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rTRIPLEXCWFlux: An R package for carbon–water coupling model to simulate net ecosystem productivity and evapotranspiration in forests.
- Source :
-
Environmental Modelling & Software . Apr2023, Vol. 162, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Carbon and water cycles in forest ecosystems are tightly coupled, but global warming-induced soil and atmospheric droughts alter the coupling, thereby greatly increasing uncertainty in predicting carbon and water cycles. Therefore, a carbon–water coupled model (TRIPLEX-CW-Flux) was developed, and an R package (rTRIPLEXCWFlux) was created to facilitate model application. TRIPLEX-CW-Flux integrates vapor pressure deficit and soil moisture into a stomatal conductance submodule to estimate forest carbon and water fluxes. Prediction accuracy of TRIPLEX-CW-Flux and rTRIPLEXCWFlux application were evaluated in a Chinese fir (Cunninghamia lanceolata) plantation. Simulated net ecosystem production (NEP) and evapotranspiration (ET) were in good agreement with flux observations (R 2 : 0.76 for NEP ; 0.71 for ET). Thus, the TRIPLEX-CW-Flux model can be used to predict and quantify effects of global warming-induced droughts on forest carbon and water cycles. The open-access rTRIPLEXCWFlux package facilitates estimations of carbon sequestration and water consumption in forest ecosystems using the observed flux data. • A physiological process model that couples carbon and water flux was developed. • Both atmospheric and soil drought stress were also integrated into the model. • An R package was encoded for the model to predict forest carbon and water flux. • The simulated values were comparable to the flux data observed in the forest. • This R package can be used to evaluate forest functions under climate change. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13648152
- Volume :
- 162
- Database :
- Academic Search Index
- Journal :
- Environmental Modelling & Software
- Publication Type :
- Academic Journal
- Accession number :
- 162477700
- Full Text :
- https://doi.org/10.1016/j.envsoft.2023.105661