Author Archives: goodmaar

Final: Stream Change Recap

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:

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 84th and 16th 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.

Ex 3: Grain size distributions and peak flow events

The context:

For this exercise, I wanted to figure out if there was a relationship between the temporal pattern of peak discharge in my study creeks and the temporal pattern of grain size distributions. The temporal pattern of grain size distributions could help tell a story about the interplay between hillslope and alluvial process and the forces involved in shaping and stabilizing the streams. One might predict that changes in grain size distribution might be related to extreme events. Large events could associated with debris flows which might reduce sorting while more moderate flow events could increase sorting.

The data:

Pebble counts were conducted in conjunction with cross section surveys at five reaches between 1995 and 2011. They show both the median (D50) and standard deviation of grain size varying over time.

During this time period, two different cross section sampling methods were used, depending on the year. In one method, every cross section in a given year was surveyed. In another method, the only cross sections sampled were ones in which the field crew thought that the creek bed had changed. Because of this, the grain size samples in some year are incomplete and biased towards conditions that favor visible change.

The graphs below show the mean of the D50 and mean of the standard deviation for each set of cross section for each year sampled in subfigure a. The error bars show standard error. The faded points represent years where the field crew only sampled a subset of the cross sections. Subfigure b shows the area-normalized annual peak discharge for the stream gauges on Mack and Lookout Creek. Cold Creek is ungauged.

 

These figures imply that most reaches were the least sorted in 1997, the year after the flood of record, followed by various decreases and increases over time.

The questions

I asked the following questions to try to understand the relationship between peak flow and grain size distribution:

  1. Is the standard deviation of grain size in a given water associated with the peak flow from that water year?
  2. Is the standard deviation of grain size in a given water associated with the peak flow from the previous water year?
  3. Is the change in standard deviation of grain size between two years associated with the largest peak flow from the interceding years?
  4. Is the change in D50 between two years associated with the largest peak flow from the interceding years?

 

The methods and results

I used simple linear regression to relate each of these variables and took the R2 value to represent how much of the sediment distribution variability each variable might explain. The results were negative in all cases.

1.

SITE  Rsquared

1 LOL    0.00983

2 LOM    0.00320

3 MAC    0.0340

4 MCC    0.106

2.

SITE  Rsquared

1 LOL     0.0546

2 LOM     0.0297

3 MAC     0.0191

4 MCC     0.217

3.

SITE  Rsquared

1 LOL     0.0845

2 LOM     0.0718

3 MAC     0.0842

4 MCC     0.0852

4.

SITE  Rsquared

1 LOL    0.0440

2 LOM    0.0252

3 MAC    0.0553

4 MCC    0.00688

The answer to these questions all appear to be no – clearly hydrology has some impact on grain size distributions, but the relationship may be too complicated to address using a single predictor variable and limited data.

 

Ex 2: Relationship between stream cross-sectional change and across-channel slope

For Exercise 1, I investigated autocorrelation in patterns of cross-sectional change across and along stream reaches.

For Exercise 2, I wanted to know whether these patterns of change were related to channel geometry.

Specifically, I wanted to know if erosion or deposition happened more frequently close to cut banks than further away from them.

I was originally going to investigate change in both the along-channel and across-channel directions, but after consulting with my classmates, I decided that I would not be able to draw as many conclusions in the along-channel direction because I don’t have good information about the spacing and orientation of my cross sections.

Methods:

To find cross-sectional change, I paired sequential years and calculated change along each cross section for each pair of years as I did in exercise #1.

I wanted to compare the change to the location of the steepest bank, but I couldn’t figure out how to identify what parts of a cross section counted as a “bank” using a computer. Instead, I looked at the spatial pattern of change in relationship to the point of steepest slope in the across-channel direction.

I used the original cross section profiles to identify the point of steepest across-channel slope. Since the point of steepest slope may move from year to year, I used the steepest slope from the first year in every year pair. I used the loess function in R to lightly smooth each cross-sectional profile before extracting slope in hopes of reducing the effects of some small bed features. This worked well in most cross sections, but in some cross sections, especially those with prominent midchannel features, the point of steepest slope occurred in the middle of the channel.

Once I had identified the steepest point in each of the cross section for each year pair, I calculated how far every other point in the cross section was from the steep point. Then, within each reach, I aggregated the data by distance, rounding to the nearest decimeter, and calculated the mean absolute elevation change (that is, counting both erosion and deposition as positive values). I wanted to see broad patterns overall, so the aggregate combines data for every cross section and every pair of sample years.

I plotted the resulting data from each reach. In the figure below, the colors represent how many horizontal centimeters of reach are aggregated into each point on the line graph. Bigger numbers and more blue colors represent averages from more cross sections and years while smaller numbers and more yellow colors represent distances where fewer cross sections or fewer years had data at that distance from the steep point.

Results:

The figure implies that perhaps a lot of channel change tends to happen very close to the steepest point, but then stabilizes. Far from the point, the average vertical change values become very unsteady, perhaps because fewer data points are integrated into the average.

