Yields Table

Yields are provided individually for each parameter and station in a simple table format. 
Not all constituents are available for all sites.

Key to column headings:
STAID = USGS Station ID
PCODE = Parameter code:
P00600 = Total nitrogen (as N)
P00631 = Nitrate (as N)
P00665 = Total phosphorus (as P)
P00671 = Orthophosphorus (as P)
P80154 = Suspended sediment
START_YEAR = First water year of averaging period
END_YEAR = Last water year of averaging period
MEAN_YIELD = mean yield for averaging period, in pounds per acre; the method (WRTDS with Kalman filtering) for deriving the 1985-2020 yields for the 9 RIM stations differs from the method (WRTDS) used for the remaining station yields.
NYEARS = Number of years in averaging period where NYEARS = 5 (2014-2018) or NYEARS = 10 (2009-2018)

Yields are obtained by dividing the WRTDS-K annual loads (pounds) of a given constituent by the respective watershed area (acres) from which the constituent was transported (generating per-acre loads or yields).

Nutrient and suspended-sediment loads were estimated using a combination of a weighted regression approach called Weighted Regressions on Time, Discharge, and Season (WRTDS; Hirsch and others, 2010) and an autoregressive Kalman model utilizing the serial correlations from each WRTDS model (Zhang and Hirsch, 2019), both of which are included in the R (version 3.6.0) software package called EGRET - Exploration and Graphics for RivEr Trends (version 3.0.5.1; Hirsch and DeCicco, 2015). The application of WRTDS to generate the results provided in this table is documented in Chanat and others (2016).

References Cited:

Hirsch, R.M., Moyer, D.L., and Archfield, S.A., 2010, Weighted regressions on time, discharge, and season (WRTDS), with an application to Chesapeake Bay river inputs: Journal of the American Water Resources Resources Association, v. 46, no. 5, p. 857-880.

Zhang, Q. and Hirsch, R. M., 2019, River water-quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model: Water Resources Research, 55, 9705– 9723. https://doi.org/10.1029/2019WR025338

Hirsch, R.M. and De Cicco, L.A., 2015, User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data (version 2.0, February 2015): U.S. Geological Survey Techniques and Methods book 4, chap. A10, 93 p., http://dx.doi.org/10.3133/tm4A10 . (accessed May 24, 2016)

Chanat, J.G., Moyer, D.L., Blomquist, J.D., Hyer, K.E., and Langland, M.J., 2016, Application of a weighted regression model for reporting nutrient and sediment concentrations, fluxes, and trends in concentration and flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, results through water year 2012: U.S. Geological Survey Scientific Investigations Report 2015-5133, 139 p. http://dx.doi.org/10.3133/sir20155133.

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STAID PCODE START_YEAR END_YEAR MEAN_YIELD NYEARS