Sensitivity of phyto-geocentric site productivity models to spatial extent and climate data aggregation
Dissertation
Datum der mündl. Prüfung:2023-09-04
Erschienen:2024-02-20
Betreuer:Prof. Dr. Jürgen Nagel
Gutachter:Prof. Dr. Jürgen Nagel
Gutachter:Prof. Dr. Christian Ammer
Dateien
Name:240115_Dissertation_LBurggraef_Revised_Digital.pdf
Size:9.79Mb
Format:PDF
Zusammenfassung
Englisch
The impact of climate warming on forestry in Germany and Europe has become more and more visible in recent years and decades. This has lead to an increased demand for predictions on the development of forest stands with regard to timber production, CO2 sequestration and conservation related properties, such as biodiversity. Phyto-geocentric climate sensitive site productivity models are helpful tools for estimating trends in the development of timber and CO2 stocks. Traditional site productivity models are phytocentric, relying solely on the properties of stands or trees. Adding climate sensitivity requires building geocentric or phyto-geocentric models, which are based on or include environmental covariates. Ideally, these models would be based on real time series. However, due to the long time periods of forest growth processes, these rarely cover climate gradients required for model development. Hence, they are often replaced by false time series. This approach is also known as Space-for-Time substitution. Phyto-geocentric climate sensitive site productivity models following this approach are based on two data components: forest inventory data, which gives information on site productivity, and climate data from which the necessary independent covariates are selected. The thesis presented here consists of two studies, each dealing with one component affecting Space-for-Time based site productivity models: (1) the spatial extent of the underlying forest inventory data and (2) the aggregation period used for the climate data. In the first study, height-diameter models for common and sessile oak (_Quercus robur/petraea_), European beech (_Fagus sylvatica_), Norway spruce (_Picea abies_) and Scots pine (_Pinus sylvestris_) were fitted based on a pan-European forest inventory dataset, supplemented with climate and soil data. In the second study, height-age models for European beech and Scots pine were developed based on three different aggregation scenarios for climate data. Soil data was included in both studies to analyze the potential of soil covariates for increasing the geocentric component of site productivity models. Both studies were compared with regard to (1) the potential of continental scale forest inventories as a basis for climate sensitive site productivity models compared to national scale inventories, (2) the differences in covariate effects and predictions resulting from different aggregation periods of the underlying climate data and (3) differences in the selection of soil covariates. Results showed that forest inventories on a continental scale may be better suited for site productivity model fitting. Compared to the German National Forest Inventory used in the second study, the identification of plausible effects proved easier, especially at the extreme edges of the data. However, similar studies show that supplementing national scale inventories with fine scale regional inventory data may yield equally plausible effects. With regard to the analyzed aggregation periods, dynamic aggregation of climate data over the tree or stand age is clearly recommended. Static aggregation periods might lead to over- or underestimation or even contrasting directions of productivity changes when projecting into the future. The identification of plausible soil covariate effects proved difficult in both studies, with most parameters returning implausible or insignificant effects. However, reasonable effects were identified for C:P ratio and pH value.
Keywords: climate change; site productivity; soil; forest growth modeling; space for time; generalized additive model