CalcModPerf: Computes model performance statistics for WRF-Hydro flux...

Description Usage Arguments Details Value See Also Examples

View source: R/calculate_stats.R

Description

CalcModPerf calculates model performance statistics for flux output.

Usage

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CalcModPerf(flxDf.mod, flxDf.obs, flxCol.mod = "q_cms",
  flxCol.obs = "q_cms", stdate = NULL, enddate = NULL,
  subdivisions = 1000)

Arguments

flxDf.mod

The flux output dataframe (required). Assumes only one forecast point per file, so if you have multiple forecast points in your output dataframe, use subset to isolate a single forecast point's data. Also assumes model output and observation both contain POSIXct fields (called "POSIXct").

flxDf.obs

The observed flux dataframe. Assumes only one observation point per file, so if you have multiple observation points in your dataframe, use subset to isolate a single point's data. Also assumes model output and observation both contain POSIXct fields (called "POSIXct").

flxCol.mod

The column name for the flux time series for the MODEL data (default="q_cms")

flxCol.obs

The column name for the flux time series for the OBSERVED data (default="q_cms")

stdate

Start date for statistics (DEFAULT=NULL, all records will be used). Date MUST be specified in POSIXct format with appropriate timezone (e.g., as.POSIXct("2013-05-01 00:00:00", format="%Y-%m-%d %H:%M:%S", tz="UTC"))

enddate

End date for statistics (DEFAULT=NULL, all records will be used). Date MUST be specified in POSIXct format with appropriate timezone (e.g., as.POSIXct("2013-05-01 00:00:00", format="%Y-%m-%d %H:%M:%S", tz="UTC"))

subdivisions

Number of subdivisions used in flow duration curve integration (DEFAULT=1000); increase value if integrate function throws an error.

Details

CalcModPerf reads a model flux time series (i.e., created using ReadFrxstPts) and an observation time series (i.e., created using ReadUsgsGage) and calculates model performance statistics (Nash-Sutcliffe Efficiency, Rmse, etc.) at various time scales and for low and high fluxes. The tool will subset data to matching time periods (e.g., if the observed data is at 5-min increments and modelled data is at 1-hr increments, the tool will subset the observed data to select only observations on the matching hour break).

Performance Statistics:
(mod = model output, obs = observations, n = sample size)

Time scales/Flux types:

Value

A new dataframe containing the model performance statistics.

See Also

Other modelEvaluation: CalcModPerfMulti, CalcNoahmpFluxes, CalcNoahmpWatBudg, CalcStatCategorical, CalcStatCont

Examples

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## Take forecast point model output for Fourmile Creek (modStrh.mod1.fc) and a 
## corresponding USGS gage observation file (obsStrh.fc), both at an hourly time step, 
## and calculate model performance statistics. The model forecast point data was 
## imported using ReadFrxstPts and the gage observation data was imported using 
## ReadUsgsGage.


## Not run: 
CalcModPerf(modStrd.chrt.fc, obsStr5min.fc)

> Output:
            n      nse   nselog      cor       rmse rmsenorm    bias        msd        mae     errcom    errmaxt     errfdc
units   count unitless unitless unitless flux units unitless percent flux units flux units hours|days hours|days flux units
t         116     0.81     0.67     0.91      0.211     8.48     7.1     0.0313     0.1385       <NA>       <NA>       0.04
daily     116     0.81     0.67     0.91      0.211     8.48     7.1     0.0313     0.1385          0          0       0.04
monthly     5     0.92     0.82     0.96     0.1073    10.35     9.1     0.0324     0.0833       -1.2        0.2       <NA>
yearly      1     <NA>     <NA>     <NA>       <NA>     <NA>     7.1     0.0313     0.0313          0          4       <NA>
max10      12     0.46     0.58     0.71      0.348    23.63    -2.8     -0.044      0.248       <NA>       <NA>       <NA>
min10      12  -385.99   -14.59     0.35     0.1678   538.78   112.4      0.056     0.0592       <NA>       <NA>       <NA>

## End(Not run)

mccreigh/rwrfhydro documentation built on May 12, 2018, 3:08 a.m.