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)) norm_env(dat)
#> Date Data.Value score #> 1 2016-01-01 15.42027 0.0000000 #> 2 2016-01-02 14.01431 0.9856855 #> 3 2016-01-03 15.57758 0.5775767 #> 4 2016-01-04 13.14911 1.8508918 #> 5 2016-01-05 17.68658 5.3731673 #> 6 2016-01-06 15.25488 0.0000000 #> 7 2016-01-07 15.26762 0.0000000 #> 8 2016-01-08 13.67158 1.3284171 #> 9 2016-01-09 14.68021 0.0000000 #> 10 2016-01-10 14.22907 0.7709329 #> 11 2016-01-11 12.16837 2.8316342 #> 12 2016-01-12 14.79592 0.0000000 #> 13 2016-01-13 14.34019 0.6598117 #> 14 2016-01-14 12.12039 2.8796087 #> 15 2016-01-15 14.26183 0.7381744 #> 16 2016-01-16 15.35948 0.0000000 #> 17 2016-01-17 17.14250 4.2850030 #> 18 2016-01-18 14.30886 0.6428417 #> 19 2016-01-19 15.16750 0.0000000 #> 20 2016-01-20 15.60383 0.6843251 #> 21 2016-01-21 13.56823 1.3351719 #> 22 2016-01-22 13.74030 1.1469990 #> 23 2016-01-23 14.88045 0.0000000 #> 24 2016-01-24 17.52003 5.0400666 #> 25 2016-01-25 14.40432 0.0000000 #> 26 2016-01-26 13.27904 1.5438637 #> 27 2016-01-27 16.03060 2.0612018 #> 28 2016-01-28 12.35168 2.4390153 #> 29 2016-01-29 17.98241 5.9648296 #> 30 2016-01-30 12.89421 1.8642872 #> 31 2016-01-31 15.11134 0.0000000 #> 32 2016-02-01 17.07672 4.1534407 #> 33 2016-02-02 16.30962 2.6192367 #> 34 2016-02-03 13.44788 1.2462159 #> 35 2016-02-04 15.28226 0.6042602 #> 36 2016-02-05 17.00881 4.0176218 #> 37 2016-02-06 12.16774 2.4780638 #> 38 2016-02-07 14.81631 0.0000000 #> 39 2016-02-08 16.83408 3.6681604 #> 40 2016-02-09 16.88431 3.7686157 #> 41 2016-02-10 14.42347 0.0000000 #> 42 2016-02-11 13.31059 1.2547139 #> 43 2016-02-12 14.51017 0.0000000 #> 44 2016-02-13 16.01322 2.0264490 #> 45 2016-02-14 15.04590 0.5289017 #> 46 2016-02-15 15.96216 1.9243117 #> 47 2016-02-16 15.07075 0.5885979 #> 48 2016-02-17 17.01331 4.0980292 #> 49 2016-02-18 16.25269 2.6124739 #> 50 2016-02-19 17.24524 4.6332713 #> 51 2016-02-20 12.06888 2.3418728 #> 52 2016-02-21 17.32950 4.8731948 #> 53 2016-02-22 17.97808 6.2060630 #> 54 2016-02-23 15.00115 0.6439490 #> 55 2016-02-24 14.15380 0.0000000 #> 56 2016-02-25 16.64948 3.6559563 #> 57 2016-02-26 15.50685 1.4064030 #> 58 2016-02-27 15.80386 2.0361165 #> 59 2016-02-28 17.15200 4.7680939 #> 60 2016-02-29 15.40137 1.2846821 #> 61 2016-03-01 13.51798 0.7321179 #> 62 2016-03-02 17.51282 5.5611386