Critique:

I thought that this was an interesting and fairly straightforward analysis to conduct, but I am not sure how physically meaningful the results are, since the steepest points are not placed in the same location in each cross section. The results figure looks a bit like a channel cross section itself, which I thought was very interesting! I wonder if this is because the averaging falls apart at a distance roughly equal to the average channel width or if there really is more change happening near channel banks on average in these streams.

 

 

Ex. 1: Spatial Autocorrelation in Stream Cross-Sectional Change In the Andrews Forest

Question that you asked

How much spatial autocorrelation exists in historic streambed changes in the Andrews Forest, both in the across-stream direction and the along-stream direction?

Name of the tool or approach that you used

I addressed each direction of the problems separately using. Since this resulted in one-dimensional, evenly spaced or faux-evenly spaced data, I just used the acf function in R to analyze the data.

Critique of the method – what was useful, what was not?

The methods provided an interesting look at patterns within the cross section data set. However, I think the one-dimensional approach fails to capture interactions between across- and along- channel changes as well as any temporal autocorrelation.

Description of steps you followed to complete the analysis and results you obtained.

For my project, I am looking at changes in the streambed along 4 stream reaches (black boxes) along two streams (blue lines) in the Andrews forest. The watershed of the larger stream, Lookout Creek is shown in black.

Researchers in the Andrews forest repeatedly surveyed fixed cross sections along the reaches from 1978 to 2011. In this exercise, I worked with a subset of the data from 1978 to 1998. The cartoon below does not contain real data but shows the arrangement of numbered reaches (blue lines) and their fixed endpoint stakes (blue dots) along a stream.

Part A – across-channel changes

Each individual cross section spans from bank to bank and show the topography and bathymetry across the width of the stream. Below are two consecutive years of cross-sectional profiles of the stream bed at Lower Lookout Creek, cross section # 3.

Profile from 1995.

Profile from 1996.

It looks like there was a lot of change between these two profiles. The active stream channel appears to have avulse from one side of the cross section to the other.

I compared the two profiles to see how much elevation was gained or lost.

Here grey line represents the surface of the stream bed in 1995, and the black line represents the surface of the stream bed in 1996. The area show in red represents material that was eroded away, and the green area represents material that was newly deposited.

We can simplify the plot above to show only the positive or negative elevation change across the channel. The figure below shows the same information as the figure above, but directly in terms of elevation change.

You can clearly see from this figure that this site at this year had one broad area of elevation gain or deposition, and one broad area of elevation loss or erosion. This isn’t precisely an attraction/repulsion phenomenon, but there can be positive feedbacks associated with erosion and deposition. For example, sediment deposited in a stream may locally obstruct or slow down the water passing by it and encourage deposition in adjacent areas.

Now, not every pair of years had this *much* elevation change, but also, not every pair of years shows such broad regions of positive or negative elevation change. Here is a figure of the elevation change at the same cross section between 1996 and 1997. You can see that elevation change is both smaller in magnitude and shows up in smaller patches.

I used spatial autocorrelation metrics to investigate the patch size. Note that autocorrelation at some scales is always expected when looking at topographic change. Even if you had extremely precise and accurate measurements, you would expect the stream bed change at one point to be strongly correlated with the change a few cm away both because the two points probably experience very similar forces and also because gravity tends to smooth out topographic irregularities in nature. But in most scenarios, you would probably not expect to find a correlation between the changes in a streambed with a change at a point 100 m away from the bank.

I picked two distances that seemed potentially interesting: 1 meter and 5 meters. Then I investigated autocorrelation at 1m and 5m lag distances using the acf function in R.

These lag distances are superimposed on the first change graph in the figure below.

I expected that all cross sections would have higher autocorrelation at the shorter lag distance than the longer one, but I also expected that a set of cross sections with very large patch sizes might show relatively higher autocorrelation at the larger lag distance compared to the small-patched cross sections. I need to spend more time looking into how the magnitude of the changes influences the autocorrelation function so that I can understand it better.

I repeated the process for every cross section at every year. Results are shown below.

The cross sections showed mostly positive autocorrelation at 1 m. Some years showed more autocorrelation across the board than others, and some individual cross sections seemed like they frequently had high autocorrelation values at multiple years.

Different patterns emerge at this spatial scale. At a 5 m lag, neutral or negative autocorrelation was much more common.

 

Part B – along-channel changes

I also wanted to look at autocorrelation between cross sections. Was the change at one cross section related to the change at cross sections immediately up- or down-stream?

Because I don’t have true geographic locations of the cross sections, I couldn’t do any fancy two dimensional work like interpolating the data into a river bathymetry model. Instead, I consolidated the cross-sectional change data into a single metric for each cross section.

Here is another look at the change plot I showed earlier. This is the same plot as before, but I’ve colored the positive values green to show deposition and the negative values red to show erosion.

I simplified this plot by removing the horizontal across-stream component and summing the red and green areas separately.

Then I had a choice: I could add the red and green areas together to show the total area of the cross section that changed in any way, or I could subtract the red area from the green area to show the net deposition in the cross section. Each of these metrics could be interesting for different reasons. For example, the total area of change might be relevant for ecological processes, but the net overal change could be relevant for tracking net sediment transport in these streams.

