DOI: 10.7910/dvn/bbkioq
title: data set for EC 30 mins for the two sites

description: Dataset Description

This dataset and accompanying R scripts support the intensive carbon dynamics observation platform conducted in tropical alpine peatlands of Guatavita, Colombia. The data include half-hourly and cumulative greenhouse-gas fluxes (CO₂, CH₄, N₂O), dissolved organic carbon (DOC) transport, and related hydrological and meteorological measurements, together with model outputs and analysis scripts. All analyses were performed in R (version ≥ 4.2).

The repository is organized into two main components:

Chamber and Bayesian analysis pipeline (root folder)

Tower flux gap-filling and uncertainty analysis (folder golden/)

1. Chamber and Bayesian Workflow

This section integrates chamber measurements, water-table data, and modeled fluxes for both conserved and degraded peatland plots. The scripts allow data preparation, prediction of half-hourly fluxes, Bayesian partitioning of net ecosystem exchange (NEE) into gross primary production (GPP) and ecosystem respiration (ER), and generation of publication-quality figures.

Main steps:

Data preparation – Cleaning and merging chamber and tower data (flux_chamber3.r, flux_wt_guatavita_jc.r, waterlevel.r).

Prediction dataset construction – Builds model input datasets (flux predict.R, flux predict2.R).

Bayesian flux partitioning – Separates NEE into GPP and ER using hierarchical Bayesian models (bayesian models.r, bayesianflux.r).

This step must be run separately for each station (ST1 and ST2) by modifying the station code inside the scripts.

Trace gas analyses – Quantifies N₂O and DOC fluxes (N2Oflux.r, DOC_flux.r).

Visualization and summaries – Produces the cumulative and seasonal flux figures and summary tables (final plot.r).

Primary outputs:

Modelled CO₂ and CH₄ fluxes (*_Model_EC_long.csv, *_pred_30min_*.csv)

Seasonal and cumulative carbon balance summaries (Final_Cumulative_CO2_CH4_CO2eq_2023_2024_bySeason_Method_Station.csv, Summary_CO2_CH4_CO2eq_byMethod_Station_Season_Year.csv)

Mean and confidence-interval tables for each gas (PerGas_CO2_CH4_with_CO2eq_Mg_ha_mean95CI.csv, Totals_CO2eq_across_gases_Mg_ha_mean95CI.csv)

Publication figures (figure.png, figure_transparent.png, figure.svg)

2. Tower Flux (Eddy-Covariance) Workflow

The folder golden/ contains the workflow used for tower-based fluxes, including gap-filling, uncertainty analysis, and manuscript-quality visualization. These scripts use the REddyProc R package and standard meteorological variables.

Scripts:

REddyProc_Guatavita_Station1_Gold.R – Gap-filling for Station 1

REddyProc_Guatavita_Station2_Gold.R – Gap-filling for Station 2

Guatavita_gapfilling_uncertainty.R – Quantifies gap-filling uncertainty

Guatavita_plot_manuscript.R – Generates final tower flux figures

Each station’s eddy-covariance data were processed independently following standard u-star filtering and uncertainty propagation routines.

Data Files

Input data include chamber fluxes (co2flux.csv, ch4flux.csv, db_gutavita_N2O_all.csv), water-table and hydrological measurements (WaterTable.csv, wtd_martos_21_25.csv), DOC transport (DOC transport.csv), and auxiliary meteorological variables (tower_var.csv).
Intermediate model results are stored in .rds files, and cumulative or seasonal summaries are provided in .csv and .xlsx formats.

Reproducibility Notes

All scripts assume relative paths from the project root. To reproduce the complete analyses:

Install required R packages (tidyverse, ggplot2, rjags, coda, REddyProc, among others).

Run the chamber workflow in the order listed above.

Repeat the Bayesian modeling step for both stations.

Execute the tower scripts in the golden/ folder for gap-filling and visualization.

Large intermediate .rds files are retained for reproducibility and should not be deleted unless re-running the models from scratch.

Citation and Contact

Principal Investigator:
Juan C. Benavides, Pontificia Universidad Javeriana, Bogotá, Colombia