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.
Highlights
Land cover and land use in tropical forest regions are highly dynamic, and most tropical forests are subject to significant human influence (Martínez-Ramos et al, 2016; Dirzo et al, 2014)
This is true for sensible heat fluxes as the model simulates large seasonal variabilities in SH when compared to observations at the site
The logging disturbance leads to reductions in gross primary production (GPP), net primary production (NPP), AR, and aboveground biomass (AGB), as well as increases in ecosystem respiration (ER), net ecosystem exchange (NEE), heterotrophic respiration (HR), and coarse woody debris (CWD)
Summary
Land cover and land use in tropical forest regions are highly dynamic, and most tropical forests are subject to significant human influence (Martínez-Ramos et al, 2016; Dirzo et al, 2014). Using data from forest inventory and long-term ecosystem carbon studies from 1990 to 2007, Pan et al (2011) suggested a net tropical forest can be a net source of carbon source of 1.3 ± 0.7 Pg C yr−1 from land use change, consisting of a gross tropical deforestation loss of 2.9 ± 0.5 Pg C yr−1 that is partially offset by a carbon uptake by tropical secondary forest regrowth of 1.6 ± 0.5 Pg C yr−1 These estimates, do not account for a tropical forest that has been degraded through the combined effects of selective logging (cutting and removal of merchantable timber), fuelwood harvest, understory fires, and fragmentation (Nepstad et al, 1999; Bradshaw et al, 2009). Selective logging accelerates gap-phase regeneration within the degraded forests (Huang et al, 2008)
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