A dynamic environment-sensitive site index model for the prediction of site productivity potential under climate change

- The DESIM was developed in the framework of state space models based on longitudinal data structure.
- The approach allows to model response of site index to spatial gradients and environmental change dynamics.
- Environmentally driven changes in site index are modelled as a function of initial site index.
- Limitations of the space-for-time substitution approach are discussed.

Accurate and reliable predictions of the future development of forest site productivity are crucial for the effective management of forest stands. Static models which simply extrapolate productivity into the future are inappropriate under conditions of environmental change since they lack a close link between fundamental environmental drivers and forest growth processes. Here we present a dynamic environment-sensitive site index model formulated in the framework of a nonlinear state space approach based on longitudinal data from long-term experimental plots. Estimation of the model parameters was carried out using the prediction error minimization method. Our aim was to identify dynamic relationships between site index and environmental variables and to make conditional predictions of the future development of site index under climate change scenarios. Nonlinear, interactive, as well as accumulative effects of environmental factors (climate/weather and nitrogen influx) on the growth response were considered in the model. In the study, we estimated the dynamic environment-sensitive site index model using data from 604 Norway spruce (Picea abies [L.] Karst.) long-term experimental plots in southwest Germany with measurement data covering a period of more than 100 years from the end of the 19th century until today. We used the calibrated model to project future site index changes under increasing growing season temperature scenarios. Conventional climate change impact studies usually utilize a gradient approach and apply space-for-time substitution for the parameterization of models that are calibrated using spatial variability in the data. In contrast, the approach presented here utilizes the longitudinal data structure of multiple real growth time series to simultaneously exploit spatial and temporal variation in the data to provide more reliable and robust projections. Limitations of the space-for-time substitution approach in forest growth modelling are discussed.