Recovery Lake Stage Envelope Score

rec_env(stg.data)

Arguments

stg.data

see details

Value

Returns a data.frame of original data and normal stage elevation score.

Details

The input stg.data is a data.frame with columns:

  • Date (as a POSIXct or Date variable)

  • Data.Value as stage elevation data in feet (NGVD29)

Examples

# Example dataset (not real data) dat=data.frame(Date=seq(as.Date("2016-01-01"),as.Date("2016-03-02"),"1 days"), Data.Value=runif(62,12,18)) rec_env(dat)
#> Date Data.Value penalty #> 1 2016-01-01 15.42027 0.9601349 #> 2 2016-01-02 14.01431 0.0000000 #> 3 2016-01-03 15.57758 2.1551535 #> 4 2016-01-04 13.14911 -1.3508918 #> 5 2016-01-05 17.68658 6.3731673 #> 6 2016-01-06 15.25488 0.8774412 #> 7 2016-01-07 15.26762 0.8838102 #> 8 2016-01-08 13.67158 0.0000000 #> 9 2016-01-09 14.68021 0.5901074 #> 10 2016-01-10 14.22907 0.0000000 #> 11 2016-01-11 12.16837 -2.3316342 #> 12 2016-01-12 14.79592 0.6479616 #> 13 2016-01-13 14.34019 0.0000000 #> 14 2016-01-14 12.12039 -2.3796087 #> 15 2016-01-15 14.26183 0.0000000 #> 16 2016-01-16 15.35948 0.9377885 #> 17 2016-01-17 17.14250 5.2850030 #> 18 2016-01-18 14.30886 0.0000000 #> 19 2016-01-19 15.16750 0.8659511 #> 20 2016-01-20 15.60383 2.2076503 #> 21 2016-01-21 13.56823 0.0000000 #> 22 2016-01-22 13.74030 0.0000000 #> 23 2016-01-23 14.88045 0.7546255 #> 24 2016-01-24 17.52003 6.0400666 #> 25 2016-01-25 14.40432 0.5326606 #> 26 2016-01-26 13.27904 -1.0438637 #> 27 2016-01-27 16.03060 3.0612018 #> 28 2016-01-28 12.35168 -1.9390153 #> 29 2016-01-29 17.98241 6.9648296 #> 30 2016-01-30 12.89421 -1.3642872 #> 31 2016-01-31 15.11134 0.9344699 #> 32 2016-02-01 17.07672 5.1534407 #> 33 2016-02-02 16.30962 3.6192367 #> 34 2016-02-03 13.44788 0.0000000 #> 35 2016-02-04 15.28226 1.3237895 #> 36 2016-02-05 17.00881 5.0176218 #> 37 2016-02-06 12.16774 -1.9780638 #> 38 2016-02-07 14.81631 0.8433029 #> 39 2016-02-08 16.83408 4.6681604 #> 40 2016-02-09 16.88431 4.7686157 #> 41 2016-02-10 14.42347 0.6710330 #> 42 2016-02-11 13.31059 0.0000000 #> 43 2016-02-12 14.51017 0.7304842 #> 44 2016-02-13 16.01322 3.0264490 #> 45 2016-02-14 15.04590 1.0598379 #> 46 2016-02-15 15.96216 2.9243117 #> 47 2016-02-16 15.07075 1.1771958 #> 48 2016-02-17 17.01331 5.0980292 #> 49 2016-02-18 16.25269 3.6124739 #> 50 2016-02-19 17.24524 5.6332713 #> 51 2016-02-20 12.06888 -1.8418728 #> 52 2016-02-21 17.32950 5.8731948 #> 53 2016-02-22 17.97808 7.2060630 #> 54 2016-02-23 15.00115 1.2878980 #> 55 2016-02-24 14.15380 0.6572261 #> 56 2016-02-25 16.64948 4.6559563 #> 57 2016-02-26 15.50685 2.4064030 #> 58 2016-02-27 15.80386 3.0361165 #> 59 2016-02-28 17.15200 5.7680939 #> 60 2016-02-29 15.40137 2.2846821 #> 61 2016-03-01 13.51798 0.0000000 #> 62 2016-03-02 17.51282 6.5611386