Modelling the effects of forest regeneration on streamflow using forest growth models

Forest regeneration is a dynamic process that affects forest hydrology through changes in structure and density of natural forests. In Victoria and Tasmania, forest hydrology models that manage the potential impacts of land cover disturbance on the water resource are not data-driven with information on vegetation dynamics that affect forest water use. Current models underutilise forest inventory databases for managing the forested water resources, even though available evidence suggests an inverse relationship between forest growth rates and long-term changes in streamflow. This dissertation is the first published study that uses forest inventory data to produce spatiotemporal forest growth models to explain vegetation-induced streamflow trends. The study was undertaken in nine small catchments (7.4 to 52.8 ha) located in Melbourne's Maroondah water catchment. The hydrology model runs on an annual time step and partitions streamflow data into climate-induced noise using a climate filter, and vegetation-induced trend using an ellipse and gamma (Kuczera curve‚ÄövÑvp) function. A simulation exercise demonstrates how well the model structure isolates the vegetation-induced trend from climatic variability in streamflow using a range of synthesised scenario cases. The model framework allows for comparison of streamflow trends against a detailed forest growth model by using the same gamma function to quantify forest growth and vegetation-induced streamflow trends. To spatially extrapolate forest growth, field measured stand characteristics were empirically analysed against LiDAR indices. The indices were produced with mixture models, which used 11 distribution functions to summarise complex canopy attributes with bimodal distributions. The LiDAR indices were used to predict overstorey stand volumes and basal area, and understorey basal area of 18-, 37-, and 70-year old Mountain Ash forest with variable density classes and treatment effects. Observed versus predicted values of eucalyptus basal area and stand volume were highly correlated, with bootstrap r2 ranging from 0.61 to 0.89 and 0.67 to 0.88 respectively. Non-eucalyptus basal area r2 ranged from 0.5 to 0.91. To temporally extrapolate stand volumes and basal area, LiDAR indices and permanent plot data were used in mixed effects models to capture the spatial heterogeneity in, and temporally polymorphic nature of forest growth. The spatiotemporal models of forest growth were then lumped to the catchment-scale to represent changes in growth rates over the stream gauging period. The relationship between catchment-scale gamma parameters of forest growth and forest water use were explored, and results demonstrate that forest growth provides useful information for explaining streamflow trends published in the literature and quantified in this study.