04 Jan 2023
 | 04 Jan 2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles Koven, Jennifer Holm, Rosie Fisher, Nate McDowell, Jeffrey Chambers, and Ruby Leung

Abstract. Tropical forest dynamics play crucial roles in the global carbon, water, and energy cycles. Dynamic global vegetation models are the primary tools to simulate terrestrial ecosystem dynamics and their response to climate change. However, realistically simulating the dynamics of competition and coexistence of differing plant functional traits within tropical forests remains a significant challenge. This study aims to improve modeling of plant functional type (PFT) coexistence in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a vegetation demography model implemented in the Energy Exascale Earth System Model (E3SM) land model (ELM), ELM-FATES. Specifically, we explore: (1) whether plant trait relationships established from field measurements can constrain ELM-FATES simulations; and (2) whether machine learning based surrogate models can emulate the complex ELM-FATES model and optimize parameter selections to improve PFT coexistence modeling. We conducted ELM-FATES experiments for a tropical forest site near Manaus, Brazil. We first conducted two ensembles of ELM-FATES experiments, without (Exp-1) and with (Exp-2) consideration of observed trait relationships, respectively. Considering the observed trait relationships (Exp-2) slightly improves ELM-FATES simulations of water, energy, and carbon fluxes, but degrades the simulation of PFT coexistence. Using eXtreme Gradient Boosting (XGBoost) based surrogate models trained on Exp-1, we optimize the trait-related parameters in ELM-FATES to enable PFT coexistence and reduce model errors relative to the field observations. We used parameters selected by the surrogate model to conduct another ensemble of ELM-FATES experiments (Exp-3). The probability of experiments yielding PFT coexistence greatly increases from 21 % in Exp-1 to 73 % in Exp-3. Further filtering those experiments that allow for PFT coexistence to agree within 15 % of the observations, Exp-3 still has 33 % of experiments left, much higher than the 1.4 % in Exp-1. Exp-3 also better reproduces the annual means and seasonal variations of water, energy and carbon fluxes, and the field inventory of above ground biomass. Our study demonstrates the benefits of using machine learning models to improve PFT coexistence modeling in ELM-FATES, with important implications for modeling the response and feedback of ecosystem dynamics to climate change. Our results also suggest that new mechanisms are required for robust simulation of coexisting plants in FATES.

Lingcheng Li et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1286', Anonymous Referee #1, 01 Feb 2023
    • AC1: 'Reply on RC1', Lingcheng Li, 31 Mar 2023
  • RC2: 'Comment on egusphere-2022-1286', Gregory Duveiller, 15 Feb 2023

Lingcheng Li et al.


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Short summary
Realistically modeling plant coexistence is still challenging in global dynamic vegetation models (e.g., ELM-FATES). We develop machine learning-based surrogate models to optimize plant trait parameters in ELM-FATES, significantly improving the modeling of plant coexistence and reducing model errors. Our study also suggests new mechanisms of development are required in ELM-FATES and provides important implications for modeling the response and feedback of ecosystem dynamics to climate change.