Last updated: 2022-02-03

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Rmd d5fe2c9 Daniel 2022-02-03 added analysis file with some blelham calculations

Introduction

Just going to see if I can run some of my vertical mixing code on this website.

Many of the vertical mixing functions used are defined in analysis/vert_mixing_functions.R. I will try to mention which these are.

Load functions:

source('analysis/vert_mixing_functions.R')

Load in the data! Note that raw data will not be uploaded to Github. However, graphs produced from this data will appear on this website (need to do some thinking about data acknowledgement and licensing as this website is public)

############## READ DATA ################

waterDF = read.csv("Data/WT_total.csv")

waterDF_dt <- waterDF %>%
  mutate(DateTime=dmy_hm(DateTime)) %>%
  rename(datetime=DateTime) # convert dates to datetime type

# change names to "wtr_x" format
names(waterDF_dt)[2:13] = paste0("wtr_", 
                                 str_split_fixed(names(waterDF_dt)[2:13],
                                                 '_', 2)[, 2])

head(waterDF_dt)
             datetime wtr_0.5  wtr_1  wtr_2  wtr_3  wtr_4  wtr_5  wtr_6  wtr_7
1 2011-01-01 00:00:00  2.2294 3.2046 3.5745 3.5541 3.6186 3.5797 3.6936 3.6632
2 2011-01-01 01:00:00  2.2457 3.2539 3.5780 3.5526 3.5727 3.6199 3.7032 3.6625
3 2011-01-01 02:00:00  2.2668 3.3081 3.5565 3.5510 3.5728 3.6200 3.7172 3.6553
4 2011-01-01 03:00:00  2.2720 3.3400 3.5645 3.5299 3.5550 3.6193 3.7302 3.6528
5 2011-01-01 04:00:00  2.2932 3.3453 3.5699 3.5273 3.5696 3.6360 3.7211 3.6570
6 2011-01-01 05:00:00  2.3114 3.3269 3.5781 3.5068 3.5803 3.6225 3.6931 3.6732
   wtr_8  wtr_9 wtr_10 wtr_12
1 3.6414 3.6032 3.6898 3.8965
2 3.6462 3.6294 3.6963 3.9203
3 3.6475 3.6234 3.6901 3.9180
4 3.6441 3.6255 3.6880 3.9003
5 3.6494 3.6385 3.6930 3.8909
6 3.6534 3.6416 3.6981 3.8998

Now we will do some time averaging of the data using my function time_avg().

########### TIME AVERAGING ##################

wt.months = time_avg(waterDF_dt, period='1 month')
wt.weeks = time_avg(waterDF_dt, period='1 week')
wt.days = time_avg(waterDF_dt, period='1 day')
wt.3days = time_avg(waterDF_dt, period='3 day')

head(wt.3days)
    datetime  wtr_0.5    wtr_1    wtr_2    wtr_3    wtr_4    wtr_5    wtr_6
1 2011-01-01 2.546601 3.333924 3.635683 3.610696 3.649722 3.622800 3.695294
2 2011-01-04 2.758772 3.158744 3.618268 3.608710 3.649494 3.605858 3.645342
3 2011-01-07 2.697289 3.336472 3.725588 3.625882 3.710243 3.656015 3.605465
4 2011-01-10 2.758176 3.175878 3.436414 3.555443 3.632249 3.666631 3.736126
5 2011-01-13 3.507190 3.615118 3.688018 3.650301 3.689082 3.716560 3.711337
6 2011-01-16 4.851286 4.860222 4.887710 4.827058 4.835175 4.783043 4.806161
     wtr_7    wtr_8    wtr_9   wtr_10   wtr_12
1 3.663222 3.651664 3.652154 3.691436 3.990332
2 3.633679 3.657394 3.655606 3.691554 4.087107
3 3.595053 3.609350 3.613693 3.710617 4.195272
4 3.747760 3.643093 3.593026 3.654765 4.193051
5 3.668439 3.722637 3.805644 3.809021 4.163619
6 4.736678 4.655631 4.562069 4.456042 4.268310

Now, we need to load in the bathymetry.

