fa | R Documentation |
Flow anomalies are a dimensionless term that reflects the difference in in current discharges compared to past discharges. A positive flow anomaly indicates the current time period, T_{1}, is wetter than the precedent time period, T_{2}.
fa(discharge, dates, T_1, T_2, clean_up = FALSE, transform = "log10")
discharge |
numeric vector of daily discharges |
dates |
vector of dates coresponding to daily discharge measurements.
Must be class |
T_1 |
size of period T_{1}
preceding a given day t. Specified in the same way as the |
T_2 |
size of period T_{2}
preceding a given day t. Specified in the same way as the |
clean_up |
logical. runs .... prior to .... |
transform |
on of |
The FA term describes how different the antecedent discharge conditions are for a selected temporal period compared to a selected period or day of analysis. Ryberg and Vecchia (2014) and Vechia et al. (2009) describe the flow anomaly (FA) term as:
FA(t)=X_{T_1}(t) - X_{T_2}(t)
The T_1
and T_2
arguments can be specified as character strings
containing one of "sec"
, "min"
, "hour"
, "day"
,
"DSTday"
, "week"
, "month"
, "quarter"
, or
"year"
. This is generally preceded by an integer and a space. Can also
be followed by an "s"
. Additionally, T_2
accepts
"period"
which coresponds with the mean of the entire flow record.
vector of numeric values corresponding to X_{T_1}(t) - X_{T_2}(t).
Ryberg, Karen R., and Aldo V. Vecchia. 2012. “WaterData—An R Package for Retrieval, Analysis, and Anomaly Calculation of Daily Hydrologic Time Series Data.” Open Filer Report 2012-1168. National Water-Quality Assessment Program. Reston, VA: USGS. https://pubs.usgs.gov/of/2012/1168/.
Vecchia, Aldo V., Robert J. Gilliom, Daniel J. Sullivan, David L. Lorenz, and Jeffrey D. Martin. 2009. “Trends in Concentrations and Use of Agricultural Herbicides for Corn Belt Rivers, 1996-2006.” Environmental Science & Technology 43 (24): 9096–9102. doi: 10.1021/es902122j.
## examples from Ryberg & Vechia 2012 ## Long-term Flow Anomaly LTFA LTFA <- fa(lavaca$Flow, dates = lavaca$Date, T_1 = "1 year", T_2 = "period", clean_up = TRUE, transform = "log10") ## Mid-term Flow Anomaly MTFA MTFA <- fa(lavaca$Flow, dates = lavaca$Date, T_1 = "1 month", T_2 = "1 year", clean_up = TRUE, transform = "log10") ## Short-term Flow Anomaly STFA STFA <- fa(lavaca$Flow, dates = lavaca$Date, T_1 = "1 day", T_2 = "1 month", clean_up = TRUE, transform = "log10")
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