Idiosyncratic phenology of greenhouse gas emissions in a Mediterranean reservoir
Data files
Apr 29, 2024 version files 257.08 KB
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analysis.R
48.69 KB
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clean_data.csv
47.98 KB
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flux_calculations.R
6.63 KB
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fluxes_example.xlsx
104.23 KB
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Fluxlat.xlsx
45.89 KB
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README.md
3.66 KB
Apr 30, 2024 version files 147.89 KB
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clean_data.csv
33.38 KB
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flux_calculations.R
6.63 KB
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fluxes_example.xlsx
104.23 KB
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README.md
3.65 KB
May 02, 2024 version files 259.23 KB
Abstract
Extreme hydrological and thermal regimes characterize the Mediterranean biome and can significantly impact the phenology of greenhouse gas (GHG) emissions in reservoirs. Our study examined the seasonal changes in GHG emissions of a shallow, eutrophic, hardwater reservoir in Spain. We observed distinctive seasonal patterns for each gas. CH4 emissions substantially increased during stratification, influenced predominantly by the rise of water temperature and gross primary production and the drop in reservoir mean depth. N2O emissions mirrored CH4's seasonal trend, significantly correlating to water temperature, wind speed, and net primary production. Conversely, CO2 emissions decreased during stratification and displayed a quadratic, rather than a linear relationship with water temperature -an unexpected deviation from CH4 and N2O emission patterns- likely associated with calcite formation coupled to photosynthesis. This investigation highlights the need to integrate these idiosyncratic patterns into GHG emissions models, enhancing the prediction of global GHG emissions in the global change era.
README: Idiosyncratic phenology of greenhouse gas emissions in a Mediterranean reservoir
https://doi.org/10.5061/dryad.cnp5hqcbz
Contains 5 files:
(1) analysis.r - R script to replicates analyses and the figures in the paper and Supplementary Material:
(2) flux_calculations.r - R script to determine CO2, CH4 and N2O fluxes from floating chamber data.
(3) Datasets (3 in total):
(3.1). clean_data.txt: Comprises the analyzed dataset to reproduce figures and models from the manuscript.
Columns:
date_time: date and time (year-month-day hour:min: sec)
rep: sample replicate
flux_N2O: Nitrous oxide flux (ug N / m2 day)
flux_CO2: Carbon dioxide flux (mg C / m2 day)
flux_CH4_diffusive: Methane diffusive flux (mg C / m2 day)
flux_CH4_ebullitive: Methane ebullitive flux (mg C / m2 day)
flux_CH4_total: Total methane flux (mg C / m2 day)
water_level: water level in m
wind_speed: wind speed in m /s
ta: water temperature in celsius degree
GPPC: Gross primary production in mg C / L day
NPPC: Net primary production in mg C / L day
CO2eq: Radiative forcing in carbon dioxide equivalents
date: date in year-month-day
(3.2). Fluxlat.xlsx: Dataset with fluxes from the literature at a global scale including data from this study. Dataset contains 4 sheets, 3 for each gas, and a forth one including the units.
Columns:
lat: latitude (degree)
lon: longitude (degree)
min: minimum flux value reported (see gases units below)
max: maximum flux value reported (see gases units below)
Reference: reference from where fluxes were obtained
sheet CO2: Carbon dioxide flux (mg C / m2 day)
sheet CH4_diffusive: Methane diffusive flux (mg C / m2 day)
sheet N2O: Nitrous oxide flux (ug N / m2 day)
(3.3). fluxes_example.xlsx: Example dataset for calculating GHG fluxes using the R code “flux_calculations.r”. Dataset contain 3 sheets, including raw data for fluxes, environmental data and the units.
Columns:
Date: date (d-m-y)
time: time in seconds (one measurement per second)
rep: replicate
N2O_dry: Nitrous oxide in ppm
CO2_dry: Carbon dioxide in ppm
CH4_dry: Methane in ppm
V: Volume of the floating chamber (m3)
A: Area of the floating chamber (m2)
P: Atmospheric pressure (Pa)
R: Universal gas constant (m3 Pa / K mol)
T: Temperature (Kelvin degree)
Methods
This study was conducted at the eutrophic Cubillas reservoir in southern Spain, from March 2021 to July 2022. It focused on weekly monitoring of CO2, CH4, and N2O emissions, capturing both diffusive and ebullitive fluxes. Measurements of these greenhouse gases were taken at the reservoir's surface using a Cavity Ring-Down Spectrometer (PICARRO G2508) connected to a floating chamber, with 4 to 6 readings recorded daily during daylight hours. In addition to greenhouse gas monitoring, the study also involved assessing environmental and biological factors that influence the seasonal patterns of these gases. This included measuring water temperature, oxygen concentration, depth, and wind speed. Furthermore, nitrate levels, Gross Primary Production (GPP), respiration (Res), and Net Ecosystem Production (NEP) were also systematically measured and analyzed. Finally, a multiple mixed linear model approach was employed to identify the primary drivers of greenhouse gas (GHG) emissions.