547

ID:

Introduction

Under climate change, altered tropical cyclone regimes could cause long-lasting effects on coastal tropical forest structure, composition, and function. A pantropical meta-analysis of 48 case studies in 24 tropical forests affected by 20 cyclones indicated that total litterfall carbon (C) flux increased from ~1.2 ± 0.14 to 10.8 ± 1.44 g C/m2/day due to cyclones, and reached pre-disturbance levels within one-year post-disturbance. Predicting the large variation in cyclone response across pantropical sites, leveraging site-level ground and remote-sensing observations, is key to predictive modeling of tropical forest response to future cyclones.

Objectives / Hypothesis

To enable dynamic vegetation model predictions of post-cyclone forest biomass C fluxes, we combined ground and remote-sensing observations in a tropical forest affected by hurricanes Hugo and Maria. We compared ground and remote-sensing observations with simulated litterfall C flux and LAI. We expected that, by assigning different canopy damage and mortality rates to shade-tolerant and light-demanding plant functional types (PFTs), simulated litterfall and LAI data would match observations.

Methods

We compiled litterfall C flux and MODIS LAI 500-m data from Bisley forest (Puerto Rico) before and over 2 years after Hugo and Maria. We calculated hurricane-induced changes in litterfall C flux and LAI and compared with changes in litterfall C flux and LAI from simulations of the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) coupled with the Energy Exascale Earth System Model (E3SM) land model (ELM-FATES). Following a 300-year spinup with ELM-FATES, we implemented hurricane disturbance with 100% defoliation, 20% structural biomass reduction, and varied mortality rates by referring to Hugo and Maria effects in Bisley.

Results

By imposing 80% mortality of light-demanding and 50% of shade-tolerant PFTs to simulate the impact of a large hurricane on Bisley, simulated LAI decreased by 55.3%, while remotely-sensed LAI after Maria decreased by 59% relative to pre-hurricane levels. Ground observations suggested a 450-fold increase in total litterfall C flux post-Hugo, representing an instantaneous cyclone-caused input ~1.3 times the average annual litterfall of ~417 g C/m2/year. Changes in simulated litterfall C flux due to a large hurricane were fifteen times greater than ground observations following Maria, and 2.5 times smaller than observations following Hugo.

Implications / Conclusions

Ongoing work is focused on benchmarking ELM-FATES predictions against ground and remote-sensing data. To achieve reliable simulations of the effects of altered cyclone regimes on tropical forests, observational data and relationships provide critical benchmarks and inputs.

Keywords:

Dynamic vegetation model, Ecosystem function, Hurricane, Leaf area index, Litterfall.

Barbara Bomfim, Mingjie Shi, Dellena Bloom, Yanlei Feng, Michael Keller, Lara Kueppers

Presentation within symposium:

S-17 Linking field-oriented ecology and ecologists with land surface models and modelers

Leveraging multi-source forest data to predict cyclone responses in tropical forest

-Review-