Improving the predictive capacity of the windthrow risk model ForestGALES with long-term monitoring data – A statistical calibration approach

Abstract

Winter storms cause severe damage in German forests. Different modelling approaches have already been used to try and map endangered areas to minimize the risk of wind damage by stand adaption. Prevalent models for Germany include empirical-statistical and hybrid-mechanistic models, such as ForestGALES (FG). As of yet, FG is not extensively used in Germany as its parametrization requires extensive experimental efforts to derive regionally sensitive species-specific parameters. Here, we implement a statistical calibration approach for German forest conditions with observed damage from single tree data, soil types, topography (topex) and gust speed data. We use simulated annealing to generate new species-specific values for the tree species, Norway spruce, European beech, and Douglas fir from within the range of all coniferous (deciduous) species for Norway spruce and Douglas fir (European beech) and an additional 10 % buffer around the default species-specific values for each species. We compare two optimization approaches: First, we aim to maximize the Matthew’s correlation coefficient (MCC), which is calculated from the confusion matrix, applying a fixed classification threshold of 0.5. In comparison to the optimization at a fixed threshold, we optimized the species-specific parameters by maximizing the area-under-curve (AUC) value directly generated from the receiver-operator characteristic (ROC) analysis. We compare our statistical parametrizations for the considered species to those currently implemented in FG and validate the resulting damage probabilities based on confusion matrices and related performance measures. We created separate parametrizations for a single-tree and stand-wide analysis of storm damage risk, which we validated with gust speed data for Germany. Our results show, that for the single-tree method, MCC improved for all species: By 0.26 (0.22) for the calibration (validation) subset for Douglas fir, by 0.22 (0.18) for Norway spruce and by 0.08 (0.05) for European beech. The optimization for the stand-method shows an increase in MCC as well, with results not being considered due to low numbers of observation data. We show that for German forests, FG’s predictive capability can be improved by statistical optimization when no tree-pulling data is available, which could be valuable for creating further regionalizations of FG.