Frequency-aware quality control of streamflow time series

Detects the sampling frequency of a numeric zoo object and dispatches daily data to streamflowQC_daily or minute/hourly data to streamflowQC_subdaily. No files are written.

Usage

streamflowQC(
  x, metadata=NULL, station.id="station", coords=c("lon", "lat"), ...,
  area=NULL, elevation=NULL, elevation.scale=500
)

Arguments

x: A numeric zoo object with one streamflow station per column.
metadata: NULL, or a data.frame with one row per station.
station.id: Name of the station-identifier field in metadata.
coords: Names of longitude and latitude fields in metadata, in that order.
...: Arguments passed unchanged to the selected workflow.
area: NULL, or the name of a positive numeric catchment-area field. Units may be chosen by the user but must be consistent among stations.
elevation: NULL, or the name of a numeric station-elevation field.
elevation.scale: Positive elevation-decay scale used in spatial neighbour ranking.

Details

The wrapper changes only the resolution-specific workflow. Test activation, thresholds, station metadata, evidence requirements, correction policy, and station recommendations remain explicit user controls. Daily and sub-daily observations must be in consistent discharge units. Duplicate timestamps are rejected because selecting among contradictory measurements would require provenance or agency-quality information not contained in a numeric zoo object.

The design combines the point plausibility and homogeneity principles of Gudmundsson et al. (2018), the real-time hierarchy of U.S. IOOS (2018), and the sub-daily artefact and event-corroboration framework of Fileni et al. (2026).

Value

An object of class "streamflowQC"; see streamflowQC-class.

References

Fileni, F. et al. (2026). UK-Flow15-QC: A quality control framework for better river flow data in hydrological research. EGUsphere preprint. doi:10.5194/egusphere-2026-277.

Gudmundsson, L., Do, H. X., Leonard, M. and Westra, S. (2018). The Global Streamflow Indices and Metadata Archive (GSIM) - Part 2: quality control, time-series indices and homogeneity assessment. Earth System Science Data, 10, 787–804. doi:10.5194/essd-10-787-2018.

U.S. Integrated Ocean Observing System (2018). Manual for Real-Time Quality Control of Stream Flow Data Version 1.0. doi:10.25923/gszc-ha43.

Examples

dates <- as.Date("2020-01-01") + 0:29
x <- zoo::zoo(cbind(A=10 + sin(seq_along(dates)),
                    B=12 + sin(seq_along(dates))), dates)
qc <- streamflowQC(
  x, checks=c(range=TRUE, duplicate=FALSE, gap=FALSE,
              climatology=FALSE, flatline=FALSE, spike=FALSE,
              rate=FALSE, highflow=FALSE, spatial=FALSE,
              breakpoint=FALSE),
  min.years=0, max.missing=1, max.review=1, max.suspicious=1
)
qc$station.summary
#>   station expected observed missing.percent review.count review.percent
#> A       A       30       30               0            0              0
#> B       B       30       30               0            0              0
#>   suspicious.count suspicious.percent breakpoint.year breakpoint.indicator
#> A                0                  0              NA                 <NA>
#> B                0                  0              NA                 <NA>
#>   breakpoint.p.value breakpoint.relative.change breakpoint.n.indicators
#> A                 NA                         NA                       0
#> B                 NA                         NA                       0
#>   breakpoint.flag record.years recommendation                   reason
#> A           FALSE   0.08213552         accept within acceptance limits
#> B           FALSE   0.08213552         accept within acceptance limits