Question asked: How is the spatial pattern of watershed recession coefficients related to the spatial patterns of watershed elevation, soils, geology, basin area, and precipitation?

Name of the tool or approach used: To perform this analysis, I used recession analysis to quantify low flow metrics for 12 streams and rivers in the Oregon Coast Range. I classified the a and b recession coefficients using 2 different approaches in ArcGIS: equal interval and natural jenks. The resulting classifications are quite different.

Methods of procedure:

1. Recession Analysis: the methods employed for the recession analysis are described in Exercise 1. For Exercise 2, I calculated recession coefficients for an additional seven sites in the Oregon Coast Range for a total of nine sites. Data for the present analysis was analyzed on the hourly timestep.
2. Watershed Delineation: I delineated watersheds in ArcGIS based on each gaged pour point.
3. Spatial data: I downloaded relevant spatial data from various geospatial databases including Oregon Geospatial Enterprise Office, USGS, USDA, and OSU PRISM Climate Group.
4. Data Visualization and Classification: I used ArcGIS to visualize my spatial data and to classify the recession analysis results. I used two classification methods: equal interval and natural jenks. Both methods had 5 classes.
   1. Equal interval classification: classification categories using this method is calculated by dividing the range of data by the designated number of classes.
   2. Natural jenks classification: this is an optimization method of classification that minimizes variance within classes and maximizes variance between classes.
5. The results of my classification were analyzed by visual comparison. That is comparison between the two classification methods, as well as how the results of each classification methods compared to the environmental variables across the watersheds.

Results:

Results from this analysis demonstrate that the recession coefficient classification method influences the apparent spatial variability of recession metrics. The natural jenks method results in more spatial heterogeneity across the latitudinal gradient (Figure 1). The a and b recession coefficients seem to be related in the way that they vary across space, however coefficient b is slightly more variable than coefficient a. The equal interval classification method results in a more homogenous representation of both a and b coefficients (Figure 2). Coefficient a seems to be largely controlled by basin size using this method, which is apparent because the smallest basin that is in the class with high values. Coefficient b is more variable, and also seems to be controlled by basin area. Precipitation may also be a primary control. Overall, between the two classification methods, the Nehalem watershed (furthest north) and the Chetco watershed (furthest south) seem to demonstrate similar recession coefficients. The watersheds between the Chetco and the Nehalem, in general, demonstrate similar recession behavior.

Figure 1. Classification of recession coefficients using the natural jenks method in ArcGIS, compared to five environmental variables. Coefficient a is in the first panel and coefficient b is in the second panel.

Figure 2. Classification of recession coefficients using the equal interval method in ArcGIS, compared to five environmental variables. Coefficient a is in the first panel and coefficient b is in the second panel.

Critique of method:

This was a simple method of visual comparison across space, however it was quite useful for both: 1) considering the spatial patterns of watershed recession behavior, and 2) comparing classification methods and how they influence the outcome of the analysis. Because it is just a visual comparison, there are no quantifiable differences presented here, which will be important moving forward. Additionally, this was an important exercise to understand the mechanical steps necessary for making this comparison.