Process-Based Evaluation of Tropical Forest Responses to Drought at the Amazon Tall Tower Observatory

Tropical forests play a central role in regulating the Earth's climate and the global carbon cycle, yet their accurate representation in terrestrial biosphere models (TBMs) remains a challenge: High species diversity, strong climatic variability, and lack of long term observations lead to high parameter uncertainty and hinder model calibration. Consequently, many questions regarding the long-term carbon storage capacity of tropical forests and especially the Amazon, as well as their vulnerability to extreme events, particularly droughts, remain open. 

In this study, we apply the TBM QUINCY (Thum et al., 2019), with a new implementation of plant hydraulics, to simulate seasonal and interannual vegetation dynamics at the Amazon Tall Tower Observatory (ATTO, http://attoproject.org), central Amazon, Brazil. The model is evaluated using eddy covariance data (NEE, GPP, ET) from 2014 to 2023 and complementary observational data including time series of soil water content, sap flow, and dendrometer measurements. This evaluation allows us to assess the representation of soil and plant water dynamics and to identify model limitations.

Specifically, our objective is to identify systematic mismatches between modeled processes and observations, in order to support targeted model development and parameterization. By linking uncertainties in carbon and water fluxes to specific model components and processes, we aim to establish a structured pathway toward improving TBM performance at ATTO, and to better understand the ecosystems sensitivity to drought under future climate change.