GSA Workshop G16

rLakeAnalyzer

Standardized methods for calculating common important derived physical features of lakes.

LakeMetabolizer

A collection of tools for the calculation of freewater metabolism.

• Derivation of lake mixing and stratification indices from high-resolution lake buoy data (Read, Jordan, Hamilton, David, Jones, Ian, Muraoka, Kohji, Kroiss, Ryan, Wu, Chin, Gaiser, Evelyn)
• Jennie Brentrup for the idea
• R Package Collaborators: Jordan Read, Richard Woolway, Jennifer Brentrup, Taylor Leach, Jake Zwart

• Multiple models of lake metabolism calculation based on diverse statistical underpinnings (In Prep) Luke A Winslow, Jacob A. Zwart, Ryan D. Batt, Hilary Dugan, R. Iestyn Woolway, Jessica Corman, Paul C Hanson, Aline Jaimes, Jordan S Read

• GLEON Fellows and Fellowship leadership

1. Become familiar with open-source R tools for Limnology

   • What they do
   • Where to get them
   • How to get started

2. Learn open and collaborative development

   • Where we work together
   • How to contribute

-Available on CRAN

install.packages('rLakeAnalyzer')
install.packages('LakeMetabolizer')

library(rLakeAnalyzer)
library(LakeMetabolizer)

R-version of original Lake Analyzer

Lake Physics

Tools for gas exchange and metabolism estimation

1. Become familiar with open-source R tools for Limnology

   • What they do
   • Where to get them
   • How to get started

2. Learn open and collaborative development

   • Where and how we work together
   • How to contribute

Online Demo

1. Become familiar with open-source R tools for Limnology

   • What they do
   • Where to get them
   • How to get started

2. Learn open and collaborative development

   • Where and how we work together
   • How to contribute

1. Sign-up for free Github account
2. Go to http://github.com/GLEON/gsa_workshop
3. Create a new “Issue”
   • What is your goal for G16
4. Look through the created issues and comment on one.

1. Testing
2. Coding
3. Writing examples
4. Writing documentation
5. Creating visualizations
6. …

1. Become familiar with open-source R tools for Limnology

   • What they do
   • Where to get them
   • How to get started

2. Learn open and collaborative development

   • Where and how we work together
   • How to contribute

wtr = c(22.51,22.42,22.4,22.4,22.4,22.36,
        22.3, 22.21, 22.11, 21.23, 16.42, 
        15.15,14.24,13.35,10.94,10.43, 
    10.36,9.94,9.45,9.1,8.91,8.58,8.43)
depths = c(0, 0.5, 1, 1.5, 2, 3,4,5,6,7, 
           8, 9, 10, 11, 12,13,14,15,16, 
          17,18,19,20)
plot(wtr, depths, type='o', ylim=c(16,0))

?thermo.depth

These functions operate on single timesteps

• thermo.depth
• meta.depths
• wedderburn.number
• schmidt.stability
• lake.number
• buoyancy.freq

?thermo.depth

All start with “ts.*”

• ts.buoyancy.freq
• ts.lake.number
• ts.meta.depths
• ts.schmidt.stability
• ts.thermo.depth
• ts.wedderburn.number

?thermo.depth

Use load.ts to load data into proper format

?load.bathy
?load.ts

wtr.path <- system.file('extdata', 'Sparkling.wtr', package="rLakeAnalyzer")
wtr = load.ts(wtr.path)
head(wtr[,1:3])

             datetime wtr_0.0 wtr_0.5
1 2009-05-02 10:00:00   6.555   6.552
2 2009-05-02 10:30:00   6.555   6.505
3 2009-05-02 11:00:00   6.555   6.540
4 2009-05-02 11:30:00   6.745   6.575
5 2009-05-02 12:00:00   6.775   6.575
6 2009-05-02 12:30:00   6.685   6.635

File format is important

file.show(wtr.path)

fpath <- system.file('extdata', 'Sparkling.bth', package="rLakeAnalyzer")  
sparkling.bathy = load.bathy(fpath)
head(sparkling.bathy)

  depths    areas
1      0 583054.0
2      1 549139.5
3      2 519084.9
4      3 496773.0
5      4 477533.7
6      5 455936.5

File format is important (See Vignette)

fpath

[1] "C:/Users/lwinslow/Documents/R/win-library/3.1/rLakeAnalyzer/extdata/Sparkling.bth"

wtr.path

[1] "C:/Users/lwinslow/Documents/R/win-library/3.1/rLakeAnalyzer/extdata/Sparkling.wtr"

ts.meta.depths(wtr[1:4,])

             datetime       top    bottom
1 2009-05-02 10:00:00 0.7583432 0.7583432
2 2009-05-02 10:30:00 7.4634060 7.4634060
3 2009-05-02 11:00:00 7.6274722 7.6274722
4 2009-05-02 11:30:00 0.2500010 0.2500010

ts.thermo.depth(wtr[1:4,])

             datetime thermo.depth
1 2009-05-02 10:00:00    0.7583422
2 2009-05-02 10:30:00    7.4634050
3 2009-05-02 11:00:00    7.6274712
4 2009-05-02 11:30:00    0.2500000

wtr.path <- system.file('extdata', 'Sparkling.daily.wtr', package="rLakeAnalyzer")
wtr=load.ts(wtr.path)
wtr.plot.temp(wtr)

For further info see:

• Derivation of lake mixing and stratification indices from high-resolution lake buoy data
• CRAN Packge page
• Github Page

Two main groups

• k.*
• metab.*
• supporting functions

Gas Exchange coefficients (timeseries)

• k.cole
• k.crusius
• k.macIntyre
• k.read

?k.cole

Metabolism functions (Single day)

• metab.mle
• metab.ols
• metab.bookkeep
• metab.kalman
• metab.bayesian

Contain detailed documentation

metab

Calculates metabolism daily across a timeseries

Click to download

1. Discuss LakeMetabolizer Data Model
2. Go over example code
3. (Challenge 1) Run example code
4. (Challenge 2) Post metabolism figure you made
5. (Challenge 3) Change the k.gas model
6. (Challenge 4) Use Metabolizer on your data