Assessing impacts of selective logging on water, energy, and carbon budgets and ecosystem dynamics in Amazon forests using the Functionally Assembled Terrestrial Ecosystem Simulator

Abstract

Abstract. Tropical forest degradation from logging, fire, and fragmentation not only alters carbon stocks and carbon fluxes, but also impacts physical land surface properties such as albedo and roughness length. Such impacts are poorly quantified to date due to difficulties in accessing and maintaining observational infrastructures, as well as the lack of proper modeling tools for capturing the interactions among biophysical properties, ecosystem demography, canopy structure, and biogeochemical cycling in tropical forests. As a first step to address these limitations, we implemented a selective logging module into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) by mimicking the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events, splitting the logged forest patch into disturbed and intact patches; determine the survivorship of cohorts in the disturbed patch; and modifying the biomass and necromass (total mass of coarse woody debris and litter) pools following logging. We parameterized the logging module to reproduce a selective logging experiment at the Tapajós National Forest in Brazil and benchmarked model outputs against available field measurements. Our results suggest that the model permits the coexistence of early and late successional functional types and realistically characterizes the seasonality of water and carbon fluxes and stocks, the forest structure and composition, and the ecosystem succession following disturbance. However, the current version of FATES overestimates water stress in the dry season and therefore fails to capture seasonal variation in latent and sensible heat fluxes. Moreover, we observed a bias towards low stem density and leaf area when compared to observations, suggesting that improvements are needed in both carbon allocation and establishment of trees. The effects of logging were assessed by different logging scenarios to represent reduced impact and conventional logging practices, both with high and low logging intensities. The model simulations suggest that in comparison to old-growth forests the logged forests rapidly recover water and energy fluxes in 1 to 3 years. In contrast, the recovery times for carbon stocks, forest structure, and composition are more than 30 years depending on logging practices and intensity. This study lays the foundation to simulate land use change and forest degradation in FATES, which will be an effective tool to directly represent forest management practices and regeneration in the context of Earth system models.

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

“…In this study, we configured two PFTs in ELM-FATES, i.e., early successional and late successional broadleaf evergreen tropical trees, which can represent a primary axis of variability in tropical forests (Huang et al, 2020;Reich, 2014;Díaz et al, 2016). There are tradeoffs between the plant traits of these two PFTs.…”

“…Observational datasets have shown some correlations between plant traits. Therefore, we derived three trait relationships based on the tropical studies of Koven et al (2020) and Longo et al (2020).…”

“…𝐿 01%2 = 0.0001 × 𝑆𝐿𝐴 (34.,4) (2) (Huang et al, 2020;Koven et al, 2020;Longo et al, 2020;Holm et al, 2020;Cheng et al, 2021;Domingues et al, 2005;Chitra-Tarak et al, 2021;Buotte et al, 2021) *𝑅 # and 𝑅 + are parameters that determine the rooting depth and vertical distribution of fine roots. *BTRAN is the plant water stress factor.…”

“…Examples of cohort-based models include the Ecosystem Demography model (ED) (Moorcroft et al, 2001), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (Fisher et al, 2018(Fisher et al, , 2015, and the Geophysical Fluid Dynamics Laboratory (GFDL) Land Model 3 with the Perfect Plasticity Approximation (LM3-PPA) (Weng et al, 2015). Among these models, FATES has been widely used in modeling ecosystem dynamics for multiple ecosystems, e.g., tropical (Holm et al, 2020;Koven et al, 2020;Cheng et al, 2021) and mixedconifer forests (Buotte et al, 2021), and forest disturbance (Huang et al, 2020). Despite ongoing applications, robust simulations of competition and coexistence in cohort-based DGVMs remain a major challenge.…”

“…Therefore, most previous studies use the filtered ensemble approach to select trait-related parameters involving several steps: 1) generate a parameter ensemble based on reference trait ranges or correlations, 2) conduct ensemble model simulations, and 3) filter the parameter ensemble by coexistence and other criteria (e.g., observation constraints). For example, Huang et al (2020) Machine learning (ML) has facilitated Earth science studies (Shen, 2018;Nearing et al, 2021;Zhu et al, 2022;Pal et al, 2019;Jung et al, 2019), possibly providing a promising approach to improve PFT coexistence modeling in DGVMs. ML algorithms have been broadly and successfully employed in recent decades.…”