Research Question: How is watershed recession behavior related to the spatial pattern of watershed lithological composition in Oregon coastal freshwater systems?

Data

Recession Analysis

For the recession analysis performed in this exercise, I used daily records of precipitation from the PRISM database. Daily data is the highest temporal resolution that PRISM has to offer. The PRISM time series were downloaded from cells nearest the gage. Time series were from 2001 to April 2019. For further methodology on recession analysis, refer to Exercise 1. River discharge data was collected by the U.S. Geological Survey at the watershed pour point used here. Discharge data is recorded every 15 minutes, which was upscaled to daily average to match the resolution of the precipitation data. As a result, recession analysis results were on the daily timestep from April 2019 to 2001.

Geophysical data

Lithological data was sourced and derived from the Oregon Department of Geology and Mineral Industries state geological map. General lithology type is a listed variable in the attribute table (GEN_LITH_TY), and ArcGIS was used to dissolve that attribute across the study area. The spatial resolution of this dataset is unclear. Additionally, the data is representing “immutable” features, and therefore there is no associated temporal scale.

Hypotheses

Streams and rivers in watersheds with predominantly volcanic geology will recede faster than those with predominantly sedimentary geology.

Streams and rivers in watersheds with similar underlying lithology will have similar recession characteristics.

Approaches

To perform this analysis, I followed the following steps:

1. Recession analysis on the daily timescale across eight watersheds. Statistical methods and other analytical procedures are outlined in Exercise 1.
2. Profile lithological composition within each of 35 study watersheds across the Oregon Coast Range. Percentages of metamorphic, plutonic, sedimentary, surficial sediments, tectonic, and volcanic lithologies were quantified for each study watershed. Technical procedures are outlined in Exercise 3.
3. Perform hierarchical clustering analysis on lithological composition profile to see which watersheds are most lithologically similar and dissimilar.
4. Compare recession analysis coefficients to clustering results and lithologic profiles to see if recession behavior appears to be different in different predominant lithologies.

Results

Results from the clustering analysis demonstrated that a most coastal watersheds are predominantly underlain by sedimentary and volcanic lithologies, and that sedimentary lithologies dominate the region. However, volcanic lithologies are well-represented, primarily at the northernmost latitudes. Because of this stratification, recession analyses could be performed at watersheds in both lithologies and compared to test whether lithology is a dominant control. However, because I conducted the recession analysis before profiling lithological composition, only one of the eight hydrologically analyzed watersheds is in predominantly volcanic geology (Figure 1). The watersheds grouped under the sedimentary branch span values of 54-100% sedimentary lithology. Watersheds grouped under the volcanic branch span values of 47-74% volcanic geology.

Figure 1. Results from hierarchical clustering analysis depicted in a dendrogram. Watersheds in which recession analyses were performed are circled.

When recession coefficients from the recession analysis are compared with the clustering analysis results, we see that coefficient a is fairly consistent among all watersheds, while coefficient b is much larger in the predominantly volcanic watershed (Table 1). The Wilson River near Tillamook, Oregon, is characterized as 50% volcanic lithology, which is the highest among the studied watershed. Recession analysis coefficient b from the Wilson River is 1.726 is also the highest in the eight studied systems.

Table 1. Table showing lithological composition of watersheds for which recession analysis was performed. Coefficient results from the analysis are also listed.

Significance

While the results of this study are inconclusive given the small sample size, it does give indication that lithology is an important control on watershed recession behavior and should therefore be included in analysis of temporal patterns of hydrological behavior. It is relevant to resource managers in coastal PNW regions because it indicates a watershed characteristic that influences storage and release behavior. Such information may be valuable for identifying systems that are more or less susceptible to drought and changes in the climatic patterns.

Learning Outcomes

• Watersheds can be delineated in Matlab, and it is much faster than ArcGIS.
• dplyr is a very important and useful package for reformatting data in a logical way
• I learned more about spatial autocorrelation from conversations with other students about their analyses, especially given the breadth of research topics in the classroom.
• Probably the two most important outcomes of this project were the recession analysis and the clustering analysis. The recession analysis involved multi-variate regression, which when applied to my data and research question, makes far more sense. The clustering analysis helped get at the question I have had for some time: how similar/dissimilar are my study watersheds? For this project, I focused on lithology, but I think it will be a useful tool for analyzing the other variables as well. Additionally the clustering analysis will provide a useful method for stratifying my sampling design into different lithological groups.