Simulating Forest Carbon-Water Fluxes in Land Surface Models through Eco-Evolutionary Optimality Principles

Reliable simulation of carbon and water fluxes in forest ecosystems is essential for understanding global energy, carbon, and water cycles, while it remains limited by the large number of poorly constrained parameters in land surface models, particularly in regions lacking flux observations. While model-data integration using satellite and eddy covariance data has improved performance, it does not resolve the fundamental problem of parameter identifiability.

Here, we use SINDBAD (Koirala et al., 2025), a model-data integration framework, to evaluate whether eco evolutionary optimality (EEO) principles can act as effective constraints on a coupled carbon water land surface model when flux observations are unavailable. Using 37 forest sites worldwide spanning 1979-2017, we compare three experiments that differ in the type of constraints applied, i.e., vegetation structure only, vegetation structure plus flux observations, and vegetation structure plus EEO based constraints, to assess to what extent theoretical optimality principles can help even without direct flux information.

We find that vegetation structure alone is insufficient to reproduce observed carbon and water fluxes, especially at water limited sites. Incorporating EEO constraints leads to clear improvements in simulations of gross primary productivity, ecosystem respiration, and evapotranspiration under water limitation, while effects are weaker at energy limited sites. EEO constrained simulations also show more realistic sensitivities of fluxes to precipitation and temperature, in some cases exceeding those obtained when flux observations are directly assimilated.

These results suggest that eco evolutionary optimality principles can provide meaningful constraints on land surface models with high dimensional parameter spaces, reducing effective parameter uncertainty under data sparse conditions.