**Research Question**

The question that I asked for this exercise was: What is the spatial variability of the recession timescale at different flow rates in rain-dominated, coastal basins?

*Approach*

Stream flow regimes are generally defined by five components: magnitude, frequency, duration, timing, and rate of change (Poff et al., 1997). For the purposes of this exercise, I am investigating the rate of change (or “flashiness”) component of flow regime in two river systems in the Oregon Coast Range: the Nehalem and Alsea River watersheds. I am analyzing recession behavior in these two systems to quantify a rate of change metric. I used the recession curve method, largely developed by Brutsaert and Nieber (1977), and later built upon by Krakauer et al. (2011) among many others. Recession curves describe the rate at which streamflow recedes in various streamflow conditions. In more general terms, recession curves provide an indication of watershed storage and groundwater behavior.

*Methods*

To complete the recession analysis in the Nehalem and Alsea watersheds, I followed the following steps:

- Downloaded streamflow data for the available period of record in 1-hour observation intervals using the dataRetrieval and EGRET (both developed by the USGS) packages in R.
- The data were in cubic-feet-per-second (cfs) units, which I converted to unit discharge (mm/hour) using respective basin areas.
- For recession analyses, only data points with insignificant precipitation are viable. Therefore, all time steps when precipitation was greater than 10% of total streamflow were removed. To do this, local precipitation data was necessary, however it is often hard to come by. As a result, the precipitation data sources for the Nehalem and the Alsea are different and the methods diverge slightly hereafter.
- Nehalem precipitation data was sourced from the USGS Vernonia site, which is upstream from the mainstem river gauging site where streamflow data for this analysis was sampled. For the purposed of this exercise, I assumed that rainfall (measured in inched at 30-minute intervals) was spatially consistent across the basin. For a more precise estimate of precipitation, multiple, spatially distributed rain gages would be needed.
- Alsea precipitation data in hourly timesteps was not readily available. There are a couple NOAA rain gages in the vicinity, but the data appeared to be spotty. As a result, I used daily precipitation data from PRISM to identify days in which total daily precipitation was greater than 10% of total daily streamflow. Next, I used the subset out the identified dates from the hourly streamflow data.

- Calculating rate of change: For this component of the analysis, I only wanted data from the receding hydrograph. I used the equation to estimate hourly -. Hourly streamflow (Q) for the corresponding hours was estimated as .
- (or ) is the rate at which flow jumps values from one time-step to the next at a given flowrate. Because this is a recession analysis, I only wanted data that was on the receding slope and therefore subset the data to time-steps when was negative.
- Lastly, I developed a simple linear regression model for vs Q for each watershed using the following equation:

* **Results*

*Alsea*

When log-transformed and plotted, the discharge and rate of change of discharge follow a linear relationship, however the relationship is different for data analyzed at different temporal resolutions (Figures 1 a-b). The recession results from the daily timestep are likely muted because recession processed occur on shorter timescales, on the order of minutes to hours.

Figures 1 a-b. On the left, recession curve results for the Alsea River on a daily time step, after removing days with high precipitation. On the right, recession curve results for the river on an hourly time step, after days with high precipitation were removed. Both a and b include only recession data.

Table 1. Table showing different coefficient values for different temporal resolutions of Alsea recession data.

Data |
coefficienta |
coefficientb |

Alsea (daily) | -3.029 | 1.639 |

Alsea (hourly) | -5.2346 | 0.9138 |

*Nehalem*

Recession analysis on year-round, hourly Nehalem River data resulted in an *a *coefficient value of -4.696 and a *b *coefficient value of 1.049, which are similar values for the Alsea hourly results. However, the Nehalem recession data is showing two linear trends in the plotted data (Figure 2). The two trendlines remained after controlling for both diurnal flux and season (Figure 3).

Figure 2. Recession results from the Nehalem River data. The red line is the calculated linear regression model, however two lines may be more representative of the recession behavior, such as those estimated in blue.

Figure 3. Recession data separated into seasons and only including receding slopes, night time hours, insignificant precipitation. The two trendlines are apparently less distinguished in certain seasons of the year.

*Critique of Method*

Recession analysis is a method that I will use in my research, and this was a helpful exercise in that it helped me understand the nuances of recession data analysis, including data acquisition and availability, and opportunities for improvement. Moving forward, this analysis would benefit from: more spatially accurate precipitation data, more investigation of the explanations for the observed patterns and trends, and more sophisticated statistical comparison between sites.

**References:**

PRISM Climate Group, Oregon State University, http://prism.oregonstate.edu, created 4 Feb 2004.

Krakauer, N. Y., & Temimi, M. (2011). Stream recession curves and storage variability in small watersheds. *Hydrology and Earth System Sciences*, *15*(7), 2377–2389. https://doi.org/10.5194/hess-15-2377-2011

Sawaske, S. R., & Freyberg, D. L. (2014). An analysis of trends in baseflow recession and low-flows in rain-dominated coastal streams of the pacific coast. *Journal of Hydrology*, *519*, 599–610. https://doi.org/10.1016/j.jhydrol.2014.07.046

Brutsaert, W., & Nieber, J. L. (1977). Regionalized drought flow hydrographs from a mature glaciated plateau. *Water Resources Research*, *13*(3), 637–643. https://doi.org/10.1029/WR013i003p00637

Poff, N. L., Allan, J. D., Bain, M. B., Karr, J. R., Prestegaard, K. L., Richter, B. D., … Stromberg, J. C. (1997). The Natural Flow Regime. *BioScience*, *47*(11), 769–784. https://doi.org/10.2307/1313099

jonesju — April 27, 2019 @ 4:59 pm

Rosemary, congratulations on getting this far. At this point I think it would be useful to identify some hypotheses about what factors would lead you to expect steep vs. flatter recession slopes in a watershed. And it seems you need to better understand why one site is producing multiple curves. But once you can generate a single value for the recession slope for each site, you can begin to look at the spatial patterns of these numbers. So for Ex 2, see if you can complete the recession slope analysis on multiple sites and create a map showing those recession slopes as color-coded points, so that you can ask whether the points with steep recession slopes are clustered or dispersed in the landscape.