Predictive modelling and uncertainty quantification of UK forest growth
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Date
26/11/2015Author
Lonsdale, Jack Henry
Metadata
Abstract
Forestry in the UK is dominated by coniferous plantations. Sitka spruce (Picea
sitchensis) and Scots pine (Pinus sylvestris) are the most prevalent species and
are mostly grown in single age mono-culture stands. Forest strategy for Scotland,
England, and Wales all include efforts to achieve further afforestation. The
aim of this afforestation is to provide a multi-functional forest with a broad
range of benefits. Due to the time scale involved in forestry, accurate forecasts
of stand productivity (along with clearly defined uncertainties) are essential to
forest managers. These can be provided by a range of approaches to modelling
forest growth. In this project model comparison, Bayesian calibration, and
data assimilation methods were all used to attempt to improve forecasts and
understanding of uncertainty therein of the two most important conifers in UK
forestry.
Three different forest growth models were compared in simulating growth of Scots
pine. A yield table approach, the process-based 3PGN model, and a Stand
Level Dynamic Growth (SLeDG) model were used. Predictions were compared
graphically over the typical productivity range for Scots pine in the UK. Strengths
and weaknesses of each model were considered. All three produced similar growth
trajectories. The greatest difference between models was in volume and biomass in
unthinned stands where the yield table predicted a much larger range compared
to the other two models. Future advances in data availability and computing
power should allow for greater use of process-based models, but in the interim
more flexible dynamic growth models may be more useful than static yield tables
for providing predictions which extend to non-standard management prescriptions
and estimates of early growth and yield.
A Bayesian calibration of the SLeDG model was carried out for both Sitka spruce
and Scots pine in the UK for the first time. Bayesian calibrations allow both
model structure and parameters to be assessed simultaneously in a probabilistic
framework, providing a model with which forecasts and their uncertainty can be
better understood and quantified using posterior probability distributions. Two
different structures for including local productivity in the model were compared
with a Bayesian model comparison. A complete calibration of the more probable
model structure was then completed. Example forecasts from the calibration
were compatible with existing yield tables for both species. This method could
be applied to other species or other model structures in the future.
Finally, data assimilation was investigated as a way of reducing forecast uncertainty.
Data assimilation assumes that neither observations nor models provide
a perfect description of a system, but combining them may provide the best estimate.
SLeDG model predictions and LiDAR measurements for sub-compartments
within Queen Elizabeth Forest Park were combined with an Ensemble Kalman
Filter. Uncertainty was reduced following the second data assimilation in all of
the state variables. However, errors in stand delineation and estimated stand
yield class may have caused observational uncertainty to be greater thus reducing
the efficacy of the method for reducing overall uncertainty.