope processes? How big of a hydrologic event needs to happen to do substantial work on the channel? The project could be useful to land managers because channel mobility could damage streamside infrastructure or alter benthic ecology

My learning (technical):

From a technical standpoint, I think improved ggplot skills. I started using cowplot and learned more about custom themes. I learned a lot about preparing data using the STARS toolbox, and I am excited to learn more about the SSN R package once I get my data working

My learning (statistics):

In this class, I learned more about autocorrelation methods and correlograms. Hope to learn more about network analysis soon too.