Spring memory

Summary

An analysis of how external TP inputs influence in-lake TP across WI lakes.

Data availability

• In-lake data for this study published on the Environmental Data Initative data portal: https://doi.org/10.6073/pasta/2cd6628a942de2a8b12d2b19962712a0
• Additional data are presented in ./External data:
  • Air temperature and climate data are drawn from the ERA5 climate reanalysis. ERA5 is a fifth-generation product from the European Centre for Medium-Range Weather Forecasts (ECMWF), and provides data from 19592022 on a 0.25 degree global grid (Hersbach et al., 2019)
  • Chlorophyll-a data from Filazzola et al. (2020) were added for n = 15 lakes without other data for chlorophyll-a
  • We collated additional metadata for each lake using HydroLAKES, a global database of 1.4 million lakes (with surface area 10 ha; Messager et al., 2016)

Repo content information

./Data processing

Data loading/processing scripts: * 01 - Load climate - ERA5.Rmd * Load netcdf of global climate data and output a csv with air temperature records at each lake * 02 - Temp and DO interpolation.Rmd * Interpolate temperature and oxygen profiles to a 1 m depth resolution * 03 - Stratified avgs.Rmd * Calculate epilimnetic and hypolimnetic means during the stratified period of each year. Add in additional chlorophyll-a data using "chla_harmonizer.csv" * 04 - Summer avgs.Rmd * Calculate epilimnetic and hypolimnetic means during the late-summer period of each year * 05 - hydrolakes.Rmd * Load all hydrolakes data and output a csv with hydrolakes metadata for each lake * 06 - VW DO Demand - based on strat dur.Rmd * Calculate volume-weighted hypolimnetic oxygen demand for each lake-year * 07 - Compile data.Rmd * Combine late-summer and stratified means with oxygen demand, and climate data. Output a file for following analyses. * 08 - Anoxic factor.Rmd * Calculate anoxic factor for the entire hypolimnion * 09 - Anoxic factor layers.Rmd * Calculate anoxic factor in two hypolimnetic layers

./Data analysis

All data analysis scripts:

• 01 - Data characterization.Rmd
  • Characterize the full, synthesized dataset. Output summary figures

./External data

Downloaded data (unmodified from original sources)

./Compiled data

Compiled datasets, created by the scripts in ./Data analysis

./Figures

Figures created by the scripts in ./Data analysis

./Illustrator files

Adobe illustrator files used to create conceputal figure, graphical abstract, and annotated figures for manuscript

References

Filazzola, A., Mahdiyan, O., Shuvo, A., Ewins, C., Moslenko, L., Sadid, T., Blagrave, K., Gray, D., Quinlan, R., OReilly, C., & Sharma, S. (2020). A global database of chlorophyll and water chemistry in freshwater lakes. https://doi.org/10.5063/F1JH3JKZ

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hornyi, A., Muoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., & Thpaut, J.-N. (2019). ERA5 monthly averaged data on single levels from 1979 to present [Data set]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/CDS.F17050D7

Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7(1), Article 1. https://doi.org/10.1038/ncomms13603