Introducing 3-PG2Py, an open-source forest growth model in Python. (April 2022) - Song, Xiaodong; Song, Yu
Abstract: Process-based forest growth models have increased applications in forest ecology, carbon sequestration, and climate change. The forest growth model Physiological Principles for Predicting Growth (3-PG) has been widely used in these fields due to its moderate complexity and explicit physiologic background. As a further step in this continued effort, we developed 3-PG2Py, a Python version of 3-PG2 (a modified version of 3-PG with respect to water balance prediction) to facilitate its extension and application to broader communities. In this study, the basic structure of 3-PG2Py is explained, and its simulation against observations is demonstrated. To facilitate sensitivity and uncertainty analyses, two global sensitivity analysis algorithms and an ensemble Kalman filter algorithm are integrated into 3-PG2Py and examples are presented. Additionally, an interface for spatial simulation is implemented. 3-PG2Py is compatible with Python 2.7+ and open-source. With 3-PG2Py, the users can adapt the model easily to more diversified applications, especially the computationally intensive ones. Highlights: A Python version of 3-PG2 model is presented. 3-PG2Py is flexible and could support cross-platform and computationally intensive applications. Global sensitivity analysis and state-parameter estimation algorithms, as well as spatial simulation interface, are tested.