# can approximate bathymetry based on depth and area
bathys = approx.bathy(14.5, 100000)
# need to change name of areas column for some Rlakeanalyser stuff to work
names(bathys)[2] = "areas" 

# BUT better to use bathymetry file, which is more representative! 
blelBathy = load.bathy("Data/bathy.csv")

Let’s plot the water temperature profiles for the summer of 2013.

wtr.heat.map(subset(wt.weeks, 
                    datetime <='2013-10-01' & datetime >='2013-04-01'))

Now, we will use my calcKz() function to calculate the vertical diffusivity coefficients for different time averages.

Note: It might be a good idea to display some of the calcKz() code, and other key functions somewhere on this website.

# remember to use a subset of the data if this code needs to be run often
wt.months.Kz = calcKz(wt.months, blelBathy, Zmax = 14.5)
Joining, by = "upperZ"
Joining, by = c("upperZ", "datetime")
`summarise()` has grouped output by 'datetime'. You can override using the `.groups` argument.
wt.weeks.Kz = calcKz(wt.weeks, blelBathy, Zmax = 14.5)
Joining, by = "upperZ"
Joining, by = c("upperZ", "datetime")
`summarise()` has grouped output by 'datetime'. You can override using the `.groups` argument.
wt.days.Kz = calcKz(wt.days, blelBathy, Zmax = 14.5)
Joining, by = "upperZ"
Joining, by = c("upperZ", "datetime")
`summarise()` has grouped output by 'datetime'. You can override using the `.groups` argument.
wt.3days.Kz = calcKz(wt.3days, blelBathy, Zmax = 14.5)
Joining, by = "upperZ"
Joining, by = c("upperZ", "datetime")
`summarise()` has grouped output by 'datetime'. You can override using the `.groups` argument.

sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale:
[1] LC_COLLATE=English_United Kingdom.1252 
[2] LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] zoo_1.8-9              gridExtra_2.3          scales_1.1.1          
 [4] reshape2_1.4.4         lubridate_1.8.0        forcats_0.5.1         
 [7] stringr_1.4.0          dplyr_1.0.7            purrr_0.3.4           
[10] readr_2.1.0            tidyr_1.1.4            tibble_3.1.6          
[13] ggplot2_3.3.5          tidyverse_1.3.1        rLakeAnalyzer_1.11.4.1
[16] workflowr_1.7.0       

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7       lattice_0.20-45  getPass_0.2-2    ps_1.6.0        
 [5] assertthat_0.2.1 rprojroot_2.0.2  digest_0.6.28    utf8_1.2.2      
 [9] plyr_1.8.6       R6_2.5.1         cellranger_1.1.0 backports_1.3.0 
[13] reprex_2.0.1     evaluate_0.14    highr_0.9        httr_1.4.2      
[17] pillar_1.6.4     rlang_0.4.12     readxl_1.3.1     rstudioapi_0.13 
[21] whisker_0.4      callr_3.7.0      jquerylib_0.1.4  rmarkdown_2.11  
[25] munsell_0.5.0    broom_0.7.10     compiler_4.1.1   httpuv_1.6.5    
[29] modelr_0.1.8     xfun_0.28        pkgconfig_2.0.3  htmltools_0.5.2 
[33] tidyselect_1.1.1 fansi_0.5.0      crayon_1.4.2     tzdb_0.2.0      
[37] dbplyr_2.1.1     withr_2.4.2      later_1.3.0      grid_4.1.1      
[41] jsonlite_1.7.2   gtable_0.3.0     lifecycle_1.0.1  DBI_1.1.1       
[45] git2r_0.29.0     magrittr_2.0.1   cli_3.1.0        stringi_1.7.5   
[49] fs_1.5.0         promises_1.2.0.1 xml2_1.3.2       ellipsis_0.3.2  
[53] generics_0.1.1   vctrs_0.3.8      tools_4.1.1      glue_1.5.0      
[57] hms_1.1.1        processx_3.5.2   fastmap_1.1.0    yaml_2.2.1      
[61] colorspace_2.0-2 rvest_1.0.2      knitr_1.36       haven_2.4.3