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Gross Primary Productivity (GPP)

PEPRMT_GPP.Rd

Gross Primary Productivity (GPP) module of the PEPRMT model (v1.0). Default values were determined via MCMC Bayesian fitting (Oikawa et al. 2023).

Usage

PEPRMT_GPP(
  data,
  a0 = 0.7479271,
  a1 = 1.0497113,
  Ha = 149.468171 + 30,
  Hd = 94.4532674 + 100,
  T_opt_GPP = 25 + 274.15
)

Arguments

data

Data frame containing 15 required columns used as model inputs. See Details for expected column structure.

a0

Empirical intercept parameter for the fPAR scaling function (unitless).

a1

Empirical slope parameter for the fPAR scaling function (unitless).

Ha

Activation energy governing the temperature response of photosynthesis for general crop-type vegetation (kJ mol^-1). Controls the rate of increase in GPP with temperature below the thermal optimum.

Hd

Deactivation energy controlling the decline in photosynthesis above the thermal optimum (kJ mol^-1). Determines the rate of decrease in GPP at high temperatures.

T_opt_GPP

Temperature optimum for GPP

Value

Updated dataframe containing:

GPP

gross primary productivity (g C CO2 m^-2 d^-1)

APAR

absorbed photosynthetically active radiation (umol m^-2 d^-1)

Details

Runs the PEPRMT gross primary productivity module for freshwater peatlands or tidal wetlands at a daily time step.

The PEPRMT model was originally parameterized for restored freshwater wetlands in the Sacramento–San Joaquin River Delta, California, USA (Oikawa et al. 2017) and later updated for tidal wetlands (Oikawa et al. 2023).

Modules are intended to be run sequentially: PEPRMT_GPP, then PEPRMT_Reco, then PEPRMT_CH4.

All variables are expected at a daily time step.

This model predicts GPP using a light use efficiency equation GPP can be predicted using leaf area index (LAI) or a greenness index from Phenocam data or remote sensing such as EVI or NDVI PEPRMT-Tidal applied in Oikawa et al. 2023 uses EVI from Landsat

Required data columns:

  1. Continuous day of year

  2. Discontinuous day of year

  3. Year

  4. Air temperature (°C)

  5. Water table depth (cm)

  6. PAR (µmol m^-2 d^-1)

  7. Leaf Area Index

  8. Greenness Index

  9. FPAR flag

  10. Light Use Efficiency

  11. Wetland age (years)

  12. Salinity (ppt)

  13. NO3 (mg L^-1)

  14. Soil organic matter (g C m^-3)

  15. Site identifier

References

Oikawa, P. Y., Jenerette, G. D., Knox, S. H., Sturtevant, C., Verfaillie, J., Dronova, I., Poindexter, C. M., Eichelmann, E., & Baldocchi, D. D. (2017). Evaluation of a hierarchy of models reveals importance of substrate limitation for predicting carbon dioxide and methane exchange in restored wetlands. Journal of Geophysical Research: Biogeosciences, 122(1), 145–167. https://doi.org/10.1002/2016JG003438

Oikawa, P. Y., Sihi, D., Forbrich, I., Fluet-Chouinard, E., Najarro, M., Thomas, O., Shahan, J., Arias-Ortiz, A., Russell, S., Knox, S. H., McNicol, G., Wolfe, J., Windham-Myers, L., Stuart-Haentjens, E., Bridgham, S. D., Needelman, B., Vargas, R., Schäfer, K., Ward, E. J., Megonigal, P., & Holmquist, J. (2024). A New Coupled Biogeochemical Modeling Approach Provides Accurate Predictions of Methane and Carbon Dioxide Fluxes Across Diverse Tidal Wetlands. Journal of Geophysical Research: Biogeosciences, 129(10), e2023JG007943. https://doi.org/10.1029/2023JG007943

Examples

# Example
# data(example_dataset)
# out <- PEPRMT_GPP(theta, example_dataset, wetland_type = 2)