Evaluating CO2 effects on semi-empirical and empirical stomatal conductance simulation in land surface models

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Abstract

Ongoing changes in climate and carbon dioxide (Cs) in the atmosphere have profound effects on plant transpiration and, consequently, on the water balance. Land surface models (LSMs) reflect plant response to these changes by simulation of stomatal conductance (gs). However, the plant response is not well understood and varies with climate. In this study, the simulation of gs within different LSMs is reviewed and a new approach, a Mixed Generalized Additive Model (MGAM) for gs simulation, is developed. The alternative gs estimation is proposed as a solution for the high parameterisation uncertainty in semi-empirical gs simulation models, and high dependency on mathematical functions for environmental stress factors in empirical gs simulation models. MGAM has high Pearson and Spearman correlations (87% and 85%, respectively) and efficiency coefficients (71%), with low error values (0.07 mol/m2s) in gs simulation. The global sensitivity analysis of the MGAM approach shows the necessity of considering the interaction between Cs and other key climate variables in gs simulation. The high accuracy and low uncertainty to the first-order key climate factors in gs simulation highlight the MGAM model's importance in future studies.

Original languageEnglish
Article number129385
Number of pages13
JournalJournal of Hydrology
Volume620
DOIs
Publication statusPublished - May 2023

Keywords

  • Atmospheric carbon dioxide
  • Climate change
  • Global sensitivity analysis
  • Land surface models
  • Mixed Generalized Additive Model (MGAM)
  • Stomatal conductance simulation

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