Tree-ring based forest model calibrations with a deep learning algorithm

Abstract

Process-based forest growth models are important tools for forest management and studies for forest growth, carbon sequestration, and forest dynamics. However, their reliable simulations rely not only on the quality of model construction but also on accurate parameters to appropriately depict various physiological and biophysical processes in the models. While explicit physiological measurements are excellent sources for model parameterization, they are not always readily available. It would be ideal to use easy-to-measure tree-ring data as a benchmark for parametrization, but such applications have been rare. Here we present a new approach to reasonably parameterize a stand growth model based on tree-ring data using deep learning algorithms. We integrated a stable carbon isotope (δ13C) version of a simple process-based stand growth model, 3-PG, into a recurrent neural network (RNN). The new 3PG-RNN model trained the RNN network and calibrated 3-PG parameters to minimize the mean squared error between the 3-PG outputs and targeted values. We then tested 3PG-RNN based on two Abies grandis stands located in Northern Idaho, USA. The results showed that the 3PG-RNN model was efficient in calibrating key parameters related to gas-exchange processes, including specifically quantum yield, maximum canopy conductance, and the slope function for stomatal responses to vapor pressure deficit. These calibrated parameters provided reasonable simulations for gas-exchanges similar to those constrained by explicit physiological observations. This was particularly true when both long-term diameter growth (estimated from tree-ring width) and tree-ring δ13C were used for model training. The RNN tool made it possible to use tree ring data as the key benchmark to calibrate the forest model and provide unbiased simulations for gas exchanges and forest growth with the RNN approach, which would greatly facilitate forest studies and management.