Understanding Tropical Forest Response to Seasonal and Interannual Variability: The Goldilocks Problem

Abstract:

Quantifying our understanding of tropical forest response to seasonal cycles of precipitation, and variability around the annual mean, is an ongoing problem. A decade ago, computer models were unable to reproduce forest behavior at some forests in tropical South America, with the result that ecophysiological function collapsed and Bowen ratio spiked unrealistially during the dry season. Subsequent work has mitigated this oversensitivity to annual cycles of rainy and dry seasons, with the result that our models may now be under-sensitive to variability around the mean. Hence the Goldilocks metaphor: We have moved our models from an over-sensitive (too hot) position to an under-sensitive (too cold) state, while we desire understanding and an ability to simulate both annual cycles and anomalous conditions (just right).

In this research we demonstrate our ability to combine in-situ and spectral datasets with models to converge on a description of biophysical processes that combines robustness to mean annual state with a realistic sensitivity to anomalous drought. We use climatology of annual mean precipitation and dry season character to obtain a Drought Resistance Index (DRI) that, when combined with soil depth data yields an initial estimate of forest drought resilience. Solar-Induced Fluorescence (SIF) observations provide higher-resolution spatiotemporal monitoring of canopy response to anomalous events (such as 2010 drought) that can we use to refine our understanding of ecophysiological stress across temperature and precipitation gradients in tropical South America. We demonstrate that we can maintain fidelity to seasonality of surface flux as observed by eddy covariance flux towers while improving model response to drought events.