I started by adding the boxes together to calculate the total area of change.

In this way, I made a score for every cross section for every year pair. Hypothetical scores for the cartoon river are shown below.

Then I used the acf function again in R to determine how correlated adjacent cross section scores were with each other, compared to more distant cross section scores. This method is not fully spatial explicit, since I did not include any information about how far apart each cross section was from its neighbors. I just assumed that they were roughly equidistant within each reach. Indeed, the distance between cross sections can become hard to define when they are placed along curved parts of the stream.

I calculated these values with every year pair on each of the four reaches. I then repeated it with the subtraction method described above.

For the “addition” method, autocorrelation values were generally close to neutral with some reaches in some years tending towards positive values. Positive values could occur if changes in one reach directly influenced an adjacent reach (i.e. if upstream channel change influenced the hydraulics directly downstream) or if adjacent cross sections were influenced by similar other factors (i.e. two adjacent cross sections placed on a steep part of the reach might experience more similar changes to each other than two cross sections placed in a shallower part of the reach.)

The pattern is different for net change, where negative autocorrelation is more common. I think this is very interesting, because it implies that parts of the stream that had net deposition were more likely to be adjacent to parts of the stream that had net erosion. This could be an expression of a sink/source phenomenon where material scoured from one area is transported and deposited immediately downstream at the next cross section.

Cross-sectional Change in the Andrews Forest

Background and Research Questions

This project explores stream bed mobility in the HJ Andrews experimental forest in relationship to peak discharge events and channel geometry. The HJ Andrews Experimental Forest is a Long Term Ecological Research (LTER) Site located east of Eugene on the western slope of the Cascade Range. The site has been managed since the 1950s for ecological and forestry research, and the largest stream in the forest has been gauged since 1950. From 1978 to 2011, researchers conducted repeated cross sectional surveys on five reaches in three different creeks in the forest. An analysis of these cross-sectional profiles will help researchers and managers gain a better understanding of how, when, and where stream beds responds to extreme hydrologic events.

Water flowing through streams exerts stresses on the bed material. Whether or not these stresses have the capacity to mobilize sediment and change the shape of a stream bed depends on the amount of water moving through the stream along with other factors. As with many geomorphological processes, bed sediment transport is dominated by movement during extreme events. Although cross sectional changes overall appear to be strongly related to the magnitude of greatest flow between measurements, I am interested in investigating confounding factors including the extent of temporal and spatial autocorrelation in the data set.

I would like to explore (a) if and (b) in what manner aggradation or erosion in one cross section might be related to changes in adjacent cross sections. I would also like to know if aggradation or erosion in one cross section in one year are related to changes in the same cross section in an adjacent year.

Data

I am analyzing cross sectional measurements of five reaches within the Andrews Forest, which range from 12 m to 55 m in horizontal extent and 1.2 to 7.3 meters in vertical extent. The cross sections are variously spaced along an along-stream dimension, and they are were surveyed every one to five years over a period of 30 years between 1978 and 2011. The vertical precision of the data set is roughly 1 cm (though the data are unlikely to be accurate to 1 cm) and the horizontal precision varies from 1 cm to several decimeters.

The cross sectional change between two adjacent pairs of years at one cross section is shown below. Areas of erosion are shown in red and areas of aggradation are shown in green.

Hypothesis and Approach

I predict that there will be a relationship between changes at one cross section during one year and the same cross section at another year. Portions of the stream bed that have been recently scoured or contain newly emplaced sediment may be less armored than undisturbed portions of the stream bed. These less armored portions of the stream bed may be more susceptible to future disturbance. Alternately, changes at a cross section may represent longer-term processes related to channel geometry: e.g. a series of cross sections could show continued incision of a cut bank over multiple years.

I do not expect to see a detectable relationship between changes at adjacent cross sections because I expect that the most important geographic controls on channel change are either smaller (e.g. pools) or much larger (e.g. along-stream variation in discharge) than the distance between cross sections.

I want to use this class as an opportunity to explore statistical relationships, but I don’t know yet what kinds of analyses are best suited to this problem. I’d like to learn more in general about how to handle spatial autocorrelation, and when it is and isn’t a statistical issue.

Justification
This project could be scientifically useful for improving our understanding of sediment transport in Pacific Northwest mountain streams. Resource managers may also have an interest in sediment transport because it relates to stream channel mobility (“How much can we depend on this creek staying in the same place?”) and ecology (“How vulnerable is stream life to disruption via bed transport?”). From a resource management perspective, it is becoming increasingly useful to study peak flow events because downscaled climate models for our region predict increased frequencies of large storms.

Preparation

I feel good about my experience with spatial technology, and I’m most interested in learning about how to use that technology to answer statistical questions. I am highly proficient with Arc software. I have TAed one undergraduate class and independently taught another short undergraduate class in ArcGIS. I have a working knowledge of ArcPy, but I still need to use references regularly to write code. I conducted some undergraduate remote sensing research using ArcPy and used ArcPy for research at a government science agency. I work extensively in R. I’ve done image processing using Arc software, Python, ENVI, ImageJ, and raster tools in R, but it’s a very broad field, and I definitely think I could learn more.