Creates datasets that will be used in loads modelling and prediction — CreateData • LRE

Creates a regularised flow dataset for prediction and a modelling dataset with flow metrics that can be used in regression modelling.

CreateData(
  Q,
  Conc,
  date.range = list(model = NULL, pred = NULL, hour = FALSE),
  samp.unit = "hour",
  Ytype = "WY",
  Qflush = 0.9,
  Reg = list(type = "ss", rainfall = NULL, date = NULL)
)

Arguments

Q: n x 2 matrix of flow (Date, Flow) where flow is measured in cumecs
Conc: $nxp$ matrix of concentrations (Date, Conc1, Conc2, ...) where concentration is measured in mg/L
date.range: list containing date ranges for modelling and prediction datasets (default: NULL)
samp.unit: sampling unit 'hour' or 'daily'
Ytype: water year "WY" or "WY2" or financial year "FY" (note only WY implemented at this stage)
Qflush: Qflush
Reg: Regularisation check

Value

a list of class 'regdata' containing: CQ flow and concentration modelling dataset Qreg regularised dataset Q Observed flow matrix Standard summary and plot functions apply.

Details

CreateData

This function creates two datasets that are used in modelling and prediction. A third dataset is just a copy of the observed flow data.

Inputs into the function are flow and concentration datasets that have been read in using the ReadInData function or are of class "data".

References

Kuhnert, P.M., Henderson, B.L., Lewis, S.E., Bainbridge, Z.T., Wilkinson, S.N. and Brodie, J.E. (2012) Quantifying total suspended sediment export from the Burdekin River catchment using the loads regression estimator tool, Water Resources Research, 48, W04533,doi:10.1029/2011WR011080.

See also

ReadInData

Examples

#Reading in data already stored in R
library(LRE)
burdR <- ReadInDataFromR(x.C = burdRC, x.Q = burdRQ)

# Create Dataset (no need for regularisation here as have full data)
date.rangeM <- c("1973-12-02", "2015-06-30")
date.rangeP <- c("1973-12-02", "2015-06-30")
loaddata <- CreateData(Q = burdR$Q, Conc = burdR$Conc,
                date.range = list(model = date.rangeM, pred = date.rangeP, hour = FALSE),
                samp.unit = "day", Ytype = "WY", Qflush = 0.9,
                Reg = list(type = "none", rainfall = NULL, date = NULL))
p <- plot(loaddata, Type = "WY")
#> `geom_smooth()` using formula = 'y ~ x'
summary(loaddata)
#> Bias in flow and concentration sampling:
#>         Year  n  AvgFlowC   AvgFlowR   AvgFlowO FlowC.bias FlowQ.bias
#> 1  1973/1974 NA        NA 2057.60410 2057.60410         NA          1
#> 2  1974/1975 NA        NA  264.52109  264.52109         NA          1
#> 3  1975/1976 NA        NA  374.91938  374.91938         NA          1
#> 4  1976/1977 NA        NA  272.58740  272.58740         NA          1
#> 5  1977/1978 NA        NA  165.90277  165.90277         NA          1
#> 6  1978/1979 NA        NA  493.41670  493.41670         NA          1
#> 7  1979/1980 NA        NA  145.24868  145.24868         NA          1
#> 8  1980/1981 NA        NA  575.67087  575.67087         NA          1
#> 9  1981/1982 NA        NA   67.75941   67.75941         NA          1
#> 10 1982/1983 NA        NA  280.63198  280.63198         NA          1
#> 11 1983/1984 NA        NA  170.57724  170.57724         NA          1
#> 12 1984/1985 NA        NA   38.01298   38.01298         NA          1
#> 13 1985/1986 NA        NA  119.32072  119.32072         NA          1
#> 14 1986/1987 NA        NA   20.82907   20.82907         NA          1
#> 15 1987/1988 NA        NA  125.83615  125.83615         NA          1
#> 16 1988/1989 NA        NA  291.20616  291.20616         NA          1
#> 17 1989/1990 NA        NA  296.43897  296.43897         NA          1
#> 18 1990/1991 NA        NA 1277.56067 1277.56067         NA          1
#> 19 1991/1992 NA        NA   16.77969   16.77969         NA          1
#> 20 1992/1993 NA        NA   17.58636   17.58636         NA          1
#> 21 1993/1994 NA        NA   92.83138   92.83138         NA          1
#> 22 1994/1995 NA        NA   24.56647   24.56647         NA          1
#> 23 1995/1996 NA        NA   68.40040   68.40040         NA          1
#> 24 1996/1997 NA        NA  275.21871  275.21871         NA          1
#> 25 1997/1998 NA        NA  286.82611  286.82611         NA          1
#> 26 1998/1999 NA        NA  190.50413  190.50413         NA          1
#> 27 1999/2000 NA        NA  437.95500  437.95500         NA          1
#> 28 2000/2001 NA        NA  277.96023  277.96023         NA          1
#> 29 2001/2002 NA        NA  142.22841  142.22841         NA          1
#> 30 2002/2003 NA        NA   66.36333   66.36333         NA          1
#> 31 2003/2004 NA        NA   47.94674   47.94674         NA          1
#> 32 2004/2005 NA        NA  137.24775  137.24775         NA          1
#> 33 2005/2006 14  824.3909   69.75343   69.75343  11.818643          1
#> 34 2006/2007 20 3633.2679  309.77088  309.77088  11.728888          1
#> 35 2007/2008 47 4513.8394  869.72241  869.72241   5.189977          1
#> 36 2008/2009 44 5459.2125  930.75820  930.75820   5.865339          1
#> 37 2009/2010 40 1335.2939  251.97980  251.97980   5.299210          1
#> 38 2010/2011 94 2654.2267 1104.36741 1104.36741   2.403391          1
#> 39 2011/2012 53  151.2060   82.44307   82.44307   1.834066          1
#> 40 2012/2013 24  578.1959  108.83642  108.83642   5.312522          1
#> 41 2013/2014 25  128.4263   46.64155   46.64155   2.753474          1
#> 42 2014/2015 16  147.4887   32.15938   32.15938   4.586180          1
#> 
#> 
#> Calculating quantiles from long term flow record with user defined cutoff.
#> No. of samples in the upper 2 percentile of flow: 72 (19% of samples collected) 
#> 
#> Distribution of flow sampling:
#>       <25%ile 25%ile-50%ile 50%ile-75%ile 75%ile-90%ile 90%ile-95%ile 
#>          0.27          2.39         14.06         20.42         18.04 
#> 95%ile-98%ile 98%ile-99%ile       >99%ile 
#>         25.73          7.43         11.67